From 610a7b8ee8872cd8140c6bd5ba0fecb21c94ef4f Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 12:41:52 +0200 Subject: [PATCH 01/62] Add .gitignore --- .gitignore | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e2615b4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Uncompressed data +data/ + +# Jupyter Notebook +.ipynb_checkpoints From 4d3bbc62c3cac522082c37680dd4ae77d0801b84 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 12:54:25 +0200 Subject: [PATCH 02/62] Remove beta from sweep_opt arguments --- lib/optimal_cut.py | 153 ++++++++++++++++++++++----------------------- 1 file changed, 75 insertions(+), 78 deletions(-) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 1b0c852..2a09d37 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -19,18 +19,17 @@ def sqrtm(mat): * matrix square root """ eigvals, eigvecs = eigh(mat) - + eigvecs = eigvecs[:, eigvals > 0] eigvals = eigvals[eigvals > 0] - + return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T)) -def sweep_opt(x, beta, F, G, k, ind): +def sweep_opt(x, F, G, k, ind): """ Sweep algorithm for sparse wavelets. Input: * x: continuous indicator vector - * beta: beta parameter for regularization * F: graphsignal * G: graph * k: max number of edges to be cut @@ -47,11 +46,11 @@ def sweep_opt(x, beta, F, G, k, ind): size_one = 0 sum_one = 0 sum_two = 0 - + for v in G.nodes(): sum_two = sum_two + F[ind[v]] - - edges_cut = 0 + + edges_cut = 0 nodes_one = {} total_size = networkx.number_of_nodes(G) @@ -59,39 +58,39 @@ def sweep_opt(x, beta, F, G, k, ind): size_one = size_one + 1 sum_one = sum_one + F[ind[G.nodes()[sorted_x[i]]]] sum_two = sum_two - F[ind[G.nodes()[sorted_x[i]]]] - + nodes_one[G.nodes()[sorted_x[i]]] = True - + for v in G.neighbors(G.nodes()[sorted_x[i]]): if v not in nodes_one: edges_cut = edges_cut + 1 else: edges_cut = edges_cut - 1 - + den = size_one * (total_size-size_one) * total_size if den > 0: val = math.pow(sum_one*(total_size-size_one) - sum_two*size_one, 2) / den else: val = 0 - + if val >= best_val and edges_cut <= k: best_cand = i best_val = val best_edges_cut = edges_cut - + if total_size * size_one * (total_size-size_one) > 0: energy = math.pow(sum_one*(total_size-size_one) - sum_two*size_one, 2) / (total_size * size_one * (total_size-size_one)) else: energy = 0 vec = numpy.zeros(total_size) - + for i in range(x.shape[0]): if i <= best_cand: vec[sorted_x[i]] = -1. else: vec[sorted_x[i]] = 1. - + return vec, best_val, best_edges_cut, energy def laplacian_complete(n): @@ -106,7 +105,7 @@ def laplacian_complete(n): C = -1 * C D = numpy.diag(numpy.ones(n)) C = (n)*D + C - + return C def weighted_adjacency_complete(G, F, ind): @@ -124,7 +123,7 @@ def weighted_adjacency_complete(G, F, ind): A.append([]) for u in G.nodes(): A[-1].append(pow(F[ind[v]]-F[ind[u]],2)) - + return numpy.array(A) def fast_cac(G, F, ind): @@ -134,7 +133,7 @@ def fast_cac(G, F, ind): * G: graph * F: graph signal * ind: vertex index v: unique integer - Output: + Output: * CAC: matrix product """ CAC = [] @@ -188,18 +187,18 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): """ isqrtCL = sqrtmi( C + beta * L) M = numpy.dot(numpy.dot(isqrtCL, CAC), isqrtCL) - + (eigvals, eigvecs) = scipy.linalg.eigh(M,eigvals=(0,0)) x = numpy.asarray(numpy.dot(eigvecs[:,0], isqrtCL))[0,:] - (x, score, size, energy) = sweep_opt(x, beta, F, G, k, ind) - + (x, score, size, energy) = sweep_opt(x, F, G, k, ind) + res = {} res["x"] = numpy.array(x) res["size"] = size res["score"] = score res["energy"] = energy - + return res def eig_vis_opt(G, F, beta): @@ -211,23 +210,23 @@ def eig_vis_opt(G, F, beta): * beta: regularization parameter Output: * v1: first eigenvector - * v2: second eigenvector + * v2: second eigenvector """ ind = {} i = 0 - + for v in G.nodes(): ind[v] = i i = i + 1 - + C = laplacian_complete(networkx.number_of_nodes(G)) A = weighted_adjacency_complete(G, F, ind) CAC = numpy.dot(numpy.dot(C,A), C) L = networkx.laplacian_matrix(G).todense() - + isqrtCL = sqrtmi( C + beta * L) M = numpy.dot(numpy.dot(isqrtCL, CAC), isqrtCL) - + (eigvals, eigvecs) = scipy.linalg.eigh(M,eigvals=(0,1)) x1 = numpy.asarray(numpy.dot(eigvecs[:,0], isqrtCL))[0,:] x2 = numpy.asarray(numpy.dot(eigvecs[:,1], isqrtCL))[0,:] @@ -244,8 +243,8 @@ def trans(L, min_v, max_v): Output: * translation """ - return (float(2.) / (max_v-min_v)) * L, -(float(max_v+min_v) / (max_v-min_v)) - + return (float(2.) / (max_v-min_v)) * L, -(float(max_v+min_v) / (max_v-min_v)) + def fun(k, n, beta, min_v, max_v, x): """ Function to be integrated in Chebyshev polynomial computation. @@ -258,9 +257,9 @@ def fun(k, n, beta, min_v, max_v, x): * function value """ y = 0.5 * math.cos(x) * float(max_v - min_v) + (0.5 * (max_v + min_v)) - + return math.cos(k*x)*(float(1.) / math.sqrt(beta*y)) - + def coef(k, n, beta, min_v, max_v): """ Chebyshev polynomial coefficients. @@ -273,7 +272,7 @@ def coef(k, n, beta, min_v, max_v): * coefficient """ return float(2. * scipy.integrate.quad(lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi - + def chebyshev_approx_2d(n, beta, X, L): """ Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using Chebyshev polynomials (twice) @@ -287,28 +286,28 @@ def chebyshev_approx_2d(n, beta, X, L): """ max_v = beta * L.shape[0] min_v = 1 - + ts1, ts2 = trans(L, min_v, max_v) P1 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * X - tkm2 = X + tkm2 = X tkm1 = scipy.sparse.csr_matrix.dot(ts1, X) + ts2 * X P1 = P1 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - - for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 + + for i in range(2, n): + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 P1 = P1 + coef(i, L.shape[0], beta, min_v, max_v) * Tk tkm2 = tkm1 tkm1 = Tk - - P1 = P1.transpose() + + P1 = P1.transpose() P2 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * P1 - tkm2 = P1 + tkm2 = P1 tkm1 = scipy.sparse.csr_matrix.dot(ts1, P1) + ts2 * P1 P2 = P2 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - - for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 + + for i in range(2, n): + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 P2 = P2 + coef(i, L.shape[0], beta, min_v, max_v) * Tk tkm2 = tkm1 tkm1 = Tk @@ -323,25 +322,25 @@ def chebyshev_approx_1d(n, beta, x, L): * beta: regularization parameter * x: vector * L: graph Laplacian - Output: + Output: * P: approximation - + """ max_v = beta * L.shape[0] min_v = 1 - + ts1, ts2 = trans(L, min_v, max_v) P = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * x tkm2 = x tkm1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x P = P + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - - for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 + + for i in range(2, n): + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 P = P + coef(i, L.shape[0], beta, min_v, max_v) * Tk tkm2 = tkm1 tkm1 = Tk - + return P def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): @@ -366,18 +365,18 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): """ L = networkx.laplacian_matrix(G) M = chebyshev_approx_2d(n, beta, CAC, L) - + eigvec = power_method(-M, start, 10) x = chebyshev_approx_1d(n, beta, eigvec, L) - - (x, score, size, energy) = sweep_opt(x, beta, F, G, k, ind) - + + (x, score, size, energy) = sweep_opt(x, F, G, k, ind) + res = {} res["x"] = numpy.array(x) res["size"] = size res["score"] = score res["energy"] = energy - + return res def fast_search(G, F, k, n, ind): @@ -424,20 +423,20 @@ def one_d_search(G, F, k, ind): b = 1000. c=b-gr*(b-a) d=a+gr*(b-a) - + #Tolerance tol = 1. resab = {} resab["size"] = k + 1 - + #golden search - while abs(c-d)>tol or resab["size"] > k: + while abs(c-d)>tol or resab["size"] > k: resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind) resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) - - if resc["size"] <= k: - if resc["score"] > resd["score"]: + + if resc["size"] <= k: + if resc["score"] > resd["score"]: start = numpy.array(resc["x"]) b = d d = c @@ -445,16 +444,16 @@ def one_d_search(G, F, k, ind): else: start = numpy.array(resd["x"]) a=c - c=d + c=d d=a+gr*(b-a) else: start = numpy.array(resc["x"]) a=c - c=d + c=d d=a+gr*(b-a) - + resab = spectral_cut(CAC, L, C, A, start, F, G, (b+a) / 2, k, ind) - + return resab def get_subgraphs(G, cut): @@ -481,7 +480,7 @@ def get_subgraphs(G, cut): G1 = G.subgraph(P1) G2 = G.subgraph(P2) - + return G1, G2 def optimal_wavelet_basis(G, F, k, npol): @@ -497,7 +496,7 @@ def optimal_wavelet_basis(G, F, k, npol): * ind: vertex index vertex: unique integer * size: number of edges cut """ - + #Creating index ind = {} i = 0 @@ -517,14 +516,14 @@ def optimal_wavelet_basis(G, F, k, npol): c["parent"] = root c["graph"] = G - + cand_cuts.append(c) #Recursively compute new cuts while size <= k and len(cand_cuts) > 0: best_cut = None b = 0 - + for i in range(0, len(cand_cuts)): if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["score"] > 0: if best_cut is None or cand_cuts[i]["score"] > best_cut["score"]: @@ -543,24 +542,24 @@ def optimal_wavelet_basis(G, F, k, npol): best_cut["parent"].add_child(n) elif networkx.number_of_nodes(G1) > 0: n = Node(None) - + if npol == 0: c = one_d_search(G1, F, k, ind) else: c = fast_search(G1, F, k, npol, ind) - + c["parent"] = n c["graph"] = G1 cand_cuts.append(c) best_cut["parent"].add_child(n) - + if networkx.number_of_nodes(G2) == 1: n = Node(ind[G2.nodes()[0]]) best_cut["parent"].add_child(n) elif networkx.number_of_nodes(G2) > 0: n = Node(None) - + if npol == 0: c = one_d_search(G2, F, k, ind) else: @@ -569,17 +568,15 @@ def optimal_wavelet_basis(G, F, k, npol): c["parent"] = n c["graph"] = G2 cand_cuts.append(c) - + best_cut["parent"].add_child(n) - + del cand_cuts[b] - + #Compute remaining cuts using ratio cuts once budget is over (not optimal) for i in range(0, len(cand_cuts)): rc_recursive(cand_cuts[i]["parent"], cand_cuts[i]["graph"], ind) set_counts(root) - - return root, ind, size - + return root, ind, size From 12919f2e8e30cce4d89672ff7e3bd3348d006e7b Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 12:59:44 +0200 Subject: [PATCH 03/62] Move sqrtm to graph_signal_proc.py --- lib/graph_signal_proc.py | 96 +++++++++++++++++++++++----------------- lib/optimal_cut.py | 13 ------ 2 files changed, 55 insertions(+), 54 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 4b39851..1563caa 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -22,7 +22,7 @@ def compute_eigenvectors_and_eigenvalues(L): """ Computes eigenvectors and eigenvalues of the matrix L - Input: + Input: * L: matrix Output: * U: eigenvector matrix, one vector/column, sorted by corresponsing eigenvalue @@ -30,7 +30,7 @@ def compute_eigenvectors_and_eigenvalues(L): """ lamb, U = linalg.eig(L) - idx = lamb.argsort() + idx = lamb.argsort() lamb = lamb[idx] U = U[:,idx] @@ -128,7 +128,7 @@ def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): s.append([]) for n in range(0, len(N)): - for m in range(0, len(U)): + for m in range(0, len(U)): s_n_m = 0 for x in range(0, len(U)): @@ -159,7 +159,7 @@ def graph_wavelets(lamb, U, N, T): for t in range(0, len(T)): for n in range(0, len(N)): - for m in range(0, len(U)): + for m in range(0, len(U)): w_t_n_m = 0 for x in range(0, len(U)): @@ -181,7 +181,7 @@ def graph_fourier(F, U): lambdas = [] for i in range(0, len(U)): - lambdas.append(numpy.dot(F, U[:,i])) + lambdas.append(numpy.dot(F, U[:,i])) lambdas = numpy.array(lambdas) @@ -312,7 +312,7 @@ def set_counts(tree): Sets counts for intermediate nodes in the tree. Input: * tree: tree node - Output: + Output: * count: count for the tree node """ if tree.data is not None: @@ -350,7 +350,7 @@ def partitions_level_rec(tree, level, G, l, partitions): partitions_level_rec(c, level, G, l+1, partitions) else: partitions.append([tree.data]) - + def partitions_level(tree, level, G): """ Recovers partitions at a certain level of the three. @@ -363,7 +363,7 @@ def partitions_level(tree, level, G): """ partitions = [] partitions_level_rec(tree, level, G, 0, partitions) - + return partitions def build_matrix(G, ind): @@ -393,7 +393,7 @@ def select_centroids(M, radius): * M * radius Output: - * centroids + * centroids """ nodes = list(range(M.shape[0])) random.shuffle(nodes) @@ -417,7 +417,7 @@ def coarse_matrix(M, H, cents, nodes): Makes matrix coarser based on centroids. Input: * M: distance matrix - * H: + * H: * cents: centroids * nodes: list of nodes Output: @@ -464,7 +464,7 @@ def coarse_matrix(M, H, cents, nodes): def get_partitions(x, node_list): """ - Gets partitions given indicator vector. + Gets partitions given indicator vector. if x < 0: partition 1 if x <= 0: partition 2 Input: @@ -501,7 +501,7 @@ def get_new_laplacians(L, P1, P2, ind): data = [] row = [] col = [] - + for i in range(len(P1)): d = 0 for j in range(len(P1)): @@ -510,17 +510,17 @@ def get_new_laplacians(L, P1, P2, ind): col.append(j) data.append(float(L[ind[P1[i]],ind[P1[j]]])) d = d - L[ind[P1[i]],ind[P1[j]]] - + row.append(i) col.append(i) data.append(float(d)) - + L1 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P1), len(P1))) - + data = [] row = [] col = [] - + for i in range(len(P2)): d = 0 for j in range(len(P2)): @@ -529,11 +529,11 @@ def get_new_laplacians(L, P1, P2, ind): col.append(j) data.append(float(L[ind[P2[i]],ind[P2[j]]])) d = d - L[ind[P2[i]],ind[P2[j]]] - + row.append(i) col.append(i) data.append(float(d)) - + L2 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P2), len(P2))) return L1, L2 @@ -550,9 +550,24 @@ def laplacian_complete(n): C = -1 * C D = numpy.diag(numpy.ones(n)) C = (n)*D + C - + return C +def sqrtm(mat): + """ + Matrix square root. + Input: + * mat: matrix + Output: + * matrix square root + """ + eigvals, eigvecs = eigh(mat) + + eigvecs = eigvecs[:, eigvals > 0] + eigvals = eigvals[eigvals > 0] + + return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T)) + def sqrtmi(mat): """ Computes the square-root inverse of a matrix. @@ -576,7 +591,7 @@ def create_linked_list(L): * linked_list: linked list """ linked_list = {} - + for i in L.nonzero()[0]: linked_list[i] = [] for j in range(L.shape[1]): @@ -589,7 +604,7 @@ def sweep(x, G): Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian) based on vector x. Input: * x: vector - * G: graph + * G: graph Output: * vec: indicator vector """ @@ -601,22 +616,22 @@ def sweep(x, G): for i in range(x.shape[0]): size_one = size_one + 1 - + nodes_one[G.nodes()[sorted_x[i]]] = True - + for v in G.neighbors(G.nodes()[sorted_x[i]]): if v not in nodes_one: edges_cut = edges_cut + 1 else: edges_cut = edges_cut - 1 - + den = size_one * (networkx.number_of_nodes(G)-size_one) if den > 0: val = float(edges_cut) / den else: val = networkx.number_of_nodes(G) - + if val <= best_val: best_cand = i best_val = val @@ -630,7 +645,7 @@ def sweep(x, G): vec[sorted_x[i]] = -1. else: vec[sorted_x[i]] = 1. - + return vec def separate_lcc(G, G0): @@ -649,7 +664,7 @@ def separate_lcc(G, G0): x.append(-1) else: x.append(1.) - + return numpy.array(x) def ratio_cut(G): @@ -660,7 +675,7 @@ def ratio_cut(G): Output: * x: Indicator vector """ - + Gcc=sorted(networkx.connected_component_subgraphs(G), key = len, reverse=True) G0=Gcc[0] @@ -689,7 +704,7 @@ def eig_vis_rc(G): x1 = numpy.asarray(eigvecs[:,0]) x2 = numpy.asarray(eigvecs[:,1]) - + return x1, x2 def get_subgraphs(G, cut): @@ -704,7 +719,7 @@ def get_subgraphs(G, cut): """ G1 = networkx.Graph() G2 = networkx.Graph() - + i = 0 P1 = [] P2 = [] @@ -714,7 +729,7 @@ def get_subgraphs(G, cut): else: P2.append(v) i = i + 1 - + G1 = G.subgraph(P1) G2 = G.subgraph(P2) @@ -737,7 +752,7 @@ def rc_recursive(node, G, ind): node.add_child(n) else: C = ratio_cut(G) - + (G1, G2) = get_subgraphs(G, C) if networkx.number_of_nodes(G1) > 1: @@ -747,7 +762,7 @@ def rc_recursive(node, G, ind): else: l = Node(ind[G1.nodes()[0]]) node.add_child(l) - + if networkx.number_of_nodes(G2) > 1: r = Node(None) rc_recursive(r, G2, ind) @@ -759,7 +774,7 @@ def rc_recursive(node, G, ind): def ratio_cut_hierarchy(G): """ Computes ratio-cut hierarchy for a graph. - Input: + Input: * G: graph Output: * root: tree root @@ -822,7 +837,7 @@ def compute_coefficients(tree, F): count = 0 for i in range(len(tree.children)): compute_coefficients(tree.children[i], F) - avg = avg + tree.children[i].avg * tree.children[i].count + avg = avg + tree.children[i].avg * tree.children[i].count count = count + tree.children[i].count if i > 0: @@ -844,7 +859,7 @@ def reconstruct_values(tree, F): * None """ if tree.data is None: - avg = tree.avg * tree.count + avg = tree.avg * tree.count count = tree.count for i in reversed(range(len(tree.children))): if i == 0: @@ -854,7 +869,7 @@ def reconstruct_values(tree, F): tree.children[i].avg = float(avg)/count + 0.5*float(tree.diffs[i-1]) / tree.children[i].count reconstruct_values(tree.children[i], F) count = count - tree.children[i].count - avg = avg - tree.children[i].avg * tree.children[i].count + avg = avg - tree.children[i].avg * tree.children[i].count tree.avgs.append(float(avg)/count) tree.avgs = list(reversed(tree.avgs)) @@ -864,12 +879,12 @@ def reconstruct_values(tree, F): def clear_tree(tree): """ Clears tree info. - Input: + Input: * tree - Output: + Output: * None """ - tree.avg = 0 + tree.avg = 0 tree.diffs = [] tree.avgs = [] @@ -985,4 +1000,3 @@ def gavish_wavelet_inverse(tree, ind, G, wtr): reconstruct_values(tree, F) return numpy.array(F) - diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 2a09d37..652fffe 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -10,20 +10,7 @@ from lib.graph_signal_proc import * from scipy import sparse -def sqrtm(mat): - """ - Matrix square root. - Input: - * mat: matrix - Output: - * matrix square root - """ - eigvals, eigvecs = eigh(mat) - - eigvecs = eigvecs[:, eigvals > 0] - eigvals = eigvals[eigvals > 0] - return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T)) def sweep_opt(x, F, G, k, ind): """ From f67c75c6a522159617517b5bfc1ea465b45caf36 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 15:28:40 +0200 Subject: [PATCH 04/62] Add info to dataset.py --- lib/datasets.py | 47 ++++++++++++++++++++++++++++++++--------------- 1 file changed, 32 insertions(+), 15 deletions(-) diff --git a/lib/datasets.py b/lib/datasets.py index ceb7df3..6363389 100644 --- a/lib/datasets.py +++ b/lib/datasets.py @@ -1,35 +1,52 @@ +r""" -#small traffic -#Speeds from traffic sensors -#vertices: 100 +This module provides path to the dataset in data. +Each one is in a directory with the following structure: + + datasetname + |- datasetname.data + |- datasetname.graph + +datasetname.data has info about the graph signal. +Row format : "vertex_id, vertex_value" + +datasetname.graph has info about edges. +Row format: "vertex_A, vertex_B[, edge_weight]" + +""" + + +# Small traffic (weighted) +# Speeds from traffic sensors +# Vertices: 100 small_traffic = {} small_traffic["path"] = "data/small_traffic/" -#Large traffic -#Speeds from traffic sensors -#vertices: 1923 +# Large traffic (weigthed) +# Speeds from traffic sensors +# Vertices: 1923 traffic = {} traffic["path"] = "data/traffic/" -#Human -#Gene expression data -#vertices: 3628 +# Human (unweighted) +# Gene expression data +# Vertices: 3628 human = {} human["path"] = "data/human/" -#Wikipedia data -#Number of views of wikipedia pages -#Vertices: 4871 +# Wikipedia data (unweighted) +# Number of views of wikipedia pages +# Vertices: 4871 wiki = {} wiki["path"] = "data/wiki/" -#Political blogs -#Link network of congressman's blogs with democrat/republican (0/1) as signal -#vertices: 1490 +# Political blogs (unweighted) +# Link network of congressman's blogs with democrat/republican (0/1) as signal +# Vertices: 1490 polblogs = {} polblogs["path"] = "data/polblogs/" From dce19115277b16f9c349a8c1f652c7f6fce4c5bc Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 16:03:25 +0200 Subject: [PATCH 05/62] Clean optimal_cut imports --- lib/optimal_cut.py | 1080 ++++++++++++++++++++++---------------------- 1 file changed, 552 insertions(+), 528 deletions(-) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 652fffe..2312aa9 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -1,569 +1,593 @@ -import networkx import math -import numpy + +import numpy as np +import networkx as nx +import scipy + +from lib.graph_signal_proc import Node +import lib.graph_signal_proc as gsp + + +""" +import math + +import numpy as np import scipy -import sys -from scipy import linalg -import time -from numpy import log, mean, dot, diag, sqrt -from numpy.linalg import eigh -from lib.graph_signal_proc import * -from scipy import sparse +import networkx as nx +""" + def sweep_opt(x, F, G, k, ind): - """ - Sweep algorithm for sparse wavelets. - Input: - * x: continuous indicator vector - * F: graphsignal - * G: graph - * k: max number of edges to be cut - * ind: vertex index v: unique integer - Output: - * vec: indicator vector - * best_val: score value - * best_edges_cut: number of edges cut - * energy: wavelet energy value - """ - best_val = 0. - best_edges_cut = 0 - sorted_x = numpy.argsort(x) - size_one = 0 - sum_one = 0 - sum_two = 0 - - for v in G.nodes(): - sum_two = sum_two + F[ind[v]] - - edges_cut = 0 - nodes_one = {} - total_size = networkx.number_of_nodes(G) - - for i in range(x.shape[0]): - size_one = size_one + 1 - sum_one = sum_one + F[ind[G.nodes()[sorted_x[i]]]] - sum_two = sum_two - F[ind[G.nodes()[sorted_x[i]]]] - - nodes_one[G.nodes()[sorted_x[i]]] = True - - for v in G.neighbors(G.nodes()[sorted_x[i]]): - if v not in nodes_one: - edges_cut = edges_cut + 1 - else: - edges_cut = edges_cut - 1 - - den = size_one * (total_size-size_one) * total_size - if den > 0: - val = math.pow(sum_one*(total_size-size_one) - sum_two*size_one, 2) / den - else: - val = 0 - - if val >= best_val and edges_cut <= k: - best_cand = i - best_val = val - best_edges_cut = edges_cut - - if total_size * size_one * (total_size-size_one) > 0: - energy = math.pow(sum_one*(total_size-size_one) - sum_two*size_one, 2) / (total_size * size_one * (total_size-size_one)) - else: - energy = 0 - - vec = numpy.zeros(total_size) - - for i in range(x.shape[0]): - if i <= best_cand: - vec[sorted_x[i]] = -1. - else: - vec[sorted_x[i]] = 1. - - return vec, best_val, best_edges_cut, energy + """ + Sweep algorithm for sparse wavelets. + Input: + * x: continuous indicator vector + * F: graph signal + * G: graph + * k: max number of edges to be cut + * ind: vertex index v: unique integer + Output: + * vec: indicator vector + * best_val: score value + * best_edges_cut: number of edges cut + * energy: wavelet energy value + """ + best_val = 0. + best_edges_cut = 0 + sorted_x = np.argsort(x) + size_one = 0 + sum_one = 0 + sum_two = 0 + + for v in G.nodes(): + sum_two = sum_two + F[ind[v]] + + edges_cut = 0 + nodes_one = {} + total_size = nx.number_of_nodes(G) + + for i in range(x.shape[0]): + size_one = size_one + 1 + sum_one = sum_one + F[ind[G.nodes()[sorted_x[i]]]] + sum_two = sum_two - F[ind[G.nodes()[sorted_x[i]]]] + + nodes_one[G.nodes()[sorted_x[i]]] = True + + for v in G.neighbors(G.nodes()[sorted_x[i]]): + if v not in nodes_one: + edges_cut = edges_cut + 1 + else: + edges_cut = edges_cut - 1 + + den = size_one * (total_size - size_one) * total_size + if den > 0: + val = math.pow(sum_one * (total_size - size_one) - sum_two * size_one, 2) / den + else: + val = 0 + + if val >= best_val and edges_cut <= k: + best_cand = i + best_val = val + best_edges_cut = edges_cut + + if total_size * size_one * (total_size - size_one) > 0: + energy = math.pow(sum_one * (total_size - size_one) - sum_two * + size_one, 2) / (total_size * size_one * (total_size - size_one)) + else: + energy = 0 + + vec = np.zeros(total_size) + + for i in range(x.shape[0]): + if i <= best_cand: + vec[sorted_x[i]] = -1. + else: + vec[sorted_x[i]] = 1. + + return vec, best_val, best_edges_cut, energy + def laplacian_complete(n): - """ - Laplacian of a complete graph with n vertices. - Input: - * n: size - Output: - * C: Laplacian - """ - C = numpy.ones((n, n)) - C = -1 * C - D = numpy.diag(numpy.ones(n)) - C = (n)*D + C - - return C + """ + Laplacian of a complete graph with n vertices. + Input: + * n: size + Output: + * C: Laplacian + """ + C = np.ones((n, n)) + C = -1 * C + D = np.diag(np.ones(n)) + C = (n) * D + C + + return C + def weighted_adjacency_complete(G, F, ind): - """ - Computes weighted adjacency complete matrix (w(v)-w(u))^2 - Input: - * G: graph - * F: graph signal - * ind: vertex index vertex: unique integer - Output: - * A: nxn matrix - """ - A = [] - for v in G.nodes(): - A.append([]) - for u in G.nodes(): - A[-1].append(pow(F[ind[v]]-F[ind[u]],2)) - - return numpy.array(A) + """ + Computes weighted adjacency complete matrix (w(v)-w(u))^2 + Input: + * G: graph + * F: graph signal + * ind: vertex index vertex: unique integer + Output: + * A: nxn matrix + """ + A = [] + for v in G.nodes(): + A.append([]) + for u in G.nodes(): + A[-1].append(pow(F[ind[v]] - F[ind[u]], 2)) + + return np.array(A) + def fast_cac(G, F, ind): - """ - Computes product C*A*C, where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix. - Input: - * G: graph - * F: graph signal - * ind: vertex index v: unique integer - Output: - * CAC: matrix product - """ - CAC = [] - for v in G.nodes(): - CAC.append([]) - for u in G.nodes(): - CAC[-1].append(F[ind[v]] * F[ind[u]]) - - CAC = numpy.array(CAC) - CAC = -2 * math.pow(networkx.number_of_nodes(G), 2) * CAC - - return CAC + """ + Computes product C*A*C, where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix. + Input: + * G: graph + * F: graph signal + * ind: vertex index v: unique integer + Output: + * CAC: matrix product + """ + CAC = [] + for v in G.nodes(): + CAC.append([]) + for u in G.nodes(): + CAC[-1].append(F[ind[v]] * F[ind[u]]) + + CAC = np.array(CAC) + CAC = -2 * math.pow(nx.number_of_nodes(G), 2) * CAC + + return CAC + def power_method(mat, start, maxit): - """ - Power method implementation. - Input: - * mat: matrix - * start: initialization - * maxit: number of iterations - Output: - * vec: largest eigenvector of mat - """ - vec = numpy.copy(start) - vec = vec/numpy.linalg.norm(vec) + """ + Power method implementation. + Input: + * mat: matrix + * start: initialization + * maxit: number of iterations + Output: + * vec: largest eigenvector of mat + """ + vec = np.copy(start) + vec = vec / np.linalg.norm(vec) - for i in range(maxit): - vec = numpy.dot(vec, mat) + for i in range(maxit): + vec = np.dot(vec, mat) - return vec + return vec def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): - """ - Spectral cut implementation. - Input: - * CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix - * L: graph laplacian matrix - * C: laplacian complete graph - * A: pairwise squared difference matrix - * start: initialization - * beta: regularization parameter - * k: max edges cut - * ind: vertex index vertex: unique integer - Output: - * res: dictionary with following fields: - - x: indicator vector - - size: number of edges cut - - score: cut score - - energy: cut energy - """ - isqrtCL = sqrtmi( C + beta * L) - M = numpy.dot(numpy.dot(isqrtCL, CAC), isqrtCL) - - (eigvals, eigvecs) = scipy.linalg.eigh(M,eigvals=(0,0)) - x = numpy.asarray(numpy.dot(eigvecs[:,0], isqrtCL))[0,:] - - (x, score, size, energy) = sweep_opt(x, F, G, k, ind) - - res = {} - res["x"] = numpy.array(x) - res["size"] = size - res["score"] = score - res["energy"] = energy - - return res + """ + Spectral cut implementation. + Input: + * CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix + * L: graph laplacian matrix + * C: laplacian complete graph + * A: pairwise squared difference matrix + * start: initialization + * beta: regularization parameter + * k: max edges cut + * ind: vertex index vertex: unique integer + Output: + * res: dictionary with following fields: + - x: indicator vector + - size: number of edges cut + - score: cut score + - energy: cut energy + """ + isqrtCL = gsp.sqrtmi(C + beta * L) + M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) + + (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 0)) + x = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] + + (x, score, size, energy) = sweep_opt(x, F, G, k, ind) + + res = {} + res["x"] = np.array(x) + res["size"] = size + res["score"] = score + res["energy"] = energy + + return res + def eig_vis_opt(G, F, beta): - """ - Computes first and second eigenvector of sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. - Input: - * G: graph - * F: graph signal - * beta: regularization parameter - Output: - * v1: first eigenvector - * v2: second eigenvector - """ - ind = {} - i = 0 - - for v in G.nodes(): - ind[v] = i - i = i + 1 - - C = laplacian_complete(networkx.number_of_nodes(G)) - A = weighted_adjacency_complete(G, F, ind) - CAC = numpy.dot(numpy.dot(C,A), C) - L = networkx.laplacian_matrix(G).todense() - - isqrtCL = sqrtmi( C + beta * L) - M = numpy.dot(numpy.dot(isqrtCL, CAC), isqrtCL) - - (eigvals, eigvecs) = scipy.linalg.eigh(M,eigvals=(0,1)) - x1 = numpy.asarray(numpy.dot(eigvecs[:,0], isqrtCL))[0,:] - x2 = numpy.asarray(numpy.dot(eigvecs[:,1], isqrtCL))[0,:] - - return x1, x2 + """ + Computes first and second eigenvector of sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. + Input: + * G: graph + * F: graph signal + * beta: regularization parameter + Output: + * v1: first eigenvector + * v2: second eigenvector + """ + ind = {} + i = 0 + + for v in G.nodes(): + ind[v] = i + i = i + 1 + + C = laplacian_complete(nx.number_of_nodes(G)) + A = weighted_adjacency_complete(G, F, ind) + CAC = np.dot(np.dot(C, A), C) + L = nx.laplacian_matrix(G).todense() + + isqrtCL = gsp.sqrtmi(C + beta * L) + M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) + + (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 1)) + x1 = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] + x2 = np.asarray(np.dot(eigvecs[:, 1], isqrtCL))[0, :] + + return x1, x2 + def trans(L, min_v, max_v): - """ - Chebyshev polynomial translation. - Input: - * L: Laplacian matrix - * min_v: lower bound - * max_v: upper bound - Output: - * translation - """ - return (float(2.) / (max_v-min_v)) * L, -(float(max_v+min_v) / (max_v-min_v)) + """ + Chebyshev polynomial translation. + Input: + * L: Laplacian matrix + * min_v: lower bound + * max_v: upper bound + Output: + * translation + """ + return (float(2.) / (max_v - min_v)) * L, -(float(max_v + min_v) / (max_v - min_v)) + def fun(k, n, beta, min_v, max_v, x): - """ - Function to be integrated in Chebyshev polynomial computation. - Input: - * k: coefficient number - * n: number of polynomials - * min_v: lower bound - * max_v: upper bound - Output: - * function value - """ - y = 0.5 * math.cos(x) * float(max_v - min_v) + (0.5 * (max_v + min_v)) - - return math.cos(k*x)*(float(1.) / math.sqrt(beta*y)) + """ + Function to be integrated in Chebyshev polynomial computation. + Input: + * k: coefficient number + * n: number of polynomials + * min_v: lower bound + * max_v: upper bound + Output: + * function value + """ + y = 0.5 * math.cos(x) * float(max_v - min_v) + (0.5 * (max_v + min_v)) + + return math.cos(k * x) * (float(1.) / math.sqrt(beta * y)) + def coef(k, n, beta, min_v, max_v): - """ - Chebyshev polynomial coefficients. - Input: - * k: coefficient number - * n: number of polynomials - * min_v: lower bound - * max_v: upper bound - Output: - * coefficient - """ - return float(2. * scipy.integrate.quad(lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi + """ + Chebyshev polynomial coefficients. + Input: + * k: coefficient number + * n: number of polynomials + * min_v: lower bound + * max_v: upper bound + Output: + * coefficient + """ + return float(2. * scipy.integrate.quad(lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi + def chebyshev_approx_2d(n, beta, X, L): - """ - Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using Chebyshev polynomials (twice) - Input: - * n: number of polynomials - * beta: regularization parameter - * X: matrix - * L: Laplacian matrix - Output: - * P2: approximation - """ - max_v = beta * L.shape[0] - min_v = 1 - - ts1, ts2 = trans(L, min_v, max_v) - P1 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * X - tkm2 = X - tkm1 = scipy.sparse.csr_matrix.dot(ts1, X) + ts2 * X - P1 = P1 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - - for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 - P1 = P1 + coef(i, L.shape[0], beta, min_v, max_v) * Tk - tkm2 = tkm1 - tkm1 = Tk - - P1 = P1.transpose() - P2 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * P1 - tkm2 = P1 - tkm1 = scipy.sparse.csr_matrix.dot(ts1, P1) + ts2 * P1 - - P2 = P2 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - - for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 - P2 = P2 + coef(i, L.shape[0], beta, min_v, max_v) * Tk - tkm2 = tkm1 - tkm1 = Tk - - return P2 + """ + Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using Chebyshev polynomials (twice) + Input: + * n: number of polynomials + * beta: regularization parameter + * X: matrix + * L: Laplacian matrix + Output: + * P2: approximation + """ + max_v = beta * L.shape[0] + min_v = 1 + + ts1, ts2 = trans(L, min_v, max_v) + P1 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * X + tkm2 = X + tkm1 = scipy.sparse.csr_matrix.dot(ts1, X) + ts2 * X + P1 = P1 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 + + for i in range(2, n): + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 + P1 = P1 + coef(i, L.shape[0], beta, min_v, max_v) * Tk + tkm2 = tkm1 + tkm1 = Tk + + P1 = P1.transpose() + P2 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * P1 + tkm2 = P1 + tkm1 = scipy.sparse.csr_matrix.dot(ts1, P1) + ts2 * P1 + + P2 = P2 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 + + for i in range(2, n): + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 + P2 = P2 + coef(i, L.shape[0], beta, min_v, max_v) * Tk + tkm2 = tkm1 + tkm1 = Tk + + return P2 + def chebyshev_approx_1d(n, beta, x, L): - """ - Approximates x*sqrt(L^+) using Chebyshev polynomials. - Input: - * n: number of polynomials - * beta: regularization parameter - * x: vector - * L: graph Laplacian - Output: - * P: approximation - - """ - max_v = beta * L.shape[0] - min_v = 1 - - ts1, ts2 = trans(L, min_v, max_v) - P = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * x - tkm2 = x - tkm1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x - P = P + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - - for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 - P = P + coef(i, L.shape[0], beta, min_v, max_v) * Tk - tkm2 = tkm1 - tkm1 = Tk - - return P + """ + Approximates x*sqrt(L^+) using Chebyshev polynomials. + Input: + * n: number of polynomials + * beta: regularization parameter + * x: vector + * L: graph Laplacian + Output: + * P: approximation + + """ + max_v = beta * L.shape[0] + min_v = 1 + + ts1, ts2 = trans(L, min_v, max_v) + P = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * x + tkm2 = x + tkm1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x + P = P + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 + + for i in range(2, n): + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 + P = P + coef(i, L.shape[0], beta, min_v, max_v) * Tk + tkm2 = tkm1 + tkm1 = Tk + + return P + def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): - """ - Fast spectral cut implementation using chebyshev polynomials. - Input: - * CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix - * start: initialization - * F: graph signal - * G: graph - * L: graph laplacian matrix - * beta: regularization parameter - * k: max edges cut - * n: number of polynomials - * ind: vertex index vertex: unique integer - Output: - * res: dictionary with following fields: - - x: indicator vector - - size: number of edges cut - - score: cut score - - energy: cut energy - """ - L = networkx.laplacian_matrix(G) - M = chebyshev_approx_2d(n, beta, CAC, L) - - eigvec = power_method(-M, start, 10) - x = chebyshev_approx_1d(n, beta, eigvec, L) - - (x, score, size, energy) = sweep_opt(x, F, G, k, ind) - - res = {} - res["x"] = numpy.array(x) - res["size"] = size - res["score"] = score - res["energy"] = energy - - return res + """ + Fast spectral cut implementation using chebyshev polynomials. + Input: + * CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix + * start: initialization + * F: graph signal + * G: graph + * L: graph laplacian matrix + * beta: regularization parameter + * k: max edges cut + * n: number of polynomials + * ind: vertex index vertex: unique integer + Output: + * res: dictionary with following fields: + - x: indicator vector + - size: number of edges cut + - score: cut score + - energy: cut energy + """ + L = nx.laplacian_matrix(G) + M = chebyshev_approx_2d(n, beta, CAC, L) + + eigvec = power_method(-M, start, 10) + x = chebyshev_approx_1d(n, beta, eigvec, L) + + (x, score, size, energy) = sweep_opt(x, F, G, k, ind) + + res = {} + res["x"] = np.array(x) + res["size"] = size + res["score"] = score + res["energy"] = energy + + return res + def fast_search(G, F, k, n, ind): - """ - Efficient version of cut computation. Does not perform 1-D search for beta. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * n: number of chebyshev polynomials - * ind: vertex index vertex: unique integer - Output: - * cut - """ - start = numpy.ones(networkx.number_of_nodes(G)) - C = laplacian_complete(networkx.number_of_nodes(G)) - A = weighted_adjacency_complete(G, F, ind) - CAC = fast_cac(G, F, ind) - - return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind) - -gr=(math.sqrt(5)-1)/2 + """ + Efficient version of cut computation. Does not perform 1-D search for beta. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * n: number of chebyshev polynomials + * ind: vertex index vertex: unique integer + Output: + * cut + """ + start = np.ones(nx.number_of_nodes(G)) + C = laplacian_complete(nx.number_of_nodes(G)) + A = weighted_adjacency_complete(G, F, ind) + CAC = fast_cac(G, F, ind) + + return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind) + + +gr = (math.sqrt(5) - 1) / 2 + def one_d_search(G, F, k, ind): - """ - Cut computation. Perform 1-D search for beta using golden search. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * n: number of chebyshev polynomials - * ind: vertex index vertex: unique integer - Output: - * cut - """ - C = laplacian_complete(networkx.number_of_nodes(G)) - A = weighted_adjacency_complete(G,F, ind) - CAC = numpy.dot(numpy.dot(C,A), C) - start = numpy.ones(networkx.number_of_nodes(G)) - L = networkx.laplacian_matrix(G).todense() - - #Upper and lower bounds for search - a = 0. - b = 1000. - c=b-gr*(b-a) - d=a+gr*(b-a) - - #Tolerance - tol = 1. - - resab = {} - resab["size"] = k + 1 - - #golden search - while abs(c-d)>tol or resab["size"] > k: - resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind) - resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) - - if resc["size"] <= k: - if resc["score"] > resd["score"]: - start = numpy.array(resc["x"]) - b = d - d = c - c=b-gr*(b-a) - else: - start = numpy.array(resd["x"]) - a=c - c=d - d=a+gr*(b-a) - else: - start = numpy.array(resc["x"]) - a=c - c=d - d=a+gr*(b-a) - - resab = spectral_cut(CAC, L, C, A, start, F, G, (b+a) / 2, k, ind) - - return resab + """ + Cut computation. Perform 1-D search for beta using golden search. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * n: number of chebyshev polynomials + * ind: vertex index vertex: unique integer + Output: + * cut + """ + C = laplacian_complete(nx.number_of_nodes(G)) + A = weighted_adjacency_complete(G, F, ind) + CAC = np.dot(np.dot(C, A), C) + start = np.ones(nx.number_of_nodes(G)) + L = nx.laplacian_matrix(G).todense() + + # Upper and lower bounds for search + a = 0. + b = 1000. + c = b - gr * (b - a) + d = a + gr * (b - a) + + # Tolerance + tol = 1. + + resab = {} + resab["size"] = k + 1 + + # golden search + while abs(c - d) > tol or resab["size"] > k: + resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind) + resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) + + if resc["size"] <= k: + if resc["score"] > resd["score"]: + start = np.array(resc["x"]) + b = d + d = c + c = b - gr * (b - a) + else: + start = np.array(resd["x"]) + a = c + c = d + d = a + gr * (b - a) + else: + start = np.array(resc["x"]) + a = c + c = d + d = a + gr * (b - a) + + resab = spectral_cut(CAC, L, C, A, start, F, G, (b + a) / 2, k, ind) + + return resab + def get_subgraphs(G, cut): - """ - Compute subgraphs generated by a cut. - Input: - * G: Original graph - * cut: cut indicator vector - Output: - * G1: subgraph 1 - * G2: subgraph 2 - """ - G1 = networkx.Graph() - G2 = networkx.Graph() - i = 0 - P1 = [] - P2 = [] - for v in G.nodes(): - if cut[i] < 0: - P1.append(v) - else: - P2.append(v) - i = i + 1 - - G1 = G.subgraph(P1) - G2 = G.subgraph(P2) - - return G1, G2 + """ + Compute subgraphs generated by a cut. + Input: + * G: Original graph + * cut: cut indicator vector + Output: + * G1: subgraph 1 + * G2: subgraph 2 + """ + G1 = nx.Graph() + G2 = nx.Graph() + i = 0 + P1 = [] + P2 = [] + for v in G.nodes(): + if cut[i] < 0: + P1.append(v) + else: + P2.append(v) + i = i + 1 + + G1 = G.subgraph(P1) + G2 = G.subgraph(P2) + + return G1, G2 + def optimal_wavelet_basis(G, F, k, npol): - """ - Computation of optimal graph wavelet basis. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * npol: number of chebyshev polynomials, if 0 run exact version - Output: - * root: tree root - * ind: vertex index vertex: unique integer - * size: number of edges cut - """ - - #Creating index - ind = {} - i = 0 - for v in G.nodes(): - ind[v] = i - i = i+1 - - #First cut - root = Node(None) - size = 0 - cand_cuts = [] - - if npol == 0: - c = one_d_search(G, F, k, ind) - else: - c = fast_search(G, F, k, npol, ind) - - c["parent"] = root - c["graph"] = G - - cand_cuts.append(c) - - #Recursively compute new cuts - while size <= k and len(cand_cuts) > 0: - best_cut = None - b = 0 - - for i in range(0, len(cand_cuts)): - if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["score"] > 0: - if best_cut is None or cand_cuts[i]["score"] > best_cut["score"]: - best_cut = cand_cuts[i] - b = i - if best_cut is None: - break - else: - #Compute cut on left and right side - (G1, G2) = get_subgraphs(best_cut["graph"], best_cut["x"]) - best_cut["parent"].cut = best_cut["size"] - size = size + best_cut["size"] - - if networkx.number_of_nodes(G1) == 1: - n = Node(ind[G1.nodes()[0]]) - best_cut["parent"].add_child(n) - elif networkx.number_of_nodes(G1) > 0: - n = Node(None) - - if npol == 0: - c = one_d_search(G1, F, k, ind) - else: - c = fast_search(G1, F, k, npol, ind) - - c["parent"] = n - c["graph"] = G1 - cand_cuts.append(c) - - best_cut["parent"].add_child(n) - - if networkx.number_of_nodes(G2) == 1: - n = Node(ind[G2.nodes()[0]]) - best_cut["parent"].add_child(n) - elif networkx.number_of_nodes(G2) > 0: - n = Node(None) - - if npol == 0: - c = one_d_search(G2, F, k, ind) - else: - c = fast_search(G2, F, k, npol, ind) - - c["parent"] = n - c["graph"] = G2 - cand_cuts.append(c) - - best_cut["parent"].add_child(n) - - del cand_cuts[b] - - #Compute remaining cuts using ratio cuts once budget is over (not optimal) - for i in range(0, len(cand_cuts)): - rc_recursive(cand_cuts[i]["parent"], cand_cuts[i]["graph"], ind) - - set_counts(root) - - return root, ind, size + """ + Computation of optimal graph wavelet basis. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * npol: number of chebyshev polynomials, if 0 run exact version + Output: + * root: tree root + * ind: vertex index vertex: unique integer + * size: number of edges cut + """ + + # Creating index + ind = {} + i = 0 + for v in G.nodes(): + ind[v] = i + i = i + 1 + + # First cut + root = Node(None) + size = 0 + cand_cuts = [] + + if npol == 0: + c = one_d_search(G, F, k, ind) + else: + c = fast_search(G, F, k, npol, ind) + + c["parent"] = root + c["graph"] = G + + cand_cuts.append(c) + + # Recursively compute new cuts + while size <= k and len(cand_cuts) > 0: + best_cut = None + b = 0 + + for i in range(0, len(cand_cuts)): + if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["score"] > 0: + if best_cut is None or cand_cuts[i]["score"] > best_cut["score"]: + best_cut = cand_cuts[i] + b = i + if best_cut is None: + break + else: + # Compute cut on left and right side + (G1, G2) = get_subgraphs(best_cut["graph"], best_cut["x"]) + best_cut["parent"].cut = best_cut["size"] + size = size + best_cut["size"] + + if nx.number_of_nodes(G1) == 1: + n = Node(ind[G1.nodes()[0]]) + best_cut["parent"].add_child(n) + elif nx.number_of_nodes(G1) > 0: + n = Node(None) + + if npol == 0: + c = one_d_search(G1, F, k, ind) + else: + c = fast_search(G1, F, k, npol, ind) + + c["parent"] = n + c["graph"] = G1 + cand_cuts.append(c) + + best_cut["parent"].add_child(n) + + if nx.number_of_nodes(G2) == 1: + n = Node(ind[G2.nodes()[0]]) + best_cut["parent"].add_child(n) + elif nx.number_of_nodes(G2) > 0: + n = Node(None) + + if npol == 0: + c = one_d_search(G2, F, k, ind) + else: + c = fast_search(G2, F, k, npol, ind) + + c["parent"] = n + c["graph"] = G2 + cand_cuts.append(c) + + best_cut["parent"].add_child(n) + + del cand_cuts[b] + + # Compute remaining cuts using ratio cuts once budget is over (not optimal) + for i in range(0, len(cand_cuts)): + gsp.rc_recursive(cand_cuts[i]["parent"], cand_cuts[i]["graph"], ind) + + gsp.set_counts(root) + + return root, ind, size From 6771cd21d4198c9e781b076a67059b6fb4c6c3b2 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 16:13:01 +0200 Subject: [PATCH 06/62] Remove unintentional comment --- lib/optimal_cut.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 2312aa9..af919f7 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -8,17 +8,6 @@ import lib.graph_signal_proc as gsp -""" -import math - -import numpy as np -import scipy -import networkx as nx - - -""" - - def sweep_opt(x, F, G, k, ind): """ Sweep algorithm for sparse wavelets. From 2c5b1b9e8bc93f2fd02d1a388c686aec72749b5d Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 16:38:01 +0200 Subject: [PATCH 07/62] Clean graph_signal_proc imports --- lib/graph_signal_proc.py | 1812 +++++++++++++++++++------------------- 1 file changed, 924 insertions(+), 888 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 1563caa..6a1893a 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -1,1002 +1,1038 @@ -import networkx import math -import scipy.optimize -import numpy +import random import sys +from collections import deque + +import networkx as nx +import numpy as np +from numpy import dot, diag, sqrt +from numpy.linalg import eigh +import scipy.optimize from scipy import linalg -import matplotlib.pyplot as plt -from IPython.display import Image -import pywt import scipy.fftpack -import random -import operator -import copy -from collections import deque + from sklearn.preprocessing import normalize -from sklearn.cluster import SpectralClustering -import statistics -from numpy import log, mean, dot, diag, sqrt -from numpy.linalg import eigh def compute_eigenvectors_and_eigenvalues(L): - """ - Computes eigenvectors and eigenvalues of the matrix L - Input: - * L: matrix - Output: - * U: eigenvector matrix, one vector/column, sorted by corresponsing eigenvalue - * lamb: eigenvalues, sorted in increasing order - """ - lamb, U = linalg.eig(L) - - idx = lamb.argsort() - lamb = lamb[idx] - U = U[:,idx] - - return U, lamb + """ + Computes eigenvectors and eigenvalues of the matrix L + Input: + * L: matrix + Output: + * U: eigenvector matrix, one vector/column, sorted by corresponsing eigenvalue + * lamb: eigenvalues, sorted in increasing order + """ + lamb, U = linalg.eig(L) + + idx = lamb.argsort() + lamb = lamb[idx] + U = U[:, idx] + + return U, lamb + def s(x): - """ - Cubic spline. - Input: - * x - Output: - * spline(x) - """ - return -5 + 11*x - 6*pow(x, 2) + pow(x, 3) + """ + Cubic spline. + Input: + * x + Output: + * spline(x) + """ + return -5 + 11 * x - 6 * pow(x, 2) + pow(x, 3) + def g(x): - """ - Wavelet kernel. - Input: - * x - Output: - * kernel of x - """ - a = 2 - b = 2 - x_1 = 1 - x_2 = 2 - - if x < x_1: - return pow(x_1, -a)*pow(x, a) - elif x <= x_2 and x >= x_1: - return s(x) - else: - return pow(x_2, b)*pow(x, -b) + """ + Wavelet kernel. + Input: + * x + Output: + * kernel of x + """ + a = 2 + b = 2 + x_1 = 1 + x_2 = 2 + + if x < x_1: + return pow(x_1, -a) * pow(x, a) + elif x <= x_2 and x >= x_1: + return s(x) + else: + return pow(x_2, b) * pow(x, -b) + def comp_gamma(): - """ - Computes gamma function - Input: - * None - Output: - * Gamma function (array) - """ - gn = lambda x: -1 * g(x) - xopt = scipy.optimize.fminbound(gn, 1, 2) - return xopt + """ + Computes gamma function + Input: + * None + Output: + * Gamma function (array) + """ + def gn(x): return -1 * g(x) + xopt = scipy.optimize.fminbound(gn, 1, 2) + return xopt + def h(x, gamma, lamb_max, K): - """ - Scaling function (see details in the paper "Graph wavelets via spectral theory". - Input: - * x - * gamma - * lamb_max: upper bound spectrum - * K: normalization - Output: - * value of scaling function - """ - lamb_min = float(lamb_max) / K - return gamma * math.exp(-pow(float(x/(lamb_min * 0.6)), 4)) + """ + Scaling function (see details in the paper "Graph wavelets via spectral theory". + Input: + * x + * gamma + * lamb_max: upper bound spectrum + * K: normalization + Output: + * value of scaling function + """ + lamb_min = float(lamb_max) / K + return gamma * math.exp(-pow(float(x / (lamb_min * 0.6)), 4)) + def comp_scales(lamb_max, K, J): - """ - Computes wavelet scales - Input: - * lamb_max: upper bound spectrum - * K: normalization - * J: number of scales - Output: - * scales array - """ - lamb_min = float(lamb_max) / K - s_min = float(1)/lamb_max - s_max = float(2)/lamb_min - - return numpy.exp(numpy.linspace(math.log(s_max), math.log(s_min), J)) + """ + Computes wavelet scales + Input: + * lamb_max: upper bound spectrum + * K: normalization + * J: number of scales + Output: + * scales array + """ + lamb_min = float(lamb_max) / K + s_min = float(1) / lamb_max + s_max = float(2) / lamb_min + + return np.exp(np.linspace(math.log(s_max), math.log(s_min), J)) + def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): - """ - Low-pass filter. - Input: - * lamb: eigenvalues - * U: eigenvector matrix - * N: number of nodes - * T: wavelet scales - * gamma: - * lamb_max: upper-bound spectrum - * K: normalization - Output: - * s: Low-pass filter as a #vertices x #vertices matrix - """ - s = [] - - for n in range(0, len(N)): - s.append([]) - - for n in range(0, len(N)): - for m in range(0, len(U)): - s_n_m = 0 - - for x in range(0, len(U)): - s_n_m = s_n_m + U[n][x] * U[m][x] * h(T[-1] * lamb[x], gamma, lamb_max, K) - - s[n].append(s_n_m) - - return s + """ + Low-pass filter. + Input: + * lamb: eigenvalues + * U: eigenvector matrix + * N: number of nodes + * T: wavelet scales + * gamma: + * lamb_max: upper-bound spectrum + * K: normalization + Output: + * s: Low-pass filter as a #vertices x #vertices matrix + """ + s = [] + + for n in range(0, len(N)): + s.append([]) + + for n in range(0, len(N)): + for m in range(0, len(U)): + s_n_m = 0 + + for x in range(0, len(U)): + s_n_m = s_n_m + U[n][x] * U[m][x] * h(T[-1] * lamb[x], gamma, lamb_max, K) + + s[n].append(s_n_m) + + return s + def graph_wavelets(lamb, U, N, T): - """ - Graph wavelets. - Input: - * lamb: eigenvalues - * U: eigenvector matrix - * N: number of nodes - * T: wavelet scales - Output: - * w: wavelets as a #vertices x #vertices x #scales matrix - """ - w = [] + """ + Graph wavelets. + Input: + * lamb: eigenvalues + * U: eigenvector matrix + * N: number of nodes + * T: wavelet scales + Output: + * w: wavelets as a #vertices x #vertices x #scales matrix + """ + w = [] - for t in range(0, len(T)): - w.append([]) - for n in range(0, len(N)): - w[t].append([]) + for t in range(0, len(T)): + w.append([]) + for n in range(0, len(N)): + w[t].append([]) + for t in range(0, len(T)): + for n in range(0, len(N)): + for m in range(0, len(U)): + w_t_n_m = 0 - for t in range(0, len(T)): - for n in range(0, len(N)): - for m in range(0, len(U)): - w_t_n_m = 0 + for x in range(0, len(U)): + w_t_n_m = w_t_n_m + U[n][x] * U[m][x] * g(T[t] * lamb[x]) - for x in range(0, len(U)): - w_t_n_m = w_t_n_m + U[n][x] * U[m][x] * g(T[t] * lamb[x]) + w[t][n].append(w_t_n_m) - w[t][n].append(w_t_n_m) + return w - return w def graph_fourier(F, U): - """ - Graph Fourier transform. - Input: - * F: Graph signal as a #vertices size array, values ordered by G.nodes() - * U: Eigenvectors matrix - Ouput: - * lambdas: Graph Fourier transform - """ - lambdas = [] + """ + Graph Fourier transform. + Input: + * F: Graph signal as a #vertices size array, values ordered by G.nodes() + * U: Eigenvectors matrix + Ouput: + * lambdas: Graph Fourier transform + """ + lambdas = [] + + for i in range(0, len(U)): + lambdas.append(np.dot(F, U[:, i])) - for i in range(0, len(U)): - lambdas.append(numpy.dot(F, U[:,i])) + lambdas = np.array(lambdas) - lambdas = numpy.array(lambdas) + return lambdas - return lambdas def graph_fourier_inverse(GF, U): - """ - Graph Fourier inverse: - Input: - * GF: Graph fourier transform - * U: Eigenvectors matrix - Output: - * F: Inverse - """ - F = numpy.zeros(U.shape[0]) - for v in range(U.shape[0]): - for u in range(U.shape[1]): - F[v] = F[v] + (GF[u]*U[v][u]).real - - return F + """ + Graph Fourier inverse: + Input: + * GF: Graph fourier transform + * U: Eigenvectors matrix + Output: + * F: Inverse + """ + F = np.zeros(U.shape[0]) + for v in range(U.shape[0]): + for u in range(U.shape[1]): + F[v] = F[v] + (GF[u] * U[v][u]).real + + return F + def hammond_wavelet_transform(w, s, T, F): - """ - Hammond wavelet transform. - Input: - * w: wavelets - * s: low-pass wavelets - * T: wavelet scales - * F: graph signal - Output: - * C: Hammond's wavelet transform - """ - C = [] - - for i in range(len(T)): - C.append([]) - for j in range(len(F)): - dotp = numpy.dot(F, w[i][j]) - C[i].append(dotp) - - C.append([]) - for j in range(len(F)): - dotp = numpy.dot(F, s[j]) - C[-1].append(dotp) - - return numpy.array(C) + """ + Hammond wavelet transform. + Input: + * w: wavelets + * s: low-pass wavelets + * T: wavelet scales + * F: graph signal + Output: + * C: Hammond's wavelet transform + """ + C = [] + + for i in range(len(T)): + C.append([]) + for j in range(len(F)): + dotp = np.dot(F, w[i][j]) + C[i].append(dotp) + + C.append([]) + for j in range(len(F)): + dotp = np.dot(F, s[j]) + C[-1].append(dotp) + + return np.array(C) + def hammond_wavelets_inverse(w, s, C): - """ - Hammond's wavelet inverse. - Input: - * w: wavelets - * s: low-pass wavelets - * C: transform - Output: - * F: inverse - """ - w = numpy.array(w) - Wc = numpy.append(w, numpy.array([s]), axis=0) - - nWc = Wc[0,:,:] - nC = C[0] - for i in range(1,Wc.shape[0]): - nWc = numpy.append(nWc, Wc[i,:,:], axis=0) - nC = numpy.append(nC, C[i], axis=0) - - nWc = numpy.array(nWc) - nC = numpy.array(nC) - - F = numpy.linalg.lstsq(nWc, nC)[0] - - return F + """ + Hammond's wavelet inverse. + Input: + * w: wavelets + * s: low-pass wavelets + * C: transform + Output: + * F: inverse + """ + w = np.array(w) + Wc = np.append(w, np.array([s]), axis=0) + + nWc = Wc[0, :, :] + nC = C[0] + for i in range(1, Wc.shape[0]): + nWc = np.append(nWc, Wc[i, :, :], axis=0) + nC = np.append(nC, C[i], axis=0) + + nWc = np.array(nWc) + nC = np.array(nC) + + F = np.linalg.lstsq(nWc, nC)[0] + + return F + class Node(object): - """ - Generic tree-structure used for hierarchical transforms. - """ - def __init__(self, data): - """ - Initialization. - Input: - * data: Anything to be stored in a node - """ - self.data = data - self.children = [] - self.avgs = [] - self.counts = [] - self.diffs = [] - self.scale = 0 - self.ftr = [] - self.L = [] - self.U = [] - self.cut = 0 - - if data is None: - self.count = 0 - else: - self.count = 1 - - def add_child(self, obj): - """ - Adds obj as a child to a node. - Input: - * obj: anything - """ - obj.scale = self.scale + 1 - self.children.append(obj) - self.count = self.count + obj.count + """ + Generic tree-structure used for hierarchical transforms. + """ + + def __init__(self, data): + """ + Initialization. + Input: + * data: Anything to be stored in a node + """ + self.data = data + self.children = [] + self.avgs = [] + self.counts = [] + self.diffs = [] + self.scale = 0 + self.ftr = [] + self.L = [] + self.U = [] + self.cut = 0 + + if data is None: + self.count = 0 + else: + self.count = 1 + + def add_child(self, obj): + """ + Adds obj as a child to a node. + Input: + * obj: anything + """ + obj.scale = self.scale + 1 + self.children.append(obj) + self.count = self.count + obj.count + def get_children(tree, part, G): - """ - Recursively gets all the children of a given node. - Input: - * tree: tree node - * part: list that will contain children - * G: graph - Output: - * None - """ - if tree.data is not None: - part.append(G.nodes()[tree.data]) - else: - for c in tree.children: - get_children(c, part, G) + """ + Recursively gets all the children of a given node. + Input: + * tree: tree node + * part: list that will contain children + * G: graph + Output: + * None + """ + if tree.data is not None: + part.append(G.nodes()[tree.data]) + else: + for c in tree.children: + get_children(c, part, G) + def set_counts(tree): - """ - Sets counts for intermediate nodes in the tree. - Input: - * tree: tree node - Output: - * count: count for the tree node - """ - if tree.data is not None: - tree.count = 1 - return 1 - else: - count = 0 - for c in tree.children: - count = count + set_counts(c) - - tree.count = count - - return count + """ + Sets counts for intermediate nodes in the tree. + Input: + * tree: tree node + Output: + * count: count for the tree node + """ + if tree.data is not None: + tree.count = 1 + return 1 + else: + count = 0 + for c in tree.children: + count = count + set_counts(c) + + tree.count = count + + return count + def partitions_level_rec(tree, level, G, l, partitions): - """ - Recursively extracts partitions up to a certain level in the tree. - Input: - * tree: tree - * level: max level - * G: graph - * l: current level - * partitions: partitions recovered - Output: - None - """ - if l >= level: - part = [] - get_children(tree, part, G) - if len(part) > 0: - partitions.append(part) - else: - if tree.data is None: - for c in tree.children: - partitions_level_rec(c, level, G, l+1, partitions) - else: - partitions.append([tree.data]) + """ + Recursively extracts partitions up to a certain level in the tree. + Input: + * tree: tree + * level: max level + * G: graph + * l: current level + * partitions: partitions recovered + Output: + None + """ + if l >= level: + part = [] + get_children(tree, part, G) + if len(part) > 0: + partitions.append(part) + else: + if tree.data is None: + for c in tree.children: + partitions_level_rec(c, level, G, l + 1, partitions) + else: + partitions.append([tree.data]) + def partitions_level(tree, level, G): - """ - Recovers partitions at a certain level of the three. - Input: - * tree: tree - * level: level - * G: graph - Output: - * partitions: set of vertices in each partition - """ - partitions = [] - partitions_level_rec(tree, level, G, 0, partitions) - - return partitions + """ + Recovers partitions at a certain level of the three. + Input: + * tree: tree + * level: level + * G: graph + Output: + * partitions: set of vertices in each partition + """ + partitions = [] + partitions_level_rec(tree, level, G, 0, partitions) + + return partitions + def build_matrix(G, ind): - """ - Builds graph distance matrix. - Input: - * G: graph - * ind: dictionary vertex: unique integer - Output: - * M: matrix - """ - M = [] - dists = networkx.all_pairs_dijkstra_path_length(G) + """ + Builds graph distance matrix. + Input: + * G: graph + * ind: dictionary vertex: unique integer + Output: + * M: matrix + """ + M = [] + dists = nx.all_pairs_dijkstra_path_length(G) - M = numpy.zeros((len(G.nodes()), len(G.nodes()))) + M = np.zeros((len(G.nodes()), len(G.nodes()))) - for v1 in G.nodes(): - for v2 in G.nodes(): - M[ind[v1]][ind[v2]] = dists[v1][v2] + for v1 in G.nodes(): + for v2 in G.nodes(): + M[ind[v1]][ind[v2]] = dists[v1][v2] + + return M - return M def select_centroids(M, radius): - """ - Selects half of the vertices as centroids. - Input: - * M - * radius - Output: - * centroids - """ - nodes = list(range(M.shape[0])) - random.shuffle(nodes) - nodes = nodes[:int(len(nodes)/2)] - cents = [nodes[0]] - mn = sys.float_info.min - - for i in range(1, len(nodes)): - add = True - for j in range(len(cents)): - if M[cents[j]][nodes[i]] <= radius*mn: - add = False - break - if add: - cents.append(nodes[i]) - - return cents + """ + Selects half of the vertices as centroids. + Input: + * M + * radius + Output: + * centroids + """ + nodes = list(range(M.shape[0])) + random.shuffle(nodes) + nodes = nodes[:int(len(nodes) / 2)] + cents = [nodes[0]] + mn = sys.float_info.min + + for i in range(1, len(nodes)): + add = True + for j in range(len(cents)): + if M[cents[j]][nodes[i]] <= radius * mn: + add = False + break + if add: + cents.append(nodes[i]) + + return cents + def coarse_matrix(M, H, cents, nodes): - """ - Makes matrix coarser based on centroids. - Input: - * M: distance matrix - * H: - * cents: centroids - * nodes: list of nodes - Output: - * Q: new matrix - * J - * new_nodes: new node list - """ - Q = numpy.zeros((len(cents), len(cents))) - J = [] - assigns = [] - new_nodes = [] - - for i in range(len(cents)): - J.append([]) - assigns.append([]) - new_nodes.append(Node(None)) - - for i in range(M.shape[0]): - min_dist = M[i][cents[0]] - min_cent = 0 - - for j in range(1, len(cents)): - if M[i][cents[j]] < min_dist: - min_dist = M[i][cents[j]] - min_cent = j - - J[min_cent].append(H[i]) - assigns[min_cent].append(i) - new_nodes[min_cent].add_child(nodes[i]) - - for i in range(len(cents)): - if len(new_nodes[i].children) == 1: - new_nodes[i] = new_nodes[i].children[0] - - for j in range(len(cents)): - if i != j: - for m in assigns[i]: - for k in assigns[j]: - Q[i][j] = Q[i][j] + pow(M[m][k], 2) - - Q = normalize(Q, axis=1, norm='l1') - - return Q, J, new_nodes + """ + Makes matrix coarser based on centroids. + Input: + * M: distance matrix + * H: + * cents: centroids + * nodes: list of nodes + Output: + * Q: new matrix + * J + * new_nodes: new node list + """ + Q = np.zeros((len(cents), len(cents))) + J = [] + assigns = [] + new_nodes = [] + + for i in range(len(cents)): + J.append([]) + assigns.append([]) + new_nodes.append(Node(None)) + + for i in range(M.shape[0]): + min_dist = M[i][cents[0]] + min_cent = 0 + + for j in range(1, len(cents)): + if M[i][cents[j]] < min_dist: + min_dist = M[i][cents[j]] + min_cent = j + + J[min_cent].append(H[i]) + assigns[min_cent].append(i) + new_nodes[min_cent].add_child(nodes[i]) + + for i in range(len(cents)): + if len(new_nodes[i].children) == 1: + new_nodes[i] = new_nodes[i].children[0] + + for j in range(len(cents)): + if i != j: + for m in assigns[i]: + for k in assigns[j]: + Q[i][j] = Q[i][j] + pow(M[m][k], 2) + + Q = normalize(Q, axis=1, norm='l1') + + return Q, J, new_nodes + def get_partitions(x, node_list): - """ - Gets partitions given indicator vector. - if x < 0: partition 1 - if x <= 0: partition 2 - Input: - * node_list: list of nodes - * x: indicator vector - Output: - * P1: partition 1 - * P2: partition 2 - """ - P1 = [] - P2 = [] - - for i in range(x.shape[0]): - if x[i] < 0: - P1.append(node_list[i]) - else: - P2.append(node_list[i]) - - return P1, P2 + """ + Gets partitions given indicator vector. + if x < 0: partition 1 + if x <= 0: partition 2 + Input: + * node_list: list of nodes + * x: indicator vector + Output: + * P1: partition 1 + * P2: partition 2 + """ + P1 = [] + P2 = [] + + for i in range(x.shape[0]): + if x[i] < 0: + P1.append(node_list[i]) + else: + P2.append(node_list[i]) + + return P1, P2 def get_new_laplacians(L, P1, P2, ind): - """ - Compute new Laplacian matrices for partitions P1 and P2. - Input: - * L: Higher-level laplacian - * P1: partition 1 - * P2: partition 2 - * ind: node index vertex: unique integer - Output: - * L1: Laplacian P1 - * L2: Laplacian P2 - """ - data = [] - row = [] - col = [] - - for i in range(len(P1)): - d = 0 - for j in range(len(P1)): - if i != j and L[ind[P1[i]],ind[P1[j]]] != 0: - row.append(i) - col.append(j) - data.append(float(L[ind[P1[i]],ind[P1[j]]])) - d = d - L[ind[P1[i]],ind[P1[j]]] - - row.append(i) - col.append(i) - data.append(float(d)) - - L1 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P1), len(P1))) - - data = [] - row = [] - col = [] - - for i in range(len(P2)): - d = 0 - for j in range(len(P2)): - if i != j and L[ind[P2[i]],ind[P2[j]]] != 0: - row.append(i) - col.append(j) - data.append(float(L[ind[P2[i]],ind[P2[j]]])) - d = d - L[ind[P2[i]],ind[P2[j]]] - - row.append(i) - col.append(i) - data.append(float(d)) - - L2 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P2), len(P2))) - - return L1, L2 + """ + Compute new Laplacian matrices for partitions P1 and P2. + Input: + * L: Higher-level laplacian + * P1: partition 1 + * P2: partition 2 + * ind: node index vertex: unique integer + Output: + * L1: Laplacian P1 + * L2: Laplacian P2 + """ + data = [] + row = [] + col = [] + + for i in range(len(P1)): + d = 0 + for j in range(len(P1)): + if i != j and L[ind[P1[i]], ind[P1[j]]] != 0: + row.append(i) + col.append(j) + data.append(float(L[ind[P1[i]], ind[P1[j]]])) + d = d - L[ind[P1[i]], ind[P1[j]]] + + row.append(i) + col.append(i) + data.append(float(d)) + + L1 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P1), len(P1))) + + data = [] + row = [] + col = [] + + for i in range(len(P2)): + d = 0 + for j in range(len(P2)): + if i != j and L[ind[P2[i]], ind[P2[j]]] != 0: + row.append(i) + col.append(j) + data.append(float(L[ind[P2[i]], ind[P2[j]]])) + d = d - L[ind[P2[i]], ind[P2[j]]] + + row.append(i) + col.append(i) + data.append(float(d)) + + L2 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P2), len(P2))) + + return L1, L2 + def laplacian_complete(n): - """ - Laplacian of a complete graph with n vertices. - Input: - * n: size - Output: - * C: Laplacian - """ - C = numpy.ones((n, n)) - C = -1 * C - D = numpy.diag(numpy.ones(n)) - C = (n)*D + C - - return C + """ + Laplacian of a complete graph with n vertices. + Input: + * n: size + Output: + * C: Laplacian + """ + C = np.ones((n, n)) + C = -1 * C + D = np.diag(np.ones(n)) + C = (n) * D + C + + return C + def sqrtm(mat): - """ - Matrix square root. - Input: - * mat: matrix - Output: - * matrix square root - """ - eigvals, eigvecs = eigh(mat) + """ + Matrix square root. + Input: + * mat: matrix + Output: + * matrix square root + """ + eigvals, eigvecs = eigh(mat) + + eigvecs = eigvecs[:, eigvals > 0] + eigvals = eigvals[eigvals > 0] - eigvecs = eigvecs[:, eigvals > 0] - eigvals = eigvals[eigvals > 0] + return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T)) - return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T)) def sqrtmi(mat): - """ - Computes the square-root inverse of a matrix. - Input: - * mat: matrix - Output: - * square root inverse - """ - eigvals, eigvecs = eigh(mat) - eigvecs = eigvecs[:, eigvals > 0] - eigvals = eigvals[eigvals > 0] - - return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) + """ + Computes the square-root inverse of a matrix. + Input: + * mat: matrix + Output: + * square root inverse + """ + eigvals, eigvecs = eigh(mat) + eigvecs = eigvecs[:, eigvals > 0] + eigvals = eigvals[eigvals > 0] + + return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) + def create_linked_list(L): - """ - Creates linked list from a Laplacian matrix. - Input: - * L: matrix - Output: - * linked_list: linked list - """ - linked_list = {} - - for i in L.nonzero()[0]: - linked_list[i] = [] - for j in range(L.shape[1]): - if L[i,j] < 0: - linked_list[i].append(j) - return linked_list + """ + Creates linked list from a Laplacian matrix. + Input: + * L: matrix + Output: + * linked_list: linked list + """ + linked_list = {} + + for i in L.nonzero()[0]: + linked_list[i] = [] + for j in range(L.shape[1]): + if L[i, j] < 0: + linked_list[i].append(j) + return linked_list + def sweep(x, G): - """ - Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian) based on vector x. - Input: - * x: vector - * G: graph - Output: - * vec: indicator vector - """ - best_val = networkx.number_of_nodes(G)-1 - sorted_x = numpy.argsort(x) - size_one = 0 - edges_cut = 0 - nodes_one = {} - - for i in range(x.shape[0]): - size_one = size_one + 1 - - nodes_one[G.nodes()[sorted_x[i]]] = True - - for v in G.neighbors(G.nodes()[sorted_x[i]]): - if v not in nodes_one: - edges_cut = edges_cut + 1 - else: - edges_cut = edges_cut - 1 - - den = size_one * (networkx.number_of_nodes(G)-size_one) - - if den > 0: - val = float(edges_cut) / den - else: - val = networkx.number_of_nodes(G) - - if val <= best_val: - best_cand = i - best_val = val - - vec = [] - - vec = numpy.zeros(networkx.number_of_nodes(G)) - - for i in range(x.shape[0]): - if i <= best_cand: - vec[sorted_x[i]] = -1. - else: - vec[sorted_x[i]] = 1. - - return vec + """ + Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian) based on vector x. + Input: + * x: vector + * G: graph + Output: + * vec: indicator vector + """ + best_val = nx.number_of_nodes(G) - 1 + sorted_x = np.argsort(x) + size_one = 0 + edges_cut = 0 + nodes_one = {} + + for i in range(x.shape[0]): + size_one = size_one + 1 + + nodes_one[G.nodes()[sorted_x[i]]] = True + + for v in G.neighbors(G.nodes()[sorted_x[i]]): + if v not in nodes_one: + edges_cut = edges_cut + 1 + else: + edges_cut = edges_cut - 1 + + den = size_one * (nx.number_of_nodes(G) - size_one) + + if den > 0: + val = float(edges_cut) / den + else: + val = nx.number_of_nodes(G) + + if val <= best_val: + best_cand = i + best_val = val + + vec = [] + + vec = np.zeros(nx.number_of_nodes(G)) + + for i in range(x.shape[0]): + if i <= best_cand: + vec[sorted_x[i]] = -1. + else: + vec[sorted_x[i]] = 1. + + return vec + def separate_lcc(G, G0): - """ - Separates vertices in G0 (LCC) from the rest in G using indicator vector. - Input: - * G: Graph - * G0: Subgraph - Output: - * x: indicator vector - """ - x = [] - - for v in G.nodes(): - if v in G0: - x.append(-1) - else: - x.append(1.) - - return numpy.array(x) + """ + Separates vertices in G0 (LCC) from the rest in G using indicator vector. + Input: + * G: Graph + * G0: Subgraph + Output: + * x: indicator vector + """ + x = [] + + for v in G.nodes(): + if v in G0: + x.append(-1) + else: + x.append(1.) + + return np.array(x) + def ratio_cut(G): - """ - Computes ratio-cut of G based on second eigenvector of the Laplacian. - Input: - * G: Graph - Output: - * x: Indicator vector - """ + """ + Computes ratio-cut of G based on second eigenvector of the Laplacian. + Input: + * G: Graph + Output: + * x: Indicator vector + """ - Gcc=sorted(networkx.connected_component_subgraphs(G), key = len, reverse=True) - G0=Gcc[0] + Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True) + G0 = Gcc[0] - if networkx.number_of_nodes(G) == networkx.number_of_nodes(G0): - x = networkx.fiedler_vector(G, method='lobpcg',tol=1e-5) + if nx.number_of_nodes(G) == nx.number_of_nodes(G0): + x = nx.fiedler_vector(G, method='lobpcg', tol=1e-5) - x = sweep(x, G) - else: - #In case G is not connected - x = separate_lcc(G, G0) + x = sweep(x, G) + else: + # In case G is not connected + x = separate_lcc(G, G0) + return np.array(x) - return numpy.array(x) def eig_vis_rc(G): - """ - Second and third eigenvectors of the graph Laplacian. For visualization. - Input: - * G: Graph - Output: - * x1: Second eigenvector - * x2: Third eigenvector - """ - L = networkx.laplacian_matrix(G).todense() - (eigvals, eigvecs) = scipy.linalg.eigh(L,eigvals=(1,2)) - - x1 = numpy.asarray(eigvecs[:,0]) - x2 = numpy.asarray(eigvecs[:,1]) - - return x1, x2 + """ + Second and third eigenvectors of the graph Laplacian. For visualization. + Input: + * G: Graph + Output: + * x1: Second eigenvector + * x2: Third eigenvector + """ + L = nx.laplacian_matrix(G).todense() + (eigvals, eigvecs) = scipy.linalg.eigh(L, eigvals=(1, 2)) + + x1 = np.asarray(eigvecs[:, 0]) + x2 = np.asarray(eigvecs[:, 1]) + + return x1, x2 + def get_subgraphs(G, cut): - """ - Compute subgraphs generated by a cut. - Input: - * G: Original graph - * cut: cut indicator vector - Output: - * G1: subgraph 1 - * G2: subgraph 2 - """ - G1 = networkx.Graph() - G2 = networkx.Graph() - - i = 0 - P1 = [] - P2 = [] - for v in G.nodes(): - if cut[i] < 0: - P1.append(v) - else: - P2.append(v) - i = i + 1 - - G1 = G.subgraph(P1) - G2 = G.subgraph(P2) - - return G1, G2 + """ + Compute subgraphs generated by a cut. + Input: + * G: Original graph + * cut: cut indicator vector + Output: + * G1: subgraph 1 + * G2: subgraph 2 + """ + G1 = nx.Graph() + G2 = nx.Graph() + + i = 0 + P1 = [] + P2 = [] + for v in G.nodes(): + if cut[i] < 0: + P1.append(v) + else: + P2.append(v) + i = i + 1 + + G1 = G.subgraph(P1) + G2 = G.subgraph(P2) + + return G1, G2 + def rc_recursive(node, G, ind): - """ - Recursively computes ratio-cut. - Input: - * node: tree node - * G: graph - * ind: vertex index v: unique integer - Output: - * none - """ - if networkx.number_of_nodes(G) < 3: - n = Node(None) - n.add_child(Node(ind[G.nodes()[0]])) - n.add_child(Node(ind[G.nodes()[1]])) - node.add_child(n) - else: - C = ratio_cut(G) - - (G1, G2) = get_subgraphs(G, C) - - if networkx.number_of_nodes(G1) > 1: - l = Node(None) - rc_recursive(l, G1, ind) - node.add_child(l) - else: - l = Node(ind[G1.nodes()[0]]) - node.add_child(l) - - if networkx.number_of_nodes(G2) > 1: - r = Node(None) - rc_recursive(r, G2, ind) - node.add_child(r) - else: - r = Node(ind[G2.nodes()[0]]) - node.add_child(r) + """ + Recursively computes ratio-cut. + Input: + * node: tree node + * G: graph + * ind: vertex index v: unique integer + Output: + * none + """ + if nx.number_of_nodes(G) < 3: + n = Node(None) + n.add_child(Node(ind[G.nodes()[0]])) + n.add_child(Node(ind[G.nodes()[1]])) + node.add_child(n) + else: + C = ratio_cut(G) + + (G1, G2) = get_subgraphs(G, C) + + if nx.number_of_nodes(G1) > 1: + l = Node(None) + rc_recursive(l, G1, ind) + node.add_child(l) + else: + l = Node(ind[G1.nodes()[0]]) + node.add_child(l) + + if nx.number_of_nodes(G2) > 1: + r = Node(None) + rc_recursive(r, G2, ind) + node.add_child(r) + else: + r = Node(ind[G2.nodes()[0]]) + node.add_child(r) + def ratio_cut_hierarchy(G): - """ - Computes ratio-cut hierarchy for a graph. - Input: - * G: graph - Output: - * root: tree root - * ind: graph index v: unique integer - """ - i = 0 - ind = {} - for v in G.nodes(): - ind[v] = i - i = i + 1 - - root = Node(None) - - rc_recursive(root, G, ind) - - return root, ind + """ + Computes ratio-cut hierarchy for a graph. + Input: + * G: graph + Output: + * root: tree root + * ind: graph index v: unique integer + """ + i = 0 + ind = {} + for v in G.nodes(): + ind[v] = i + i = i + 1 + + root = Node(None) + + rc_recursive(root, G, ind) + + return root, ind + def gavish_hierarchy(G, radius): - """ - Builds Gavish's hierarchy of a graph. - Input: - * G: graph - * radius: radius - Output: - * tree root - * ind: vertex index v: unique integer - """ - H = [] - nodes = [] - ind = {} - i = 0 - for v in G.nodes(): - ind[v] = i - nodes.append(Node(i)) - H.append(i) - i = i + 1 - - M = build_matrix(G, ind) - - while M.shape[0] > 1: - cents = select_centroids(M, radius) - Q, J, new_nodes = coarse_matrix(M, H, cents, nodes) - M = Q - H = J - nodes = new_nodes - - return nodes[0], ind + """ + Builds Gavish's hierarchy of a graph. + Input: + * G: graph + * radius: radius + Output: + * tree root + * ind: vertex index v: unique integer + """ + H = [] + nodes = [] + ind = {} + i = 0 + for v in G.nodes(): + ind[v] = i + nodes.append(Node(i)) + H.append(i) + i = i + 1 + + M = build_matrix(G, ind) + + while M.shape[0] > 1: + cents = select_centroids(M, radius) + Q, J, new_nodes = coarse_matrix(M, H, cents, nodes) + M = Q + H = J + nodes = new_nodes + + return nodes[0], ind + def compute_coefficients(tree, F): - """ - Computes tree coefficients for Gavish's transform. - Input: - * tree: tree - * F: graph signal - Output: - * None - """ - if tree.data is None: - avg = 0 - count = 0 - for i in range(len(tree.children)): - compute_coefficients(tree.children[i], F) - avg = avg + tree.children[i].avg * tree.children[i].count - count = count + tree.children[i].count - - if i > 0: - tree.avgs.append(float(avg) / count) - tree.counts.append(count) - tree.diffs.append(2*tree.children[i].count*(tree.children[i].avg-float(avg)/count)) - tree.avgs = list(reversed(tree.avgs)) - tree.avg = float(avg) / tree.count - else: - tree.avg = F[tree.data] + """ + Computes tree coefficients for Gavish's transform. + Input: + * tree: tree + * F: graph signal + Output: + * None + """ + if tree.data is None: + avg = 0 + count = 0 + for i in range(len(tree.children)): + compute_coefficients(tree.children[i], F) + avg = avg + tree.children[i].avg * tree.children[i].count + count = count + tree.children[i].count + + if i > 0: + tree.avgs.append(float(avg) / count) + tree.counts.append(count) + tree.diffs.append(2 * tree.children[i].count * + (tree.children[i].avg - float(avg) / count)) + tree.avgs = list(reversed(tree.avgs)) + tree.avg = float(avg) / tree.count + else: + tree.avg = F[tree.data] + def reconstruct_values(tree, F): - """ - Reconstructs values for Gavish's transform based on a tree. - Input: - * tree: tree - * F: graph signal - Output: - * None - """ - if tree.data is None: - avg = tree.avg * tree.count - count = tree.count - for i in reversed(range(len(tree.children))): - if i == 0: - tree.children[i].avg = avg / tree.children[i].count - reconstruct_values(tree.children[i], F) - else: - tree.children[i].avg = float(avg)/count + 0.5*float(tree.diffs[i-1]) / tree.children[i].count - reconstruct_values(tree.children[i], F) - count = count - tree.children[i].count - avg = avg - tree.children[i].avg * tree.children[i].count - tree.avgs.append(float(avg)/count) - - tree.avgs = list(reversed(tree.avgs)) - else: - F[tree.data] = tree.avg + """ + Reconstructs values for Gavish's transform based on a tree. + Input: + * tree: tree + * F: graph signal + Output: + * None + """ + if tree.data is None: + avg = tree.avg * tree.count + count = tree.count + for i in reversed(range(len(tree.children))): + if i == 0: + tree.children[i].avg = avg / tree.children[i].count + reconstruct_values(tree.children[i], F) + else: + tree.children[i].avg = float(avg) / count + 0.5 * \ + float(tree.diffs[i - 1]) / tree.children[i].count + reconstruct_values(tree.children[i], F) + count = count - tree.children[i].count + avg = avg - tree.children[i].avg * tree.children[i].count + tree.avgs.append(float(avg) / count) + + tree.avgs = list(reversed(tree.avgs)) + else: + F[tree.data] = tree.avg + def clear_tree(tree): - """ - Clears tree info. - Input: - * tree - Output: - * None - """ - tree.avg = 0 - tree.diffs = [] - tree.avgs = [] - - if tree.data is None: - for i in range(len(tree.children)): - clear_tree(tree.children[i]) + """ + Clears tree info. + Input: + * tree + Output: + * None + """ + tree.avg = 0 + tree.diffs = [] + tree.avgs = [] + + if tree.data is None: + for i in range(len(tree.children)): + clear_tree(tree.children[i]) + def get_coefficients(tree, wtr): - """ - Recovers wavelet coefficients from the wavelet tree. - Input: - * tree - * wtr: wavelet coefficients - Output: - * None - """ - Q = deque() - scales = [] - wtr.append(tree.count*tree.avg) - - Q.append(tree) - - while len(Q) > 0: - node = Q.popleft() - scales.append(node.scale) - - for j in range(len(node.diffs)): - wtr.append(node.diffs[j]) - - for i in range(len(node.children)): - Q.append(node.children[i]) + """ + Recovers wavelet coefficients from the wavelet tree. + Input: + * tree + * wtr: wavelet coefficients + Output: + * None + """ + Q = deque() + scales = [] + wtr.append(tree.count * tree.avg) + + Q.append(tree) + + while len(Q) > 0: + node = Q.popleft() + scales.append(node.scale) + + for j in range(len(node.diffs)): + wtr.append(node.diffs[j]) + + for i in range(len(node.children)): + Q.append(node.children[i]) + def get_cut_sizes(tree): - """ - Recovers cut sizes from tree. - Input: - * tree - Output: - * None - """ - Q = deque() - cut_sizes = [] + """ + Recovers cut sizes from tree. + Input: + * tree + Output: + * None + """ + Q = deque() + cut_sizes = [] + + Q.append(tree) - Q.append(tree) + while len(Q) > 0: + node = Q.popleft() + cut_sizes.append(node.cut) - while len(Q) > 0: - node = Q.popleft() - cut_sizes.append(node.cut) + for i in range(len(node.children)): + Q.append(node.children[i]) - for i in range(len(node.children)): - Q.append(node.children[i]) + return cut_sizes - return cut_sizes def set_coefficients(tree, wtr): - """ - Sets wavelet tree coefficients. - Input: - * tree - * wtr: wavelet coefficients - """ - Q = deque() - tree.avg = float(wtr[0]) / tree.count - p = 1 - Q.append(tree) - - while len(Q) > 0: - node = Q.popleft() - - for j in range(len(node.children)-1): - node.diffs.append(wtr[p]) - p = p + 1 - - for i in range(len(node.children)): - Q.append(node.children[i]) + """ + Sets wavelet tree coefficients. + Input: + * tree + * wtr: wavelet coefficients + """ + Q = deque() + tree.avg = float(wtr[0]) / tree.count + p = 1 + Q.append(tree) + + while len(Q) > 0: + node = Q.popleft() + + for j in range(len(node.children) - 1): + node.diffs.append(wtr[p]) + p = p + 1 + + for i in range(len(node.children)): + Q.append(node.children[i]) + def gavish_wavelet_transform(tree, ind, G, F): - """ - Gavish's wavelet transform. - Input: - * tree - * ind: vertex index v : unique integer - * G: graph - * F: graph signal - Output: - * wtr: wavelet transform. - """ - wtr = [] - clear_tree(tree) - compute_coefficients(tree, F) - get_coefficients(tree, wtr) - - return numpy.array(wtr) + """ + Gavish's wavelet transform. + Input: + * tree + * ind: vertex index v : unique integer + * G: graph + * F: graph signal + Output: + * wtr: wavelet transform. + """ + wtr = [] + clear_tree(tree) + compute_coefficients(tree, F) + get_coefficients(tree, wtr) + + return np.array(wtr) + def gavish_wavelet_inverse(tree, ind, G, wtr): - """ - Gavish's wavelet inverse. - Input: - * tree - * ind: vertex index v: unique integer - * G: graph - * wtr: wavelet transform - Output: - * F: wavelet inverse - """ - F = [] - - for i in range(len(G.nodes())): - F.append(0) - - clear_tree(tree) - set_coefficients(tree, wtr) - reconstruct_values(tree, F) - - return numpy.array(F) + """ + Gavish's wavelet inverse. + Input: + * tree + * ind: vertex index v: unique integer + * G: graph + * wtr: wavelet transform + Output: + * F: wavelet inverse + """ + F = [] + + for i in range(len(G.nodes())): + F.append(0) + + clear_tree(tree) + set_coefficients(tree, wtr) + reconstruct_values(tree, F) + + return np.array(F) From dd5b6e52f3b8831863e3b158d3d002b3f615995f Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 21:48:21 +0200 Subject: [PATCH 08/62] Fix some doc in graph_signal_proc --- lib/graph_signal_proc.py | 87 ++++++++++++++++++++++------------------ 1 file changed, 47 insertions(+), 40 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 6a1893a..b3a1c88 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -16,12 +16,13 @@ def compute_eigenvectors_and_eigenvalues(L): """ - Computes eigenvectors and eigenvalues of the matrix L - Input: - * L: matrix - Output: - * U: eigenvector matrix, one vector/column, sorted by corresponsing eigenvalue - * lamb: eigenvalues, sorted in increasing order + Computes eigenvectors and eigenvalues of the matrix L + Input: + * L: matrix + Output: + * U: eigenvector matrix, one vector/column, sorted by corresponding + eigenvalue + * lamb: eigenvalues, sorted in increasing order """ lamb, U = linalg.eig(L) @@ -34,22 +35,24 @@ def compute_eigenvectors_and_eigenvalues(L): def s(x): """ - Cubic spline. - Input: - * x - Output: - * spline(x) + Cubic spline, see Hammond, D. K.,Vandergheynst, P., & Gribonval, R. + (2011). "Wavelets on graphs via spectral graph theory". + Input: + * x + Output: + * spline(x) """ return -5 + 11 * x - 6 * pow(x, 2) + pow(x, 3) def g(x): """ - Wavelet kernel. - Input: - * x - Output: - * kernel of x + Wavelet generating kernel, see Hammond, D. K.,Vandergheynst, P., + & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory". + Input: + * x + Output: + * kernel of x """ a = 2 b = 2 @@ -66,27 +69,30 @@ def g(x): def comp_gamma(): """ - Computes gamma function - Input: - * None - Output: - * Gamma function (array) + In Hammond, D. K.,Vandergheynst, P.,& Gribonval, R. (2011). + "Wavelets on graphs via spectral graph theory" gamma is a parameter + used to determine the scaling function h. It is such that h(0) = max(g) + Input: + * None + Output: + * Gamma function (array) """ - def gn(x): return -1 * g(x) - xopt = scipy.optimize.fminbound(gn, 1, 2) + # fminbound finds the minimum within the optimization bounds + xopt = scipy.optimize.fminbound(lambda x: -g(x), 1, 2) return xopt def h(x, gamma, lamb_max, K): """ - Scaling function (see details in the paper "Graph wavelets via spectral theory". - Input: - * x - * gamma - * lamb_max: upper bound spectrum - * K: normalization - Output: - * value of scaling function + Scaling function see Hammond, D. K.,Vandergheynst, P., + & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory". + Input: + * x + * gamma + * lamb_max: upper bound spectrum + * K: normalization + Output: + * value of scaling function """ lamb_min = float(lamb_max) / K return gamma * math.exp(-pow(float(x / (lamb_min * 0.6)), 4)) @@ -94,13 +100,14 @@ def h(x, gamma, lamb_max, K): def comp_scales(lamb_max, K, J): """ - Computes wavelet scales - Input: - * lamb_max: upper bound spectrum - * K: normalization - * J: number of scales - Output: - * scales array + Computes wavelet scales see Hammond, D. K.,Vandergheynst, P., + & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory". + Input: + * lamb_max: upper bound spectrum + * K: normalization + * J: number of scales + Output: + * scales array """ lamb_min = float(lamb_max) / K s_min = float(1) / lamb_max @@ -111,13 +118,13 @@ def comp_scales(lamb_max, K, J): def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): """ - Low-pass filter. + Low-pass filter (square matrix). Input: * lamb: eigenvalues * U: eigenvector matrix * N: number of nodes * T: wavelet scales - * gamma: + * gamma: scaling function parameter * lamb_max: upper-bound spectrum * K: normalization Output: From 311c3d2479d47b16b01e54530969e74b8e44c827 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 28 Aug 2017 21:56:58 +0200 Subject: [PATCH 09/62] Fix incoherent use of arg N of graph_low_pass --- lib/graph_signal_proc.py | 8 +- lib/static.py | 401 ++++++++++++++++++++------------------- 2 files changed, 208 insertions(+), 201 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index b3a1c88..e10514a 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -132,14 +132,14 @@ def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): """ s = [] - for n in range(0, len(N)): + for n in range(N): s.append([]) - for n in range(0, len(N)): - for m in range(0, len(U)): + for n in range(N): + for m in range(len(U)): s_n_m = 0 - for x in range(0, len(U)): + for x in range(len(U)): s_n_m = s_n_m + U[n][x] * U[m][x] * h(T[-1] * lamb[x], gamma, lamb_max, K) s[n].append(s_n_m) diff --git a/lib/static.py b/lib/static.py index eb3fd74..b0240dd 100644 --- a/lib/static.py +++ b/lib/static.py @@ -17,217 +17,224 @@ from lib.graph_signal_proc import * from lib.optimal_cut import * -class Fourier(object): - """ - Graph Fourier transform. - """ - def name(self): - return "FT" - - def set_graph(self, _G): - self.G = _G - L = networkx.laplacian_matrix(self.G) - L = L.todense() - self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L) - - def transform(self, F): - """ - """ - return graph_fourier(F, self.U) - - def inverse(self, ftr): - """ - """ - return graph_fourier_inverse(ftr, self.U) - - def drop_frequency(self, ftr, n): - """ - Keeps only the n top-energy coefficients of ftr. - Input: - * ftr: transform - * n: number of coefficients - Output: - * ftr_copy: changed transform - """ - coeffs = {} - - for i in range(ftr.shape[0]): - coeffs[i] = abs(ftr[i]) - - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) - - ftr_copy = numpy.copy(ftr) - - for k in range(n, len(sorted_coeffs)): - i = sorted_coeffs[k][0] - - ftr_copy[i] = 0 - - return ftr_copy + +class Fourier(object): + """ + Graph Fourier transform. + """ + + def name(self): + return "FT" + + def set_graph(self, _G): + self.G = _G + L = networkx.laplacian_matrix(self.G) + L = L.todense() + self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L) + + def transform(self, F): + """ + """ + return graph_fourier(F, self.U) + + def inverse(self, ftr): + """ + """ + return graph_fourier_inverse(ftr, self.U) + + def drop_frequency(self, ftr, n): + """ + Keeps only the n top-energy coefficients of ftr. + Input: + * ftr: transform + * n: number of coefficients + Output: + * ftr_copy: changed transform + """ + coeffs = {} + + for i in range(ftr.shape[0]): + coeffs[i] = abs(ftr[i]) + + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + + ftr_copy = numpy.copy(ftr) + + for k in range(n, len(sorted_coeffs)): + i = sorted_coeffs[k][0] + + ftr_copy[i] = 0 + + return ftr_copy + class HWavelets(object): - """ - Hammond's wavelets (spectral theory) - """ - def name(self): - return "HWT" - - def set_graph(self, _G): - """ - """ - self.G = _G - L = networkx.normalized_laplacian_matrix(self.G) - L = L.todense() - self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L) - lamb_max = max(self.lamb_str.real) - - #default parameters defined by author - K = 100 - J = 4 - gamma = comp_gamma() - self.T = comp_scales(lamb_max, K, J) - self.w = graph_wavelets(self.lamb_str.real, self.U.real, range(len(self.G.nodes())), self.T) - self.s = graph_low_pass(self.lamb_str.real, self.U.real, range(len(self.G.nodes())), self.T, gamma, lamb_max, K) - - def transform(self, F): - """ - """ - return hammond_wavelet_transform(self.w, self.s, self.T, F) - - def inverse(self, wtr): - """ - """ - return hammond_wavelets_inverse(self.w, self.s, wtr) - - def drop_frequency(self, wtr, n): - """ - Keeps only the n top-energy coefficients of wtr. - Input: - * wtr: transform - * n: number of coefficients - Output: - * wtr_copy: changed transform - """ - coeffs = {} - for i in range(len(wtr)): - for j in range(len(wtr[i])): - coeffs[(i,j)] = abs(wtr[i][j]) - - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) - - wtr_copy = numpy.copy(wtr) - - for k in range(n, len(sorted_coeffs)): - i = sorted_coeffs[k][0][0] - j = sorted_coeffs[k][0][1] - - wtr_copy[i][j] = 0.0 - - return wtr_copy + """ + Hammond's wavelets (spectral theory) + """ + + def name(self): + return "HWT" + + def set_graph(self, _G): + """ + """ + self.G = _G + L = networkx.normalized_laplacian_matrix(self.G) + L = L.todense() + self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L) + lamb_max = max(self.lamb_str.real) + + # default parameters defined by author + K = 100 + J = 4 + gamma = comp_gamma() + self.T = comp_scales(lamb_max, K, J) + self.w = graph_wavelets(self.lamb_str.real, self.U.real, range(len(self.G.nodes())), self.T) + self.s = graph_low_pass(self.lamb_str.real, self.U.real, len( + self.G.nodes()), self.T, gamma, lamb_max, K) + + def transform(self, F): + """ + """ + return hammond_wavelet_transform(self.w, self.s, self.T, F) + + def inverse(self, wtr): + """ + """ + return hammond_wavelets_inverse(self.w, self.s, wtr) + + def drop_frequency(self, wtr, n): + """ + Keeps only the n top-energy coefficients of wtr. + Input: + * wtr: transform + * n: number of coefficients + Output: + * wtr_copy: changed transform + """ + coeffs = {} + for i in range(len(wtr)): + for j in range(len(wtr[i])): + coeffs[(i, j)] = abs(wtr[i][j]) + + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + + wtr_copy = numpy.copy(wtr) + + for k in range(n, len(sorted_coeffs)): + i = sorted_coeffs[k][0][0] + j = sorted_coeffs[k][0][1] + + wtr_copy[i][j] = 0.0 + + return wtr_copy + class GRCWavelets(object): - """ - Gavish's wavelet transform. - """ - def name(self): - return "GWT" + """ + Gavish's wavelet transform. + """ + + def name(self): + return "GWT" - def set_graph(self, _G): - """ - """ - self.G = _G - (self.tree, self.ind) = ratio_cut_hierarchy(self.G) + def set_graph(self, _G): + """ + """ + self.G = _G + (self.tree, self.ind) = ratio_cut_hierarchy(self.G) - def transform(self, F): - """ - """ - return gavish_wavelet_transform(self.tree, self.ind, self.G, F) + def transform(self, F): + """ + """ + return gavish_wavelet_transform(self.tree, self.ind, self.G, F) - def inverse(self, wtr): - """ - """ - return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) + def inverse(self, wtr): + """ + """ + return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) - def drop_frequency(self, wtr, n): - """ - Keeps only the n top-energy coefficients of wtr. - Input: - * wtr: transform - * n: number of coefficients - Output: - * wtr_copy: changed transform - """ - coeffs = {} - for i in range(len(wtr)): - coeffs[i] = abs(wtr[i]) + def drop_frequency(self, wtr, n): + """ + Keeps only the n top-energy coefficients of wtr. + Input: + * wtr: transform + * n: number of coefficients + Output: + * wtr_copy: changed transform + """ + coeffs = {} + for i in range(len(wtr)): + coeffs[i] = abs(wtr[i]) - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) - wtr_copy = numpy.copy(wtr) + wtr_copy = numpy.copy(wtr) - for k in range(n, len(sorted_coeffs)): - i = sorted_coeffs[k][0] + for k in range(n, len(sorted_coeffs)): + i = sorted_coeffs[k][0] - wtr_copy[i] = 0.0 + wtr_copy[i] = 0.0 - return wtr_copy + return wtr_copy class OptWavelets(object): - """ - Sparse wavelet transform. - """ - def __init__(self, n=0): - """ - """ - self.n = n - - def name(self): - """ - """ - if self.n == 0: - return "SWT" - else: - return "FSWT" - - def set_graph(self, _G): - self.G = _G - - def transform(self, F): - """ - """ - self.F = F - return None - - def inverse(self, wtr): - """ - """ - return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) - - def drop_frequency(self, wtr, n): - coeffs = {} - - k = n - #Computing optimal basis - (self.tree, self.ind, s) = optimal_wavelet_basis(self.G, self.F, k, self.n) - - #Gavish's wavelet transform - tr = gavish_wavelet_transform(self.tree, self.ind, self.G, self.F) - - for i in range(len(tr)): - coeffs[i] = abs(tr[i]) - - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) - - wtr_copy = numpy.copy(tr) - - #Computing number of integers required to represent the edges cut (rounded) - v = n - int(math.ceil(float(s * math.log2(len(self.G.edges()))) / 64)) - - for k in range(v, len(sorted_coeffs)): - i = sorted_coeffs[k][0] - - wtr_copy[i] = 0.0 - - return wtr_copy + """ + Sparse wavelet transform. + """ + + def __init__(self, n=0): + """ + """ + self.n = n + + def name(self): + """ + """ + if self.n == 0: + return "SWT" + else: + return "FSWT" + + def set_graph(self, _G): + self.G = _G + + def transform(self, F): + """ + """ + self.F = F + return None + + def inverse(self, wtr): + """ + """ + return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) + + def drop_frequency(self, wtr, n): + coeffs = {} + + k = n + # Computing optimal basis + (self.tree, self.ind, s) = optimal_wavelet_basis(self.G, self.F, k, self.n) + + # Gavish's wavelet transform + tr = gavish_wavelet_transform(self.tree, self.ind, self.G, self.F) + + for i in range(len(tr)): + coeffs[i] = abs(tr[i]) + + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + + wtr_copy = numpy.copy(tr) + + # Computing number of integers required to represent the edges cut (rounded) + v = n - int(math.ceil(float(s * math.log2(len(self.G.edges()))) / 64)) + + for k in range(v, len(sorted_coeffs)): + i = sorted_coeffs[k][0] + + wtr_copy[i] = 0.0 + return wtr_copy From e9c6ed5a6c4164fd34bb756b85398c239867d10c Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Tue, 29 Aug 2017 09:09:22 +0200 Subject: [PATCH 10/62] Update graph_low_pass and graph_wavelets --- lib/graph_signal_proc.py | 60 +++++++++++++++++++++------------------- lib/static.py | 8 ++++-- 2 files changed, 37 insertions(+), 31 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index e10514a..0a19d78 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -118,29 +118,33 @@ def comp_scales(lamb_max, K, J): def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): """ - Low-pass filter (square matrix). - Input: - * lamb: eigenvalues - * U: eigenvector matrix - * N: number of nodes - * T: wavelet scales - * gamma: scaling function parameter - * lamb_max: upper-bound spectrum - * K: normalization - Output: - * s: Low-pass filter as a #vertices x #vertices matrix + Low-pass spectral filter (square matrix). + See "The emerging field of signal processing on graph" + Input: + * lamb: eigenvalues + * U: eigenvector matrix + * N: number of nodes + * T: wavelet scales + * gamma: scaling function parameter + * lamb_max: upper-bound spectrum + * K: normalization + Output: + * s: Low-pass filter as a N x N matrix """ + + h_vector_form = [h(T[-1] * lamb[x], gamma, lamb_max, K) for x in range(N)] + s = [] for n in range(N): s.append([]) for n in range(N): - for m in range(len(U)): + for m in range(N): s_n_m = 0 - for x in range(len(U)): - s_n_m = s_n_m + U[n][x] * U[m][x] * h(T[-1] * lamb[x], gamma, lamb_max, K) + for x in range(N): + s_n_m = s_n_m + U[n][x] * U[m][x] * h_vector_form[x] s[n].append(s_n_m) @@ -149,28 +153,28 @@ def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): def graph_wavelets(lamb, U, N, T): """ - Graph wavelets. - Input: - * lamb: eigenvalues - * U: eigenvector matrix - * N: number of nodes - * T: wavelet scales - Output: - * w: wavelets as a #vertices x #vertices x #scales matrix + Graph wavelets. + Input: + * lamb: eigenvalues + * U: eigenvector matrix + * N: number of nodes + * T: wavelet scales + Output: + * w: wavelets as a N x N x #scales matrix """ w = [] - for t in range(0, len(T)): + for t in range(len(T)): w.append([]) - for n in range(0, len(N)): + for n in range(N): w[t].append([]) - for t in range(0, len(T)): - for n in range(0, len(N)): - for m in range(0, len(U)): + for t in range(len(T)): + for n in range(N): + for m in range(N): w_t_n_m = 0 - for x in range(0, len(U)): + for x in range(N): w_t_n_m = w_t_n_m + U[n][x] * U[m][x] * g(T[t] * lamb[x]) w[t][n].append(w_t_n_m) diff --git a/lib/static.py b/lib/static.py index b0240dd..00fac99 100644 --- a/lib/static.py +++ b/lib/static.py @@ -90,9 +90,11 @@ def set_graph(self, _G): J = 4 gamma = comp_gamma() self.T = comp_scales(lamb_max, K, J) - self.w = graph_wavelets(self.lamb_str.real, self.U.real, range(len(self.G.nodes())), self.T) - self.s = graph_low_pass(self.lamb_str.real, self.U.real, len( - self.G.nodes()), self.T, gamma, lamb_max, K) + self.w = graph_wavelets(self.lamb_str.real, self.U.real, + len(self.G.nodes()), self.T) + self.s = graph_low_pass(self.lamb_str.real, self.U.real, + len(self.G.nodes()), self.T, gamma, + lamb_max, K) def transform(self, F): """ From 31f96af0cc69015c345bb7a5466a0099d7fef4d9 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Tue, 29 Aug 2017 10:24:17 +0200 Subject: [PATCH 11/62] Fix graph_fourier and graph_fourier_inverse --- lib/graph_signal_proc.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 0a19d78..09afbe8 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -160,7 +160,7 @@ def graph_wavelets(lamb, U, N, T): * N: number of nodes * T: wavelet scales Output: - * w: wavelets as a N x N x #scales matrix + * w: wavelets as a len(T) x N x N matrix """ w = [] @@ -184,28 +184,28 @@ def graph_wavelets(lamb, U, N, T): def graph_fourier(F, U): """ - Graph Fourier transform. - Input: - * F: Graph signal as a #vertices size array, values ordered by G.nodes() - * U: Eigenvectors matrix - Ouput: - * lambdas: Graph Fourier transform + Graph Fourier transform. + Input: + * F: Graph signal as a #vertices size array, values ordered by G.nodes() + * U: Eigenvectors matrix + Ouput: + * F_hat: Graph Fourier transform """ - lambdas = [] + F_hat = [] for i in range(0, len(U)): - lambdas.append(np.dot(F, U[:, i])) + F_hat.append(np.dot(F, U[:, i])) - lambdas = np.array(lambdas) + F_hat = np.array(F_hat) - return lambdas + return F_hat -def graph_fourier_inverse(GF, U): +def graph_fourier_inverse(F_hat, U): """ Graph Fourier inverse: Input: - * GF: Graph fourier transform + * F_hat: Graph fourier transform * U: Eigenvectors matrix Output: * F: Inverse @@ -213,7 +213,7 @@ def graph_fourier_inverse(GF, U): F = np.zeros(U.shape[0]) for v in range(U.shape[0]): for u in range(U.shape[1]): - F[v] = F[v] + (GF[u] * U[v][u]).real + F[v] = F[v] + (F_hat[u] * U[v][u]).real return F From 4a07252a91b1be33dcbc2d2d4ac7f950b451e64c Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Wed, 30 Aug 2017 12:10:37 +0200 Subject: [PATCH 12/62] Add comment to Hammond's transform and inverse --- lib/graph_signal_proc.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 09afbe8..b7785c4 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -219,11 +219,11 @@ def graph_fourier_inverse(F_hat, U): def hammond_wavelet_transform(w, s, T, F): - """ + r""" Hammond wavelet transform. Input: * w: wavelets - * s: low-pass wavelets + * s: low-pass wavelet (scaling function) * T: wavelet scales * F: graph signal Output: @@ -233,11 +233,13 @@ def hammond_wavelet_transform(w, s, T, F): for i in range(len(T)): C.append([]) + # Each wavelet is represented by an N x N matrix for j in range(len(F)): dotp = np.dot(F, w[i][j]) C[i].append(dotp) C.append([]) + # Append output of scaling function application at the end for j in range(len(F)): dotp = np.dot(F, s[j]) C[-1].append(dotp) @@ -246,18 +248,18 @@ def hammond_wavelet_transform(w, s, T, F): def hammond_wavelets_inverse(w, s, C): - """ + r""" Hammond's wavelet inverse. Input: * w: wavelets - * s: low-pass wavelets + * s: low-pass wavelet (scaling function) * C: transform Output: * F: inverse """ w = np.array(w) Wc = np.append(w, np.array([s]), axis=0) - + # Creates copies of the inputs nWc = Wc[0, :, :] nC = C[0] for i in range(1, Wc.shape[0]): @@ -266,7 +268,8 @@ def hammond_wavelets_inverse(w, s, C): nWc = np.array(nWc) nC = np.array(nC) - + # Search a least square solution F, solving: + # nWc F = nC F = np.linalg.lstsq(nWc, nC)[0] return F From dcbd2428311f9b5620ef159ebf2f143383d2d35a Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Wed, 30 Aug 2017 20:10:08 +0200 Subject: [PATCH 13/62] Update comments in partitions_level_rec --- lib/graph_signal_proc.py | 72 +++++++++++++++++++++------------------- 1 file changed, 37 insertions(+), 35 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index b7785c4..987d488 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -277,14 +277,14 @@ def hammond_wavelets_inverse(w, s, C): class Node(object): """ - Generic tree-structure used for hierarchical transforms. + Generic tree-structure used for hierarchical transforms. """ def __init__(self, data): """ - Initialization. - Input: - * data: Anything to be stored in a node + Initialization. + Input: + * data: Anything to be stored in a node """ self.data = data self.children = [] @@ -304,9 +304,9 @@ def __init__(self, data): def add_child(self, obj): """ - Adds obj as a child to a node. - Input: - * obj: anything + Adds obj as a child to a node. + Input: + * obj: anything """ obj.scale = self.scale + 1 self.children.append(obj) @@ -315,13 +315,13 @@ def add_child(self, obj): def get_children(tree, part, G): """ - Recursively gets all the children of a given node. - Input: - * tree: tree node - * part: list that will contain children - * G: graph - Output: - * None + Recursively gets all the children of a given node. + Input: + * tree: tree node + * part: list that will contain children + * G: graph + Output: + * None """ if tree.data is not None: part.append(G.nodes()[tree.data]) @@ -332,11 +332,11 @@ def get_children(tree, part, G): def set_counts(tree): """ - Sets counts for intermediate nodes in the tree. - Input: - * tree: tree node - Output: - * count: count for the tree node + Sets counts for intermediate nodes in the tree. + Input: + * tree: tree node + Output: + * count: count for the tree node """ if tree.data is not None: tree.count = 1 @@ -353,16 +353,18 @@ def set_counts(tree): def partitions_level_rec(tree, level, G, l, partitions): """ - Recursively extracts partitions up to a certain level in the tree. - Input: - * tree: tree - * level: max level - * G: graph - * l: current level - * partitions: partitions recovered - Output: - None + Recursively extracts partitions from the current level + up to a certain level in the tree. + Input: + * tree: tree + * level: max level + * G: graph + * l: current level + * partitions: partitions recovered + Output: + None """ + # Stopping condition if l >= level: part = [] get_children(tree, part, G) @@ -378,13 +380,13 @@ def partitions_level_rec(tree, level, G, l, partitions): def partitions_level(tree, level, G): """ - Recovers partitions at a certain level of the three. - Input: - * tree: tree - * level: level - * G: graph - Output: - * partitions: set of vertices in each partition + Recovers partitions up to a certain level of the three. + Input: + * tree: tree + * level: level + * G: graph + Output: + * partitions: set of vertices in each partition """ partitions = [] partitions_level_rec(tree, level, G, 0, partitions) From 5f2b1e91449e22ffe60279d87e17d2a7567c04bd Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Wed, 30 Aug 2017 22:48:44 +0200 Subject: [PATCH 14/62] Remove create_linked_list --- lib/graph_signal_proc.py | 131 +++++++++++++++++---------------------- 1 file changed, 57 insertions(+), 74 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 987d488..a0f5cc6 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -10,7 +10,6 @@ import scipy.optimize from scipy import linalg import scipy.fftpack - from sklearn.preprocessing import normalize @@ -186,7 +185,7 @@ def graph_fourier(F, U): """ Graph Fourier transform. Input: - * F: Graph signal as a #vertices size array, values ordered by G.nodes() + * F: Graph signal with values ordered by G.nodes() * U: Eigenvectors matrix Ouput: * F_hat: Graph Fourier transform @@ -396,12 +395,12 @@ def partitions_level(tree, level, G): def build_matrix(G, ind): """ - Builds graph distance matrix. - Input: - * G: graph - * ind: dictionary vertex: unique integer - Output: - * M: matrix + Builds graph distance matrix. + Input: + * G: graph + * ind: dictionary vertex: unique integer + Output: + * M: matrix """ M = [] dists = nx.all_pairs_dijkstra_path_length(G) @@ -417,12 +416,12 @@ def build_matrix(G, ind): def select_centroids(M, radius): """ - Selects half of the vertices as centroids. - Input: - * M - * radius - Output: - * centroids + Selects half of the vertices as centroids. + Input: + * M + * radius + Output: + * centroids """ nodes = list(range(M.shape[0])) random.shuffle(nodes) @@ -495,15 +494,15 @@ def coarse_matrix(M, H, cents, nodes): def get_partitions(x, node_list): """ - Gets partitions given indicator vector. - if x < 0: partition 1 - if x <= 0: partition 2 - Input: - * node_list: list of nodes - * x: indicator vector - Output: - * P1: partition 1 - * P2: partition 2 + Gets partitions given indicator vector. + if x < 0: partition 1 + if x => 0: partition 2 + Input: + * x: indicator vector + * node_list: list of nodes + Output: + * P1: partition 1 + * P2: partition 2 """ P1 = [] P2 = [] @@ -588,11 +587,11 @@ def laplacian_complete(n): def sqrtm(mat): """ - Matrix square root. - Input: - * mat: matrix - Output: - * matrix square root + Matrix square root. + Input: + * mat: matrix + Output: + * matrix square root """ eigvals, eigvecs = eigh(mat) @@ -604,11 +603,11 @@ def sqrtm(mat): def sqrtmi(mat): """ - Computes the square-root inverse of a matrix. - Input: - * mat: matrix - Output: - * square root inverse + Computes the square-root inverse of a matrix. + Input: + * mat: matrix + Output: + * square root inverse """ eigvals, eigvecs = eigh(mat) eigvecs = eigvecs[:, eigvals > 0] @@ -617,32 +616,15 @@ def sqrtmi(mat): return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) -def create_linked_list(L): - """ - Creates linked list from a Laplacian matrix. - Input: - * L: matrix - Output: - * linked_list: linked list - """ - linked_list = {} - - for i in L.nonzero()[0]: - linked_list[i] = [] - for j in range(L.shape[1]): - if L[i, j] < 0: - linked_list[i].append(j) - return linked_list - - def sweep(x, G): """ - Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian) based on vector x. - Input: - * x: vector - * G: graph - Output: - * vec: indicator vector + Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian). + Based on vector x. + Input: + * x: vector + * G: graph + Output: + * vec: indicator vector """ best_val = nx.number_of_nodes(G) - 1 sorted_x = np.argsort(x) @@ -687,12 +669,12 @@ def sweep(x, G): def separate_lcc(G, G0): """ - Separates vertices in G0 (LCC) from the rest in G using indicator vector. - Input: - * G: Graph - * G0: Subgraph - Output: - * x: indicator vector + Separates vertices in G0 (LCC) from the rest in G using indicator vector. + Input: + * G: Graph + * G0: Subgraph + Output: + * x: indicator vector """ x = [] @@ -707,11 +689,11 @@ def separate_lcc(G, G0): def ratio_cut(G): """ - Computes ratio-cut of G based on second eigenvector of the Laplacian. - Input: - * G: Graph - Output: - * x: Indicator vector + Computes ratio-cut of G based on second eigenvector of the Laplacian. + Input: + * G: Graph + Output: + * x: Indicator vector """ Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True) @@ -730,12 +712,13 @@ def ratio_cut(G): def eig_vis_rc(G): """ - Second and third eigenvectors of the graph Laplacian. For visualization. - Input: - * G: Graph - Output: - * x1: Second eigenvector - * x2: Third eigenvector + Uses the second and third eigenvectors of the graph + Laplacian for visualization. + Input: + * G: Graph + Output: + * x1: Second eigenvector + * x2: Third eigenvector """ L = nx.laplacian_matrix(G).todense() (eigvals, eigvecs) = scipy.linalg.eigh(L, eigvals=(1, 2)) From 08b5d981d2bde83dd270cd1a63d6060f0e5838c8 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sun, 3 Sep 2017 18:46:48 +0200 Subject: [PATCH 15/62] Clean the sweep function --- lib/graph_signal_proc.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index a0f5cc6..7c2784d 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -647,14 +647,9 @@ def sweep(x, G): if den > 0: val = float(edges_cut) / den - else: - val = nx.number_of_nodes(G) - - if val <= best_val: - best_cand = i - best_val = val - - vec = [] + if val <= best_val: + best_cand = i + best_val = val vec = np.zeros(nx.number_of_nodes(G)) @@ -669,7 +664,8 @@ def sweep(x, G): def separate_lcc(G, G0): """ - Separates vertices in G0 (LCC) from the rest in G using indicator vector. + Separate vertices in G0 (LCC) from the rest in G returning + an indicator vector. Input: * G: Graph * G0: Subgraph From c748441327a7aa59a07d2f989da2ddfa0d089bf6 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 09:35:03 +0200 Subject: [PATCH 16/62] Remove arg in graph_low_pass --- lib/graph_signal_proc.py | 128 ++++++++++++++++++++------------------- lib/static.py | 3 +- 2 files changed, 66 insertions(+), 65 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 7c2784d..f8b4d2e 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -98,15 +98,15 @@ def h(x, gamma, lamb_max, K): def comp_scales(lamb_max, K, J): - """ + r""" Computes wavelet scales see Hammond, D. K.,Vandergheynst, P., & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory". Input: - * lamb_max: upper bound spectrum - * K: normalization - * J: number of scales + * lamb_max: upper bound spectrum + * K: desired ratio for lambda_max / lambda_min + * J: number of scales Output: - * scales array + * scales array """ lamb_min = float(lamb_max) / K s_min = float(1) / lamb_max @@ -115,7 +115,7 @@ def comp_scales(lamb_max, K, J): return np.exp(np.linspace(math.log(s_max), math.log(s_min), J)) -def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): +def graph_low_pass(lamb, U, T, gamma, lamb_max, K): """ Low-pass spectral filter (square matrix). See "The emerging field of signal processing on graph" @@ -131,6 +131,8 @@ def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K): * s: Low-pass filter as a N x N matrix """ + N = len(lamb) + h_vector_form = [h(T[-1] * lamb[x], gamma, lamb_max, K) for x in range(N)] s = [] @@ -727,13 +729,13 @@ def eig_vis_rc(G): def get_subgraphs(G, cut): """ - Compute subgraphs generated by a cut. - Input: - * G: Original graph - * cut: cut indicator vector - Output: - * G1: subgraph 1 - * G2: subgraph 2 + Compute subgraphs generated by a cut. + Input: + * G: Original graph + * cut: cut indicator vector + Output: + * G1: subgraph 1 + * G2: subgraph 2 """ G1 = nx.Graph() G2 = nx.Graph() @@ -756,13 +758,13 @@ def get_subgraphs(G, cut): def rc_recursive(node, G, ind): """ - Recursively computes ratio-cut. - Input: - * node: tree node - * G: graph - * ind: vertex index v: unique integer - Output: - * none + Recursively computes ratio-cut. + Input: + * node: tree node + * G: graph + * ind: vertex index v: unique integer + Output: + * none """ if nx.number_of_nodes(G) < 3: n = Node(None) @@ -793,12 +795,12 @@ def rc_recursive(node, G, ind): def ratio_cut_hierarchy(G): """ - Computes ratio-cut hierarchy for a graph. - Input: - * G: graph - Output: - * root: tree root - * ind: graph index v: unique integer + Computes ratio-cut hierarchy for a graph. + Input: + * G: graph + Output: + * root: tree root + * ind: graph index v: unique integer """ i = 0 ind = {} @@ -847,12 +849,12 @@ def gavish_hierarchy(G, radius): def compute_coefficients(tree, F): """ - Computes tree coefficients for Gavish's transform. - Input: - * tree: tree - * F: graph signal - Output: - * None + Computes tree coefficients for Gavish's transform. + Input: + * tree: tree + * F: graph signal + Output: + * None """ if tree.data is None: avg = 0 @@ -904,11 +906,11 @@ def reconstruct_values(tree, F): def clear_tree(tree): """ - Clears tree info. - Input: - * tree - Output: - * None + Clears tree info. + Input: + * tree + Output: + * None """ tree.avg = 0 tree.diffs = [] @@ -921,12 +923,12 @@ def clear_tree(tree): def get_coefficients(tree, wtr): """ - Recovers wavelet coefficients from the wavelet tree. - Input: - * tree - * wtr: wavelet coefficients - Output: - * None + Recovers wavelet coefficients from the wavelet tree. + Input: + * tree + * wtr: wavelet coefficients + Output: + * None """ Q = deque() scales = [] @@ -970,10 +972,10 @@ def get_cut_sizes(tree): def set_coefficients(tree, wtr): """ - Sets wavelet tree coefficients. - Input: - * tree - * wtr: wavelet coefficients + Sets wavelet tree coefficients. + Input: + * tree + * wtr: wavelet coefficients """ Q = deque() tree.avg = float(wtr[0]) / tree.count @@ -993,14 +995,14 @@ def set_coefficients(tree, wtr): def gavish_wavelet_transform(tree, ind, G, F): """ - Gavish's wavelet transform. - Input: - * tree - * ind: vertex index v : unique integer - * G: graph - * F: graph signal - Output: - * wtr: wavelet transform. + Gavish's wavelet transform. + Input: + * tree + * ind: vertex index v : unique integer + * G: graph + * F: graph signal + Output: + * wtr: wavelet transform. """ wtr = [] clear_tree(tree) @@ -1012,14 +1014,14 @@ def gavish_wavelet_transform(tree, ind, G, F): def gavish_wavelet_inverse(tree, ind, G, wtr): """ - Gavish's wavelet inverse. - Input: - * tree - * ind: vertex index v: unique integer - * G: graph - * wtr: wavelet transform - Output: - * F: wavelet inverse + Gavish's wavelet inverse. + Input: + * tree + * ind: vertex index v: unique integer + * G: graph + * wtr: wavelet transform + Output: + * F: wavelet inverse """ F = [] diff --git a/lib/static.py b/lib/static.py index 00fac99..8a387ad 100644 --- a/lib/static.py +++ b/lib/static.py @@ -93,8 +93,7 @@ def set_graph(self, _G): self.w = graph_wavelets(self.lamb_str.real, self.U.real, len(self.G.nodes()), self.T) self.s = graph_low_pass(self.lamb_str.real, self.U.real, - len(self.G.nodes()), self.T, gamma, - lamb_max, K) + self.T, gamma, lamb_max, K) def transform(self, F): """ From 9d0b9bf356c0a762dbaae77367635cf2f7ef8306 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 09:49:14 +0200 Subject: [PATCH 17/62] Remove for loop in graph_low_pass --- lib/graph_signal_proc.py | 1 - 1 file changed, 1 deletion(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index f8b4d2e..b1bc72a 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -140,7 +140,6 @@ def graph_low_pass(lamb, U, T, gamma, lamb_max, K): for n in range(N): s.append([]) - for n in range(N): for m in range(N): s_n_m = 0 From d5eec504b020e84567e4e61dc0be7f1ef45643e7 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 09:55:34 +0200 Subject: [PATCH 18/62] Remove for loop in graph_wavelets --- lib/graph_signal_proc.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index b1bc72a..c49809c 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -168,9 +168,6 @@ def graph_wavelets(lamb, U, N, T): w.append([]) for n in range(N): w[t].append([]) - - for t in range(len(T)): - for n in range(N): for m in range(N): w_t_n_m = 0 From 1f91edbac897f06c4dc9d32939420ff36ea1e2f9 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 10:06:19 +0200 Subject: [PATCH 19/62] Fix doc in lib/graph_signal_proc.py --- lib/graph_signal_proc.py | 47 ++++++++++++++++++++-------------------- 1 file changed, 23 insertions(+), 24 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index c49809c..63dd9c1 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -170,7 +170,6 @@ def graph_wavelets(lamb, U, N, T): w[t].append([]) for m in range(N): w_t_n_m = 0 - for x in range(N): w_t_n_m = w_t_n_m + U[n][x] * U[m][x] * g(T[t] * lamb[x]) @@ -183,10 +182,10 @@ def graph_fourier(F, U): """ Graph Fourier transform. Input: - * F: Graph signal with values ordered by G.nodes() + * F: Signal in the vertex domain * U: Eigenvectors matrix Ouput: - * F_hat: Graph Fourier transform + * F_hat: Signal in the graph spectral domain """ F_hat = [] @@ -200,12 +199,12 @@ def graph_fourier(F, U): def graph_fourier_inverse(F_hat, U): """ - Graph Fourier inverse: - Input: - * F_hat: Graph fourier transform - * U: Eigenvectors matrix - Output: - * F: Inverse + Graph Fourier inverse: + Input: + * F_hat: Signal in the graph spectral domain + * U: Eigenvectors matrix + Output: + * F: Signal in the vertex domain """ F = np.zeros(U.shape[0]) for v in range(U.shape[0]): @@ -217,14 +216,14 @@ def graph_fourier_inverse(F_hat, U): def hammond_wavelet_transform(w, s, T, F): r""" - Hammond wavelet transform. - Input: - * w: wavelets - * s: low-pass wavelet (scaling function) - * T: wavelet scales - * F: graph signal - Output: - * C: Hammond's wavelet transform + Hammond wavelet transform. + Input: + * w: wavelets + * s: low-pass wavelet (scaling function) + * T: wavelet scales + * F: graph signal + Output: + * C: Hammond's wavelet transform """ C = [] @@ -246,13 +245,13 @@ def hammond_wavelet_transform(w, s, T, F): def hammond_wavelets_inverse(w, s, C): r""" - Hammond's wavelet inverse. - Input: - * w: wavelets - * s: low-pass wavelet (scaling function) - * C: transform - Output: - * F: inverse + Hammond's wavelet inverse. + Input: + * w: wavelets + * s: low-pass wavelet (scaling function) + * C: transform + Output: + * F: inverse """ w = np.array(w) Wc = np.append(w, np.array([s]), axis=0) From 2ec310c60a1c3102941e56565657a00f167b86ee Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 10:34:36 +0200 Subject: [PATCH 20/62] Remove attributes from Node object --- lib/graph_signal_proc.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 63dd9c1..6ac5df5 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -223,7 +223,8 @@ def hammond_wavelet_transform(w, s, T, F): * T: wavelet scales * F: graph signal Output: - * C: Hammond's wavelet transform + * C: Hammond's wavelet transform. (len(T) + 1) x len(F) + matrix of transform coefficients """ C = [] @@ -249,9 +250,10 @@ def hammond_wavelets_inverse(w, s, C): Input: * w: wavelets * s: low-pass wavelet (scaling function) - * C: transform + * C: Hammond's wavelet transform. (len(T) + 1) x len(F) + matrix of transform coefficients Output: - * F: inverse + * F: Reconstructed signal in the vertex domain """ w = np.array(w) Wc = np.append(w, np.array([s]), axis=0) @@ -280,7 +282,8 @@ def __init__(self, data): """ Initialization. Input: - * data: Anything to be stored in a node + * data: Anything to be stored in a node. Usually + only leaf nodes have data != None. """ self.data = data self.children = [] @@ -288,9 +291,6 @@ def __init__(self, data): self.counts = [] self.diffs = [] self.scale = 0 - self.ftr = [] - self.L = [] - self.U = [] self.cut = 0 if data is None: From e7d271da3c5df973d7cc54060bd7350b95651635 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 12:55:00 +0200 Subject: [PATCH 21/62] Clean code in graph_signal_proc.py --- lib/graph_signal_proc.py | 41 +++++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 19 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 6ac5df5..03667d0 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -280,19 +280,20 @@ class Node(object): def __init__(self, data): """ - Initialization. Input: - * data: Anything to be stored in a node. Usually - only leaf nodes have data != None. + * data: Anything to be stored in a node. + Usually only leaf nodes have data != None + data != None often used as stopping condition """ self.data = data self.children = [] self.avgs = [] self.counts = [] self.diffs = [] + # Level on the tree. The root has scale = 0 self.scale = 0 self.cut = 0 - + # count: number of leaves (data != None) in the subtree if data is None: self.count = 0 else: @@ -309,9 +310,9 @@ def add_child(self, obj): self.count = self.count + obj.count -def get_children(tree, part, G): +def get_leaves(tree, part, G): """ - Recursively gets all the children of a given node. + Recursively gets all the leaves of a given tree. Input: * tree: tree node * part: list that will contain children @@ -320,10 +321,10 @@ def get_children(tree, part, G): * None """ if tree.data is not None: - part.append(G.nodes()[tree.data]) + part.append(tree.data) else: for c in tree.children: - get_children(c, part, G) + get_leaves(c, part, G) def set_counts(tree): @@ -349,21 +350,21 @@ def set_counts(tree): def partitions_level_rec(tree, level, G, l, partitions): """ - Recursively extracts partitions from the current level - up to a certain level in the tree. + Recursively extracts partitions from the current level "l" + up to a certain level "level" in the tree. Input: * tree: tree * level: max level * G: graph * l: current level - * partitions: partitions recovered + * partitions: list of partitions recovered Output: None """ # Stopping condition if l >= level: part = [] - get_children(tree, part, G) + get_leaves(tree, part, G) if len(part) > 0: partitions.append(part) else: @@ -390,9 +391,9 @@ def partitions_level(tree, level, G): return partitions -def build_matrix(G, ind): +def distance_matrix(G, ind): """ - Builds graph distance matrix. + Builds graph distance matrix according to an index Input: * G: graph * ind: dictionary vertex: unique integer @@ -426,14 +427,16 @@ def select_centroids(M, radius): cents = [nodes[0]] mn = sys.float_info.min - for i in range(1, len(nodes)): + for candidate_cent in nodes[1:]: add = True - for j in range(len(cents)): - if M[cents[j]][nodes[i]] <= radius * mn: + for cent in cents: + # If the candidate centroid is too close to a centroid + # it is not added to the list of centroids + if M[cent][candidate_cent] <= radius * mn: add = False break if add: - cents.append(nodes[i]) + cents.append(candidate_cent) return cents @@ -830,7 +833,7 @@ def gavish_hierarchy(G, radius): H.append(i) i = i + 1 - M = build_matrix(G, ind) + M = distance_matrix(G, ind) while M.shape[0] > 1: cents = select_centroids(M, radius) From 8ee07a837428085379fa4b76cc64b7bdb603cf27 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 14:46:01 +0200 Subject: [PATCH 22/62] Fix imports and style in lib/syn.py --- lib/syn.py | 481 +++++++++++++++++++++++++++-------------------------- 1 file changed, 241 insertions(+), 240 deletions(-) diff --git a/lib/syn.py b/lib/syn.py index f5b01f0..0d0f8fc 100644 --- a/lib/syn.py +++ b/lib/syn.py @@ -1,262 +1,263 @@ -import networkx +import random import math -import scipy.optimize -import numpy -import sys + +import networkx as nx +import numpy as np from scipy import linalg -import matplotlib.pyplot as plt -from IPython.display import Image -import pywt -import scipy.fftpack -import random -import operator -import copy -from collections import deque -from sklearn.preprocessing import normalize -from sklearn.cluster import SpectralClustering -import random def synthetic_graph(size, num_edges, sparsity, energy, balance, noise): - size_part_a = int(math.ceil(float(size * balance) / 2)) - size_part_b = size - size_part_a - F = [] - edges = {} - - avg_a = float(numpy.sqrt(float(energy * size) / (size_part_a * size_part_b))) / 2. - - avg_b = -float(numpy.sqrt(float(energy * size) / (size_part_a * size_part_b))) / 2. - - for v in range(size): - if v < size_part_a: - F.append(random.gauss(avg_a, noise*avg_a)) - else: - F.append(random.gauss(avg_b, noise*avg_a)) - - G = networkx.Graph() - - for v in range(size-1): - G.add_edge(v,v+1) - edges[(v,v+1)] = True - - remaining_edges = num_edges - len(G.edges()) - edges_accross = int((size_part_a * size_part_b * (1.-sparsity) * remaining_edges) / (size * (size-1))) - edges_within = remaining_edges - edges_accross - - for e in range(edges_accross): - v1 = random.randint(0, size_part_a-1) - v2 = random.randint(size_part_a, size-1) - - while (v1,v2) in edges or v1 == v2: - v1 = random.randint(0,size_part_a-1) - v2 = random.randint(size_part_a, size_part_a+size_part_b-1) - - G.add_edge(v1,v2) - edges[(v1,v2)] = True - - for e in range(edges_within): - v1 = random.randint(0,size-1) - v2 = random.randint(0,size-1) - - if v1 > v2: - tmp = v1 - v1 = v2 - v2 = tmp - - while (v1,v2) in edges or v1 == v2 or (v1 < size_part_a and v2 >= size_part_a) or (v1 >= size_part_a and v2 < size_part_a): - v1 = random.randint(0,size-1) - v2 = random.randint(0,size-1) - - if v1 > v2: - tmp = v1 - v1 = v2 - v2 = tmp - - G.add_edge(v1,v2) - edges[(v1,v2)] = True - - return G, numpy.array(F), edges_accross+1 + size_part_a = int(math.ceil(float(size * balance) / 2)) + size_part_b = size - size_part_a + F = [] + edges = {} + + avg_a = float(np.sqrt(float(energy * size) / + (size_part_a * size_part_b))) / 2. + + avg_b = -float(np.sqrt(float(energy * size) / + (size_part_a * size_part_b))) / 2. + + for v in range(size): + if v < size_part_a: + F.append(random.gauss(avg_a, noise * avg_a)) + else: + F.append(random.gauss(avg_b, noise * avg_a)) + + G = nx.Graph() + + for v in range(size - 1): + G.add_edge(v, v + 1) + edges[(v, v + 1)] = True + + remaining_edges = num_edges - len(G.edges()) + edges_accross = int((size_part_a * size_part_b * (1. - sparsity) + * remaining_edges) / (size * (size - 1))) + edges_within = remaining_edges - edges_accross + + for e in range(edges_accross): + v1 = random.randint(0, size_part_a - 1) + v2 = random.randint(size_part_a, size - 1) + + while (v1, v2) in edges or v1 == v2: + v1 = random.randint(0, size_part_a - 1) + v2 = random.randint(size_part_a, size_part_a + size_part_b - 1) + + G.add_edge(v1, v2) + edges[(v1, v2)] = True + + for e in range(edges_within): + v1 = random.randint(0, size - 1) + v2 = random.randint(0, size - 1) + + if v1 > v2: + tmp = v1 + v1 = v2 + v2 = tmp + + while ((v1, v2) in edges or v1 == v2 or + (v1 < size_part_a and v2 >= size_part_a) or + (v1 >= size_part_a and v2 < size_part_a)): + v1 = random.randint(0, size - 1) + v2 = random.randint(0, size - 1) + + if v1 > v2: + tmp = v1 + v1 = v2 + v2 = tmp + + G.add_edge(v1, v2) + edges[(v1, v2)] = True + + return G, np.array(F), edges_accross + 1 + def compute_distances(center, graph): - distances = networkx.shortest_path_length(graph, center) - - return distances + distances = nx.shortest_path_length(graph, center) + + return distances + def compute_embedding(distances, radius, graph): - B = [] - s = 0 - nodes = {} - for v in graph.nodes(): - if distances[v] <= radius: - B.append(1) - s = s + 1 - else: - B.append(0) - - return numpy.array(B) + B = [] + s = 0 + for v in graph.nodes(): + if distances[v] <= radius: + B.append(1) + s = s + 1 + else: + B.append(0) + + return np.array(B) + def generate_dyn_cascade(G, diam, duration, n): - Fs = [] - - for j in range(n): - v = random.randint(0, len(G.nodes())-1) - distances = compute_distances(G.nodes()[v], G) - - if diam > duration: - num_snaps = diam - else: - num_snaps = duration - - for i in range(num_snaps): - r = int(i * math.ceil(float(diam)/duration)) - - F = compute_embedding(distances, r, G) - Fs.append(F) - - return numpy.array(Fs) + Fs = [] + + for j in range(n): + v = random.randint(0, len(G.nodes()) - 1) + distances = compute_distances(G.nodes()[v], G) + + if diam > duration: + num_snaps = diam + else: + num_snaps = duration + + for i in range(num_snaps): + r = int(i * math.ceil(float(diam) / duration)) + + F = compute_embedding(distances, r, G) + Fs.append(F) + + return np.array(Fs) + def generate_dyn_heat(G, s, jump, n): - Fs = [] - L = networkx.normalized_laplacian_matrix(G) - L = L.todense() - F0s = [] - seeds = [] - - for i in range(s): - F0 = numpy.zeros(len(G.nodes())) - v = random.randint(0, len(G.nodes())-1) - seeds.append(v) - F0[v] = len(G.nodes()) - F0s.append(F0) - - Fs.append(numpy.sum(F0s, axis=0)) - - for j in range(n): - FIs = [] - for i in range(s): - FI = numpy.multiply(linalg.expm(-j*jump*L), F0s[i])[:,seeds[i]] - FIs.append(FI) - - Fs.append(numpy.sum(FIs, axis=0)) - - return numpy.array(Fs)[1:] + Fs = [] + L = nx.normalized_laplacian_matrix(G) + L = L.todense() + F0s = [] + seeds = [] + + for i in range(s): + F0 = np.zeros(len(G.nodes())) + v = random.randint(0, len(G.nodes()) - 1) + seeds.append(v) + F0[v] = len(G.nodes()) + F0s.append(F0) + + Fs.append(np.sum(F0s, axis=0)) + + for j in range(n): + FIs = [] + for i in range(s): + FI = np.multiply(linalg.expm(-j * jump * L), F0s[i])[:, seeds[i]] + FIs.append(FI) + + Fs.append(np.sum(FIs, axis=0)) + + return np.array(Fs)[1:] + def generate_dyn_gaussian_noise(G, n): - Fs = [] - - for j in range(n): - F = numpy.random.rand(len(G.nodes())) - Fs.append(F) + Fs = [] + + for j in range(n): + F = np.random.rand(len(G.nodes())) + Fs.append(F) + + return np.array(Fs) - return numpy.array(Fs) def generate_dyn_bursty_noise(G, n): - Fs = [] - bursty_beta = 1 - non_bursty_beta = 1000 - bursty_bursty = 0.7 - non_bursty_non_bursty = 0.9 - bursty = False - - for j in range(n): - r = random.random() - - if not bursty: - if r > non_bursty_non_bursty: - bursty = True - else: - if r > bursty_bursty: - bursty = False - - if bursty: - F = numpy.random.exponential(bursty_beta, len(G.nodes())) - else: - F = numpy.random.exponential(non_bursty_beta, len(G.nodes())) - - Fs.append(F) - - return numpy.array(Fs) + Fs = [] + bursty_beta = 1 + non_bursty_beta = 1000 + bursty_bursty = 0.7 + non_bursty_non_bursty = 0.9 + bursty = False + + for j in range(n): + r = random.random() + + if not bursty: + if r > non_bursty_non_bursty: + bursty = True + else: + if r > bursty_bursty: + bursty = False + + if bursty: + F = np.random.exponential(bursty_beta, len(G.nodes())) + else: + F = np.random.exponential(non_bursty_beta, len(G.nodes())) + + Fs.append(F) + + return np.array(Fs) + def generate_dyn_indep_cascade(G, s, p): - Fs = [] - - seeds = numpy.random.choice(len(G.nodes()), s, replace=False) - - F0 = numpy.zeros(len(G.nodes())) - - ind = {} - i = 0 - - for v in G.nodes(): - ind[v] = i - i = i + 1 - - for s in seeds: - F0[s] = 2.0 - - while True: - F1 = numpy.zeros(len(G.nodes())) - new_inf = 0 - for v in G.nodes(): - if F0[ind[v]] > 1.0: - for u in G.neighbors(v): - r = random.random() - if r <= p and F0[ind[u]] < 1.0: - F1[ind[u]] = 2.0 - new_inf = new_inf + 1 - F1[ind[v]] = 1.0 - F0[ind[v]] = 1.0 - elif F0[ind[v]] > 0.0: - F1[ind[v]] = 1.0 - - Fs.append(F0) - - if new_inf == 0 and len(Fs) > 1: - break - - F0 = numpy.copy(F1) - - return numpy.array(Fs) + Fs = [] + + seeds = np.random.choice(len(G.nodes()), s, replace=False) + + F0 = np.zeros(len(G.nodes())) + + ind = {} + i = 0 + + for v in G.nodes(): + ind[v] = i + i = i + 1 + + for s in seeds: + F0[s] = 2.0 + + while True: + F1 = np.zeros(len(G.nodes())) + new_inf = 0 + for v in G.nodes(): + if F0[ind[v]] > 1.0: + for u in G.neighbors(v): + r = random.random() + if r <= p and F0[ind[u]] < 1.0: + F1[ind[u]] = 2.0 + new_inf = new_inf + 1 + F1[ind[v]] = 1.0 + F0[ind[v]] = 1.0 + elif F0[ind[v]] > 0.0: + F1[ind[v]] = 1.0 + + Fs.append(F0) + + if new_inf == 0 and len(Fs) > 1: + break + + F0 = np.copy(F1) + + return np.array(Fs) + def generate_dyn_linear_threshold(G, s): - Fs = [] - - seeds = numpy.random.choice(len(G.nodes()), s, replace=False) - - F0 = numpy.zeros(len(G.nodes())) - thresholds = numpy.random.uniform(0.0,1.0,len(G.nodes())) - - ind = {} - i = 0 - - for v in G.nodes(): - ind[v] = i - i = i + 1 - - for s in seeds: - F0[s] = 1.0 - - while True: - F1 = numpy.zeros(len(G.nodes())) - new_inf = 0 - for v in G.nodes(): - if F0[ind[v]] < 1.0: - n = 0 - for u in G.neighbors(v): - if F0[ind[u]] > 0: - n = n + 1 - - if (float(n) / len(G.neighbors(v))) >= thresholds[ind[v]]: - F1[ind[v]] = 1.0 - new_inf = new_inf + 1 - else: - F1[ind[v]] = 1.0 - - Fs.append(F0) - - if new_inf == 0 and len(Fs) > 1: - break - - F0 = numpy.copy(F1) - - return numpy.array(Fs) + Fs = [] + + seeds = np.random.choice(len(G.nodes()), s, replace=False) + + F0 = np.zeros(len(G.nodes())) + thresholds = np.random.uniform(0.0, 1.0, len(G.nodes())) + + ind = {} + i = 0 + + for v in G.nodes(): + ind[v] = i + i = i + 1 + + for s in seeds: + F0[s] = 1.0 + + while True: + F1 = np.zeros(len(G.nodes())) + new_inf = 0 + for v in G.nodes(): + if F0[ind[v]] < 1.0: + n = 0 + for u in G.neighbors(v): + if F0[ind[u]] > 0: + n = n + 1 + + if (float(n) / len(G.neighbors(v))) >= thresholds[ind[v]]: + F1[ind[v]] = 1.0 + new_inf = new_inf + 1 + else: + F1[ind[v]] = 1.0 + + Fs.append(F0) + + if new_inf == 0 and len(Fs) > 1: + break + + F0 = np.copy(F1) + + return np.array(Fs) From 78435e82c687f06ede40639e6b9b83f36c01aae5 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 15:25:37 +0200 Subject: [PATCH 23/62] Add seeds as args for functions in syn.py --- lib/syn.py | 39 +++++++++++++++++++++++++++++++-------- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/lib/syn.py b/lib/syn.py index 0d0f8fc..6823197 100644 --- a/lib/syn.py +++ b/lib/syn.py @@ -6,7 +6,11 @@ from scipy import linalg -def synthetic_graph(size, num_edges, sparsity, energy, balance, noise): +def synthetic_graph(size, num_edges, sparsity, energy, balance, noise, + seed=None): + if seed: + random.seed(seed) + size_part_a = int(math.ceil(float(size * balance) / 2)) size_part_b = size - size_part_a F = [] @@ -91,7 +95,11 @@ def compute_embedding(distances, radius, graph): return np.array(B) -def generate_dyn_cascade(G, diam, duration, n): +def generate_dyn_cascade(G, diam, duration, n, seed=None): + + if seed: + random.seed(seed) + Fs = [] for j in range(n): @@ -112,7 +120,9 @@ def generate_dyn_cascade(G, diam, duration, n): return np.array(Fs) -def generate_dyn_heat(G, s, jump, n): +def generate_dyn_heat(G, s, jump, n, seed=None): + if seed: + random.seed(seed) Fs = [] L = nx.normalized_laplacian_matrix(G) L = L.todense() @@ -139,9 +149,10 @@ def generate_dyn_heat(G, s, jump, n): return np.array(Fs)[1:] -def generate_dyn_gaussian_noise(G, n): +def generate_dyn_gaussian_noise(G, n, seed=None): + if seed: + np.random.seed(seed) Fs = [] - for j in range(n): F = np.random.rand(len(G.nodes())) Fs.append(F) @@ -149,7 +160,11 @@ def generate_dyn_gaussian_noise(G, n): return np.array(Fs) -def generate_dyn_bursty_noise(G, n): +def generate_dyn_bursty_noise(G, n, seed1=None, seed2=None): + if seed1: + random.seed(seed1) + if seed2: + np.random.seed(seed2) Fs = [] bursty_beta = 1 non_bursty_beta = 1000 @@ -177,7 +192,11 @@ def generate_dyn_bursty_noise(G, n): return np.array(Fs) -def generate_dyn_indep_cascade(G, s, p): +def generate_dyn_indep_cascade(G, s, p, seed1=None, seed2=None): + if seed1: + random.seed(seed1) + if seed2: + np.random.seed(seed2) Fs = [] seeds = np.random.choice(len(G.nodes()), s, replace=False) @@ -219,7 +238,11 @@ def generate_dyn_indep_cascade(G, s, p): return np.array(Fs) -def generate_dyn_linear_threshold(G, s): +def generate_dyn_linear_threshold(G, s, seed1=None, seed2=None): + if seed1: + random.seed(seed1) + if seed2: + np.random.seed(seed2) Fs = [] seeds = np.random.choice(len(G.nodes()), s, replace=False) From 07972895a132cb06971bdd7f99b03ab8967bc333 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 15:41:37 +0200 Subject: [PATCH 24/62] Set seeds in synthetic-data for reproducibility --- compression-experiments.ipynb | 158 ++++++++++------------------------ synthetic-data.ipynb | 52 +++++------ 2 files changed, 68 insertions(+), 142 deletions(-) diff --git a/compression-experiments.ipynb b/compression-experiments.ipynb index 6ef1673..106e1ff 100644 --- a/compression-experiments.ipynb +++ b/compression-experiments.ipynb @@ -17,15 +17,13 @@ { "cell_type": "code", "execution_count": 79, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import networkx\n", "import math\n", "import scipy.optimize\n", - "import numpy\n", + "import numpy as np\n", "import sys\n", "from scipy import linalg\n", "import matplotlib.pyplot as plt\n", @@ -58,9 +56,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(small_traffic[\"path\"] + \"traffic.graph\", small_traffic[\"path\"] + \"traffic.data\")\n", @@ -70,9 +66,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -91,24 +85,20 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), OptWavelets(), GRCWavelets(), Fourier(), HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_smt, time_smt = compression_experiment_static(G, numpy.array(F), algs, comp_ratios, 10)" + "res_smt, time_smt = compression_experiment_static(G, np.array(F), algs, comp_ratios, 10)" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -137,9 +127,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -154,7 +142,7 @@ } ], "source": [ - "numpy.divide(res_smt['GWT'], res_smt['FSWT'])" + "np.divide(res_smt['GWT'], res_smt['FSWT'])" ] }, { @@ -167,9 +155,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -184,7 +170,7 @@ } ], "source": [ - "numpy.divide(res_smt['GWT'], res_smt['SWT'])" + "np.divide(res_smt['GWT'], res_smt['SWT'])" ] }, { @@ -197,9 +183,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(traffic[\"path\"] + \"traffic.graph\", traffic[\"path\"] + \"traffic.data\")\n", @@ -209,9 +193,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -230,9 +212,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -245,9 +225,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -276,9 +254,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -293,7 +269,7 @@ } ], "source": [ - "numpy.divide(res_t['GWT'], res_t['FSWT'])" + "np.divide(res_t['GWT'], res_t['FSWT'])" ] }, { @@ -306,9 +282,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(human[\"path\"] + \"human.graph\", human[\"path\"] + \"human.data\")\n", @@ -318,9 +292,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -339,9 +311,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -354,9 +324,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -385,9 +353,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -402,7 +368,7 @@ } ], "source": [ - "numpy.divide(res_h['GWT'], res_h['FSWT'])" + "np.divide(res_h['GWT'], res_h['FSWT'])" ] }, { @@ -427,9 +393,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -442,9 +406,7 @@ { "cell_type": "code", "execution_count": 76, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -466,9 +428,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -483,14 +443,12 @@ } ], "source": [ - "numpy.divide(res_w['GWT'], res_w['FSWT'])" + "np.divide(res_w['GWT'], res_w['FSWT'])" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Blogs" ] @@ -498,9 +456,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(polblogs[\"path\"] + \"polblogs.graph\", polblogs[\"path\"] + \"polblogs.data\")\n", @@ -510,9 +466,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -531,9 +485,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -546,9 +498,7 @@ { "cell_type": "code", "execution_count": 77, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -577,9 +527,7 @@ { "cell_type": "code", "execution_count": 78, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -594,14 +542,12 @@ } ], "source": [ - "numpy.divide(res_b['GWT'], res_b['FSWT'])" + "np.divide(res_b['GWT'], res_b['FSWT'])" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Average Computation times" ] @@ -618,9 +564,7 @@ { "cell_type": "code", "execution_count": 70, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -636,7 +580,7 @@ ], "source": [ "for alg in time_smt:\n", - " print(alg, \" \" ,numpy.mean(time_smt[alg]))" + " print(alg, \" \" ,np.mean(time_smt[alg]))" ] }, { @@ -649,9 +593,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -665,7 +607,7 @@ ], "source": [ "for alg in time_t:\n", - " print(alg, \" \", numpy.mean(time_t[alg]))" + " print(alg, \" \", np.mean(time_t[alg]))" ] }, { @@ -678,9 +620,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -694,7 +634,7 @@ ], "source": [ "for alg in time_h:\n", - " print(alg, \" \", numpy.mean(time_h[alg]))" + " print(alg, \" \", np.mean(time_h[alg]))" ] }, { @@ -707,9 +647,7 @@ { "cell_type": "code", "execution_count": 72, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -723,7 +661,7 @@ ], "source": [ "for alg in time_w:\n", - " print(alg, \" \",numpy.mean(time_w[alg]))" + " print(alg, \" \",np.mean(time_w[alg]))" ] }, { @@ -736,9 +674,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -752,7 +688,7 @@ ], "source": [ "for alg in time_b:\n", - " print(alg, \" \", numpy.mean(time_b[alg]))" + " print(alg, \" \", np.mean(time_b[alg]))" ] } ], @@ -772,9 +708,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/synthetic-data.ipynb b/synthetic-data.ipynb index 11743bb..2984f86 100644 --- a/synthetic-data.ipynb +++ b/synthetic-data.ipynb @@ -17,15 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import networkx\n", "import math\n", "import scipy.optimize\n", - "import numpy\n", + "import numpy as np\n", "import sys\n", "from scipy import linalg\n", "import matplotlib.pyplot as plt\n", @@ -58,9 +56,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sizes = range(200, 1001, 200)\n", @@ -70,15 +66,15 @@ "sparsity = 0.8\n", "noise = .1\n", "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_t = size_time_experiment(sizes, balance, sparsity, energy, noise, num)" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -93,7 +89,7 @@ } ], "source": [ - "plot_size_time_experiment(numpy.array(res_t), sizes, \"figs/size_time_synthetic.png\")\n", + "plot_size_time_experiment(np.array(res_t), sizes, \"figs/size_time_synthetic.png\")\n", "Image(filename=\"figs/size_time_synthetic.png\")" ] }, @@ -107,9 +103,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sparsity = [0.2, 0.4, 0.6, 0.8]\n", @@ -118,15 +112,15 @@ "balance = 1.\n", "noise = .5\n", "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_acc = sparsity_acc_experiment(sparsity, size, balance, energy, noise, num)" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -155,9 +149,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "energy = [10, 100, 1000, 10000]\n", @@ -166,15 +158,15 @@ "num = 10\n", "sparsity = .5\n", "balance = 1.\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_acc = energy_acc_experiment(energy, size, sparsity, noise, balance, num)" ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -204,9 +196,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "noise = [.2, .4, .6, .8]\n", @@ -215,15 +205,15 @@ "sparsity = 1.\n", "balance = 1.\n", "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_acc = noise_acc_experiment(noise, size, sparsity, energy, balance, num)" ] }, { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -259,9 +249,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 68e83b0870c1b5206c882dd9d8e208af95505485 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 15:41:37 +0200 Subject: [PATCH 25/62] Set seeds in synthetic-data for reproducibility --- compression-experiments.ipynb | 158 ++++++++++------------------------ synthetic-data.ipynb | 52 +++++------ 2 files changed, 68 insertions(+), 142 deletions(-) diff --git a/compression-experiments.ipynb b/compression-experiments.ipynb index 6ef1673..106e1ff 100644 --- a/compression-experiments.ipynb +++ b/compression-experiments.ipynb @@ -17,15 +17,13 @@ { "cell_type": "code", "execution_count": 79, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import networkx\n", "import math\n", "import scipy.optimize\n", - "import numpy\n", + "import numpy as np\n", "import sys\n", "from scipy import linalg\n", "import matplotlib.pyplot as plt\n", @@ -58,9 +56,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(small_traffic[\"path\"] + \"traffic.graph\", small_traffic[\"path\"] + \"traffic.data\")\n", @@ -70,9 +66,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -91,24 +85,20 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), OptWavelets(), GRCWavelets(), Fourier(), HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_smt, time_smt = compression_experiment_static(G, numpy.array(F), algs, comp_ratios, 10)" + "res_smt, time_smt = compression_experiment_static(G, np.array(F), algs, comp_ratios, 10)" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -137,9 +127,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -154,7 +142,7 @@ } ], "source": [ - "numpy.divide(res_smt['GWT'], res_smt['FSWT'])" + "np.divide(res_smt['GWT'], res_smt['FSWT'])" ] }, { @@ -167,9 +155,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -184,7 +170,7 @@ } ], "source": [ - "numpy.divide(res_smt['GWT'], res_smt['SWT'])" + "np.divide(res_smt['GWT'], res_smt['SWT'])" ] }, { @@ -197,9 +183,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(traffic[\"path\"] + \"traffic.graph\", traffic[\"path\"] + \"traffic.data\")\n", @@ -209,9 +193,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -230,9 +212,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -245,9 +225,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -276,9 +254,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -293,7 +269,7 @@ } ], "source": [ - "numpy.divide(res_t['GWT'], res_t['FSWT'])" + "np.divide(res_t['GWT'], res_t['FSWT'])" ] }, { @@ -306,9 +282,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(human[\"path\"] + \"human.graph\", human[\"path\"] + \"human.data\")\n", @@ -318,9 +292,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -339,9 +311,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -354,9 +324,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -385,9 +353,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -402,7 +368,7 @@ } ], "source": [ - "numpy.divide(res_h['GWT'], res_h['FSWT'])" + "np.divide(res_h['GWT'], res_h['FSWT'])" ] }, { @@ -427,9 +393,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -442,9 +406,7 @@ { "cell_type": "code", "execution_count": 76, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -466,9 +428,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -483,14 +443,12 @@ } ], "source": [ - "numpy.divide(res_w['GWT'], res_w['FSWT'])" + "np.divide(res_w['GWT'], res_w['FSWT'])" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Blogs" ] @@ -498,9 +456,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "G = read_graph(polblogs[\"path\"] + \"polblogs.graph\", polblogs[\"path\"] + \"polblogs.data\")\n", @@ -510,9 +466,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -531,9 +485,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", @@ -546,9 +498,7 @@ { "cell_type": "code", "execution_count": 77, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -577,9 +527,7 @@ { "cell_type": "code", "execution_count": 78, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -594,14 +542,12 @@ } ], "source": [ - "numpy.divide(res_b['GWT'], res_b['FSWT'])" + "np.divide(res_b['GWT'], res_b['FSWT'])" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Average Computation times" ] @@ -618,9 +564,7 @@ { "cell_type": "code", "execution_count": 70, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -636,7 +580,7 @@ ], "source": [ "for alg in time_smt:\n", - " print(alg, \" \" ,numpy.mean(time_smt[alg]))" + " print(alg, \" \" ,np.mean(time_smt[alg]))" ] }, { @@ -649,9 +593,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -665,7 +607,7 @@ ], "source": [ "for alg in time_t:\n", - " print(alg, \" \", numpy.mean(time_t[alg]))" + " print(alg, \" \", np.mean(time_t[alg]))" ] }, { @@ -678,9 +620,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -694,7 +634,7 @@ ], "source": [ "for alg in time_h:\n", - " print(alg, \" \", numpy.mean(time_h[alg]))" + " print(alg, \" \", np.mean(time_h[alg]))" ] }, { @@ -707,9 +647,7 @@ { "cell_type": "code", "execution_count": 72, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -723,7 +661,7 @@ ], "source": [ "for alg in time_w:\n", - " print(alg, \" \",numpy.mean(time_w[alg]))" + " print(alg, \" \",np.mean(time_w[alg]))" ] }, { @@ -736,9 +674,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -752,7 +688,7 @@ ], "source": [ "for alg in time_b:\n", - " print(alg, \" \", numpy.mean(time_b[alg]))" + " print(alg, \" \", np.mean(time_b[alg]))" ] } ], @@ -772,9 +708,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/synthetic-data.ipynb b/synthetic-data.ipynb index 11743bb..2984f86 100644 --- a/synthetic-data.ipynb +++ b/synthetic-data.ipynb @@ -17,15 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import networkx\n", "import math\n", "import scipy.optimize\n", - "import numpy\n", + "import numpy as np\n", "import sys\n", "from scipy import linalg\n", "import matplotlib.pyplot as plt\n", @@ -58,9 +56,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sizes = range(200, 1001, 200)\n", @@ -70,15 +66,15 @@ "sparsity = 0.8\n", "noise = .1\n", "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_t = size_time_experiment(sizes, balance, sparsity, energy, noise, num)" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -93,7 +89,7 @@ } ], "source": [ - "plot_size_time_experiment(numpy.array(res_t), sizes, \"figs/size_time_synthetic.png\")\n", + "plot_size_time_experiment(np.array(res_t), sizes, \"figs/size_time_synthetic.png\")\n", "Image(filename=\"figs/size_time_synthetic.png\")" ] }, @@ -107,9 +103,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sparsity = [0.2, 0.4, 0.6, 0.8]\n", @@ -118,15 +112,15 @@ "balance = 1.\n", "noise = .5\n", "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_acc = sparsity_acc_experiment(sparsity, size, balance, energy, noise, num)" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -155,9 +149,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "energy = [10, 100, 1000, 10000]\n", @@ -166,15 +158,15 @@ "num = 10\n", "sparsity = .5\n", "balance = 1.\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_acc = energy_acc_experiment(energy, size, sparsity, noise, balance, num)" ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -204,9 +196,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "noise = [.2, .4, .6, .8]\n", @@ -215,15 +205,15 @@ "sparsity = 1.\n", "balance = 1.\n", "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", "res_acc = noise_acc_experiment(noise, size, sparsity, energy, balance, num)" ] }, { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -259,9 +249,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.4.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 54b9117849344253c8358985e31c4edf26a11d07 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 17:47:52 +0200 Subject: [PATCH 26/62] Fix imports in lib/experiments.py --- lib/experiments.py | 839 ++++++++++++++++++++++++--------------------- lib/optimal_cut.py | 331 +++++++++--------- 2 files changed, 611 insertions(+), 559 deletions(-) diff --git a/lib/experiments.py b/lib/experiments.py index 3b86267..fb45a0f 100644 --- a/lib/experiments.py +++ b/lib/experiments.py @@ -1,422 +1,461 @@ -import matplotlib.pyplot as plt -from matplotlib.lines import Line2D -from lib.static import * -from mpl_toolkits.mplot3d import axes3d -import numpy -from matplotlib.mlab import griddata -from matplotlib import cm -from lib.syn import * import time -import sys + +import numpy +import matplotlib.pyplot as plt +import networkx as nx + +import lib.optimal_cut as oc +import lib.syn as syn + def L2(F, F_approx): - """ - Sum of squared errors - """ - e = 0 - for i in range(F.shape[0]): - e = e + ((F[i]-F_approx[i])**2).sum() + """ + Sum of squared errors + """ + e = 0 + for i in range(F.shape[0]): + e = e + ((F[i] - F_approx[i])**2).sum() + + return float(e) - return float(e) def size_time_experiment(sizes, balance, sparsity, energy, noise, num): - """ - Size x time experiment using synthetic data. - Input: - * sizes: many - * balance - * sparsity - * energy - * noise - * num: number of repetitions - Output: - * res_time: time results - """ - res_time = [] - - for s in range(len(sizes)): - res_t = [] - for i in range(num): - (G,F,cut) = synthetic_graph(sizes[s], 3*sizes[s], sparsity, energy, balance, noise) - - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 - - k = int(len(G.edges())*sparsity) - start_time = time.time() - c = one_d_search(G, F, k, ind) - time_slow = time.time()-start_time - - start_time = time.time() - c = fast_search(G, F, k, 5, ind) - time_5 = time.time()-start_time - - start_time = time.time() - c = fast_search(G, F, k, 20, ind) - time_20 = time.time()-start_time - - start_time = time.time() - c = fast_search(G, F, k, 50, ind) - time_50 = time.time()-start_time - - res_t.append([time_slow, time_5, time_20, time_50]) - - r = numpy.mean(numpy.array(res_t), axis=0) - res_time.append(r) - - return numpy.array(res_time) + """ + Size x time experiment using synthetic data. + Input: + * sizes: list of sizes + * balance + * sparsity + * energy + * noise + * num: number of repetitions + Output: + * res_time: time results + """ + res_time = [] + + for s in range(len(sizes)): + res_t = [] + num_edges = 3 * sizes[s] + for i in range(num): + # synthetic_graph(size, num_edges, sparsity, energy, balance, + # noise, seed=None) + (G, F, cut) = syn.synthetic_graph(sizes[s], num_edges, sparsity, + energy, balance, noise) + + j = 0 + ind = {} + for v in G.nodes(): + ind[v] = j + j = j + 1 + + # k = max number of edges to be cut + k = int(len(G.edges()) * sparsity) + + start_time = time.time() + c = oc.one_d_search(G, F, k, ind) + time_slow = time.time() - start_time + + start_time = time.time() + c = oc.fast_search(G, F, k, 5, ind) + time_5 = time.time() - start_time + + start_time = time.time() + c = oc.fast_search(G, F, k, 20, ind) + time_20 = time.time() - start_time + + start_time = time.time() + c = oc.fast_search(G, F, k, 50, ind) + time_50 = time.time() - start_time + + res_t.append([time_slow, time_5, time_20, time_50]) + + r = numpy.mean(numpy.array(res_t), axis=0) + res_time.append(r) + + return numpy.array(res_time) + def sparsity_acc_experiment(sparsity, size, balance, energy, noise, num): - """ - Sparsity x accuracy experiments using synthetic data. - Input: - * sparsity: many - * size - * balance - * energy - * noise - * num: number of repetitions - Output: - * res: accuracy results - """ - res = [] - - for s in range(len(sparsity)): - res_a = [] - for i in range(num): - (G,F,k) = synthetic_graph(size, 3*size, sparsity[s], energy, balance, noise) - - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 - - c = one_d_search(G, F, k, ind) - acc_slow = c["energy"] - - c = fast_search(G, F, k, 5, ind) - acc_5 = c["energy"] - - c = fast_search(G, F, k, 20, ind) - acc_20 = c["energy"] - - c = fast_search(G, F, k, 50, ind) - acc_50 = c["energy"] - - res_a.append([acc_slow, acc_5, acc_20, acc_50]) - - r = numpy.mean(numpy.array(res_a), axis=0) - res.append(r) - - return numpy.array(res) + """ + Sparsity x accuracy experiments using synthetic data. + Input: + * sparsity: many + * size + * balance + * energy + * noise + * num: number of repetitions + Output: + * res: accuracy results + """ + res = [] + + for s in range(len(sparsity)): + res_a = [] + for i in range(num): + (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity[s], + energy, balance, noise) + + j = 0 + ind = {} + for v in G.nodes(): + ind[v] = j + j = j + 1 + + c = oc.one_d_search(G, F, k, ind) + acc_slow = c["energy"] + + c = oc.fast_search(G, F, k, 5, ind) + acc_5 = c["energy"] + + c = oc.fast_search(G, F, k, 20, ind) + acc_20 = c["energy"] + + c = oc.fast_search(G, F, k, 50, ind) + acc_50 = c["energy"] + + res_a.append([acc_slow, acc_5, acc_20, acc_50]) + + r = numpy.mean(numpy.array(res_a), axis=0) + res.append(r) + + return numpy.array(res) + def noise_acc_experiment(noise, size, sparsity, energy, balance, num): - """ - Noise x accuracy experiments using synthetic data. - Input: - * noise: many - * size - * sparsity - * energy - * balance - * num: number of repetitions - Output: - * res: accuracy results - """ - res = [] - - for s in range(len(noise)): - res_a = [] - for i in range(num): - (G,F,k) = synthetic_graph(size, 3*size, sparsity, energy, balance, noise[s]) - - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 - - c = one_d_search(G, F, k, ind) - acc_slow = c["energy"] - - L = networkx.laplacian_matrix(G) - c = fast_search(G, F, k, 5, ind) - acc_5 = c["energy"] - - c = fast_search(G, F, k, 20, ind) - acc_20 = c["energy"] - - c = fast_search(G, F, k, 50, ind) - acc_50 = c["energy"] - - res_a.append([acc_slow, acc_5, acc_20, acc_50]) - - r = numpy.mean(numpy.array(res_a), axis=0) - res.append(r) - - return numpy.array(res) + """ + Noise x accuracy experiments using synthetic data. + Input: + * noise: many + * size + * sparsity + * energy + * balance + * num: number of repetitions + Output: + * res: accuracy results + """ + res = [] + + for s in range(len(noise)): + res_a = [] + for i in range(num): + (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity, + energy, balance, noise[s]) + + j = 0 + ind = {} + for v in G.nodes(): + ind[v] = j + j = j + 1 + + c = oc.one_d_search(G, F, k, ind) + acc_slow = c["energy"] + + L = nx.laplacian_matrix(G) + c = oc.fast_search(G, F, k, 5, ind) + acc_5 = c["energy"] + + c = oc.fast_search(G, F, k, 20, ind) + acc_20 = c["energy"] + + c = oc.fast_search(G, F, k, 50, ind) + acc_50 = c["energy"] + + res_a.append([acc_slow, acc_5, acc_20, acc_50]) + + r = numpy.mean(numpy.array(res_a), axis=0) + res.append(r) + + return numpy.array(res) + def energy_acc_experiment(energy, size, sparsity, noise, balance, num): - """ - Energy x accuracy experiments using synthetic data. - Input: - * energy: many - * size - * sparsity - * noise - * balance - * num: number of repetitions - Output: - * res: accuracy results - """ - res = [] - - for s in range(len(energy)): - res_a = [] - for i in range(num): - (G,F,k) = synthetic_graph(size, 3*size, sparsity, energy[s], balance, noise) - - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 - - c = one_d_search(G, F, k, ind) - acc_slow = c["energy"] - - c = fast_search(G, F, k, 5, ind) - acc_5 = c["energy"] - - c = fast_search(G, F, k, 20, ind) - acc_20 = c["energy"] - - c = fast_search(G, F, k, 50, ind) - acc_50 = c["energy"] - - res_a.append([acc_slow, acc_5, acc_20, acc_50]) - - r = numpy.mean(numpy.array(res_a), axis=0) - res.append(r) - - return numpy.array(res) + """ + Energy x accuracy experiments using synthetic data. + Input: + * energy: many + * size + * sparsity + * noise + * balance + * num: number of repetitions + Output: + * res: accuracy results + """ + res = [] + + for s in range(len(energy)): + res_a = [] + for i in range(num): + (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity, + energy[s], balance, noise) + + j = 0 + ind = {} + for v in G.nodes(): + ind[v] = j + j = j + 1 + + c = oc.one_d_search(G, F, k, ind) + acc_slow = c["energy"] + + c = oc.fast_search(G, F, k, 5, ind) + acc_5 = c["energy"] + + c = oc.fast_search(G, F, k, 20, ind) + acc_20 = c["energy"] + + c = oc.fast_search(G, F, k, 50, ind) + acc_50 = c["energy"] + + res_a.append([acc_slow, acc_5, acc_20, acc_50]) + + r = numpy.mean(numpy.array(res_a), axis=0) + res.append(r) + + return numpy.array(res) + def plot_size_time_experiment(results, sizes, output_file_name): - """ - Plots size x time experiment. - Input: - * results: time results - * sizes: graph sizes - * output_file_name: output file name - Output: - * None - """ - plt.clf() - - ax = plt.subplot(111) - - ncol=2 - ax.plot(sizes, results[:,0], marker="x", color="cyan", label="SWT", markersize=15) - ax.plot(sizes, results[:,1], marker="o", color="orangered", label="FSWT-5", markersize=15) - ax.plot(sizes, results[:,2], marker="o", color="darkgreen", label="FSWT-20", markersize=15) - ax.plot(sizes, results[:,3], marker="o", color="k", label="FSWT-50", markersize=15) - plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size':20}, ncol=ncol) - ax.set_ylabel('time (sec.)', fontsize=30) - ax.set_xlabel('#vertices', fontsize=30) - ax.tick_params(labelsize=23) - #plt.rcParams['xtick.labelsize'] = 80 - #plt.rcParams['ytick.labelsize'] = 80 - ax.set_xlim([180,1020]) - ax.set_ylim([0.01,50000]) - ax.set_yscale('log') - - plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + """ + Plots size x time experiment. + Input: + * results: time results + * sizes: graph sizes + * output_file_name: output file name + Output: + * None + """ + plt.clf() + + ax = plt.subplot(111) + + ncol = 2 + ax.plot(sizes, results[:, 0], marker="x", color="cyan", + label="SWT", markersize=15) + ax.plot(sizes, results[:, 1], marker="o", color="orangered", + label="FSWT-5", markersize=15) + ax.plot(sizes, results[:, 2], marker="o", color="darkgreen", + label="FSWT-20", markersize=15) + ax.plot(sizes, results[:, 3], marker="o", color="k", + label="FSWT-50", markersize=15) + plt.gcf().subplots_adjust(bottom=0.15) + ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.set_ylabel('time (sec.)', fontsize=30) + ax.set_xlabel('#vertices', fontsize=30) + ax.tick_params(labelsize=23) + # plt.rcParams['xtick.labelsize'] = 80 + # plt.rcParams['ytick.labelsize'] = 80 + ax.set_xlim([180, 1020]) + ax.set_ylim([0.01, 50000]) + ax.set_yscale('log') + + plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + def plot_sparsity_acc_experiment(results, sparsity, output_file_name): - """ - Plots sparsity x accuracy experiment. - Input: - * results: accuracy results - * sparsity: sparsity values - * output_file_name: output file name - Output: - * None - """ - plt.clf() - - ncol=2 - ax = plt.subplot(111) - width = 0.04 # the width of the bars) - ax.bar(numpy.array(sparsity)-2*width, results[:,0], width, color='cyan', label="SWT", hatch="/") - ax.bar(numpy.array(sparsity)-width, results[:,1], width, color='orangered', label="FSWT-5", hatch="\\") - ax.bar(numpy.array(sparsity), results[:,2], width, color='darkgreen', label="FSWT-20", hatch="-") - ax.bar(numpy.array(sparsity)+width, results[:,3], width, color='k', label="FSWT-50", hatch="*") - plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size':20}, ncol=ncol) - ax.set_ylabel(r'L$_2$ energy', fontsize=30) - ax.set_xlabel('sparsity', fontsize=30) - plt.rcParams['xtick.labelsize'] = 20 - plt.rcParams['ytick.labelsize'] = 20 - ax.set_xlim([0.1,0.9]) - ax.set_ylim([0,150]) - - plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + """ + Plots sparsity x accuracy experiment. + Input: + * results: accuracy results + * sparsity: sparsity values + * output_file_name: output file name + Output: + * None + """ + plt.clf() + + ncol = 2 + ax = plt.subplot(111) + width = 0.04 # the width of the bars) + ax.bar(numpy.array(sparsity) - 2 * width, + results[:, 0], width, color='cyan', label="SWT", hatch="/") + ax.bar(numpy.array(sparsity) - width, results[:, 1], + width, color='orangered', label="FSWT-5", hatch="\\") + ax.bar(numpy.array(sparsity), results[:, 2], width, + color='darkgreen', label="FSWT-20", hatch="-") + ax.bar(numpy.array(sparsity) + width, results[:, 3], + width, color='k', label="FSWT-50", hatch="*") + plt.gcf().subplots_adjust(bottom=0.15) + ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.set_ylabel(r'L$_2$ energy', fontsize=30) + ax.set_xlabel('sparsity', fontsize=30) + plt.rcParams['xtick.labelsize'] = 20 + plt.rcParams['ytick.labelsize'] = 20 + ax.set_xlim([0.1, 0.9]) + ax.set_ylim([0, 150]) + + plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + def plot_noise_acc_experiment(results, noise, output_file_name): - """ - Plots noise x accuracy experiment. - Input: - * results: accuracy results - * noise: noise values - * output_file_name: output file name - Output: - * None - """ - plt.clf() - - ax = plt.subplot(111) - ncol=2 - width = 0.04 # the width of the bars) - rects1 = ax.bar(numpy.array(noise)-2*width, results[:,0], width, color='cyan', label="SWT") - rects2 = ax.bar(numpy.array(noise)-width, results[:,1], width, color='orangered', label="FSWT-5") - rects3 = ax.bar(numpy.array(noise), results[:,2], width, color='darkgreen', label="FSWT-20") - rects4 = ax.bar(numpy.array(noise)+width, results[:,3], width, color='k', label="FSWT-50") - - plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size':20}, ncol=ncol) - ax.set_ylabel(r'L$_2$ energy', fontsize=30) - ax.set_xlabel('noise', fontsize=30) - plt.rcParams['xtick.labelsize'] = 20 - plt.rcParams['ytick.labelsize'] = 20 - ax.set_xlim([0.1,0.9]) - ax.set_ylim([0,150]) - - plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + """ + Plots noise x accuracy experiment. + Input: + * results: accuracy results + * noise: noise values + * output_file_name: output file name + Output: + * None + """ + plt.clf() + + ax = plt.subplot(111) + ncol = 2 + width = 0.04 # the width of the bars) + rects1 = ax.bar(numpy.array(noise) - 2 * width, results[:, 0], + width, color='cyan', label="SWT") + rects2 = ax.bar(numpy.array(noise) - width, + results[:, 1], width, color='orangered', label="FSWT-5") + rects3 = ax.bar(numpy.array(noise), results[:, 2], + width, color='darkgreen', label="FSWT-20") + rects4 = ax.bar(numpy.array(noise) + width, results[:, 3], + width, color='k', label="FSWT-50") + + plt.gcf().subplots_adjust(bottom=0.15) + ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.set_ylabel(r'L$_2$ energy', fontsize=30) + ax.set_xlabel('noise', fontsize=30) + plt.rcParams['xtick.labelsize'] = 20 + plt.rcParams['ytick.labelsize'] = 20 + ax.set_xlim([0.1, 0.9]) + ax.set_ylim([0, 150]) + + plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + def plot_energy_acc_experiment(results, energy, output_file_name): - """ - Plots energy x accuracy experiment. - Input: - * results: accuracy results - * energy: energy values - * output_file_name: output file name - Output: - * None - """ - plt.clf() - ncol=2 - ind = numpy.array(list(range(4))) - ax = plt.subplot(111) - width = 0.2 # the width of the bars) - rects1 = ax.bar(ind-width, results[:,0], width, color='cyan', label="SWT", log=True) - rects2 = ax.bar(ind, results[:,1], width, color='orangered', label="FSWT-5", log=True) - rects3 = ax.bar(ind+width, results[:,2], width, color='darkgreen', label="FSWT-20", log=True) - rects4 = ax.bar(ind+2*width, results[:,3], width, color='k', label="FSWT-50", log=True) - - plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper left', prop={'size':20}, ncol=ncol) - ax.set_ylabel(r'L$_2$ energy', fontsize=30) - ax.set_xlabel(r'L$_2$ energy (data)', fontsize=30) - plt.rcParams['xtick.labelsize'] = 20 - plt.rcParams['ytick.labelsize'] = 20 - ax.set_yscale('log') - ax.set_xticks(ind + width) - ax.set_xticklabels((r'10$\mathregular{^1}$', r'10$\mathregular{^2}$', r'10$\mathregular{^3}$', r'10$\mathregular{^4}$')) - ax.set_ylim(0.1,1000000) - ax.set_xlim(-0.3,3.7) - - plt.savefig(output_file_name, dpi=300, bbox_inches='tight') - -def plot_compression_experiments(results, comp_ratios, output_file_name, max_y): - """ - Plots compression size x accuracy experiment. - Input: - * results: accuracy results - * comp_ratios: compression ratios - * output_file_name: output file name - * max_y: maximum y-axis - Output: - * None - """ - plt.clf() - - for alg in results: - for i in range(1,results[alg].shape[0]): - if results[alg][i] > results[alg][i-1]: - results[alg][i] = results[alg][i-1] - - ax = plt.subplot(111) - ncol = 3 - - ax.semilogy(comp_ratios, results["FSWT"], marker="o", color="r", label="FSWT", markersize=15) - ax.semilogy(comp_ratios, results["FT"], marker="*", color="c", label="FT", markersize=15) - - if "SWT" in results: - ax.semilogy(comp_ratios, results["SWT"], marker="x", color="b", label="SWT", markersize=15) - - ax.semilogy(comp_ratios, results["GWT"], marker="s", color="g", label="GWT", markersize=15) - - if "HWT" in results: - ax.semilogy(comp_ratios, results["HWT"], marker="v", color="y", label="HWT", markersize=15) - - plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size':20}, ncol=ncol) - ax.set_ylabel(r'L$_2$ error', fontsize=30) - ax.set_xlabel('size', fontsize=30) - plt.rcParams['xtick.labelsize'] = 20 - plt.rcParams['ytick.labelsize'] = 20 - ax.set_xlim(0.,0.65) - ax.set_ylim(0., max_y) - - plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + """ + Plots energy x accuracy experiment. + Input: + * results: accuracy results + * energy: energy values + * output_file_name: output file name + Output: + * None + """ + plt.clf() + ncol = 2 + ind = numpy.array(list(range(4))) + ax = plt.subplot(111) + width = 0.2 # the width of the bars) + rects1 = ax.bar(ind - width, results[:, 0], + width, color='cyan', label="SWT", log=True) + rects2 = ax.bar(ind, results[:, 1], + width, color='orangered', label="FSWT-5", log=True) + rects3 = ax.bar(ind + width, results[:, 2], + width, color='darkgreen', label="FSWT-20", log=True) + rects4 = ax.bar(ind + 2 * width, results[:, 3], + width, color='k', label="FSWT-50", log=True) + + plt.gcf().subplots_adjust(bottom=0.15) + ax.legend(loc='upper left', prop={'size': 20}, ncol=ncol) + ax.set_ylabel(r'L$_2$ energy', fontsize=30) + ax.set_xlabel(r'L$_2$ energy (data)', fontsize=30) + plt.rcParams['xtick.labelsize'] = 20 + plt.rcParams['ytick.labelsize'] = 20 + ax.set_yscale('log') + ax.set_xticks(ind + width) + ax.set_xticklabels((r'10$\mathregular{^1}$', r'10$\mathregular{^2}$', + r'10$\mathregular{^3}$', r'10$\mathregular{^4}$')) + ax.set_ylim(0.1, 1000000) + ax.set_xlim(-0.3, 3.7) + + plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + + +def plot_compression_experiments(results, comp_ratios, output_file_name, + max_y): + """ + Plots compression size x accuracy experiment. + Input: + * results: accuracy results + * comp_ratios: compression ratios + * output_file_name: output file name + * max_y: maximum y-axis + Output: + * None + """ + plt.clf() + + for alg in results: + for i in range(1, results[alg].shape[0]): + if results[alg][i] > results[alg][i - 1]: + results[alg][i] = results[alg][i - 1] + + ax = plt.subplot(111) + ncol = 3 + + ax.semilogy(comp_ratios, results["FSWT"], marker="o", color="r", + label="FSWT", markersize=15) + ax.semilogy(comp_ratios, results["FT"], marker="*", color="c", + label="FT", markersize=15) + + if "SWT" in results: + ax.semilogy(comp_ratios, results["SWT"], marker="x", color="b", + label="SWT", markersize=15) + + ax.semilogy(comp_ratios, results["GWT"], marker="s", color="g", + label="GWT", markersize=15) + + if "HWT" in results: + ax.semilogy(comp_ratios, results["HWT"], marker="v", color="y", + label="HWT", markersize=15) + + plt.gcf().subplots_adjust(bottom=0.15) + ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.set_ylabel(r'L$_2$ error', fontsize=30) + ax.set_xlabel('size', fontsize=30) + plt.rcParams['xtick.labelsize'] = 20 + plt.rcParams['ytick.labelsize'] = 20 + ax.set_xlim(0., 0.65) + ax.set_ylim(0., max_y) + + plt.savefig(output_file_name, dpi=300, bbox_inches='tight') + def compression_experiment_static(G, F, algs, comp_ratios, num): - """ - Runs compression experiment static. - Input: - * G: graph - * F: graph signal - * algs: compression algorithms/transforms - * comp_ratios: compression ratios - * num: number of repetitions - Output: - * results: compression results - * times: compression times - """ - results = {} - times = {} - - for alg in algs: - results[alg.name()] = [] - times[alg.name()] = [] - - for r in range(len(comp_ratios)): - T = [] - R = [] - for i in range(num): - start_time = time.time() - alg.set_graph(G) - tr = alg.transform(F) - size = int(F.size * comp_ratios[r]) - appx_tr = alg.drop_frequency(tr, size) - appx_F = alg.inverse(appx_tr) - t = time.time()-start_time - T.append(t) - R.append(L2(F, appx_F)) - T = numpy.array(T) - R = numpy.array(R) - times[alg.name()].append(numpy.mean(T)) - results[alg.name()].append(numpy.mean(R)) - - results[alg.name()] = numpy.array(results[alg.name()]) - - times[alg.name()] = numpy.array(times[alg.name()]) - - return results, times - - + """ + Runs compression experiment static. + Input: + * G: graph + * F: graph signal + * algs: compression algorithms/transforms + * comp_ratios: compression ratios + * num: number of repetitions + Output: + * results: compression results + * times: compression times + """ + results = {} + times = {} + + for alg in algs: + results[alg.name()] = [] + times[alg.name()] = [] + + for r in range(len(comp_ratios)): + T = [] + R = [] + for i in range(num): + start_time = time.time() + alg.set_graph(G) + tr = alg.transform(F) + size = int(F.size * comp_ratios[r]) + appx_tr = alg.drop_frequency(tr, size) + appx_F = alg.inverse(appx_tr) + t = time.time() - start_time + T.append(t) + R.append(L2(F, appx_F)) + T = numpy.array(T) + R = numpy.array(R) + times[alg.name()].append(numpy.mean(T)) + results[alg.name()].append(numpy.mean(R)) + + results[alg.name()] = numpy.array(results[alg.name()]) + + times[alg.name()] = numpy.array(times[alg.name()]) + + return results, times diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index af919f7..638c34a 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -10,18 +10,18 @@ def sweep_opt(x, F, G, k, ind): """ - Sweep algorithm for sparse wavelets. - Input: - * x: continuous indicator vector - * F: graph signal - * G: graph - * k: max number of edges to be cut - * ind: vertex index v: unique integer - Output: - * vec: indicator vector - * best_val: score value - * best_edges_cut: number of edges cut - * energy: wavelet energy value + Sweep algorithm for sparse wavelets. + Input: + * x: continuous indicator vector + * F: graph signal + * G: graph + * k: max number of edges to be cut + * ind: vertex index v: unique integer + Output: + * vec: indicator vector + * best_val: score value + * best_edges_cut: number of edges cut + * energy: wavelet energy value """ best_val = 0. best_edges_cut = 0 @@ -52,7 +52,8 @@ def sweep_opt(x, F, G, k, ind): den = size_one * (total_size - size_one) * total_size if den > 0: - val = math.pow(sum_one * (total_size - size_one) - sum_two * size_one, 2) / den + val = math.pow(sum_one * (total_size - size_one) - sum_two * + size_one, 2) / den else: val = 0 @@ -63,7 +64,8 @@ def sweep_opt(x, F, G, k, ind): if total_size * size_one * (total_size - size_one) > 0: energy = math.pow(sum_one * (total_size - size_one) - sum_two * - size_one, 2) / (total_size * size_one * (total_size - size_one)) + size_one, 2) / (total_size * size_one * + (total_size - size_one)) else: energy = 0 @@ -80,11 +82,11 @@ def sweep_opt(x, F, G, k, ind): def laplacian_complete(n): """ - Laplacian of a complete graph with n vertices. - Input: - * n: size - Output: - * C: Laplacian + Laplacian of a complete graph with n vertices. + Input: + * n: size + Output: + * C: Laplacian """ C = np.ones((n, n)) C = -1 * C @@ -96,13 +98,13 @@ def laplacian_complete(n): def weighted_adjacency_complete(G, F, ind): """ - Computes weighted adjacency complete matrix (w(v)-w(u))^2 - Input: - * G: graph - * F: graph signal - * ind: vertex index vertex: unique integer - Output: - * A: nxn matrix + Computes weighted adjacency complete matrix (w(v)-w(u))^2 + Input: + * G: graph + * F: graph signal + * ind: vertex index vertex: unique integer + Output: + * A: nxn matrix """ A = [] for v in G.nodes(): @@ -115,13 +117,13 @@ def weighted_adjacency_complete(G, F, ind): def fast_cac(G, F, ind): """ - Computes product C*A*C, where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix. - Input: - * G: graph - * F: graph signal - * ind: vertex index v: unique integer - Output: - * CAC: matrix product + Computes product C*A*C, where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix. + Input: + * G: graph + * F: graph signal + * ind: vertex index v: unique integer + Output: + * CAC: matrix product """ CAC = [] for v in G.nodes(): @@ -137,13 +139,13 @@ def fast_cac(G, F, ind): def power_method(mat, start, maxit): """ - Power method implementation. - Input: - * mat: matrix - * start: initialization - * maxit: number of iterations - Output: - * vec: largest eigenvector of mat + Power method implementation. + Input: + * mat: matrix + * start: initialization + * maxit: number of iterations + Output: + * vec: largest eigenvector of mat """ vec = np.copy(start) vec = vec / np.linalg.norm(vec) @@ -156,22 +158,23 @@ def power_method(mat, start, maxit): def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): """ - Spectral cut implementation. - Input: - * CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix - * L: graph laplacian matrix - * C: laplacian complete graph - * A: pairwise squared difference matrix - * start: initialization - * beta: regularization parameter - * k: max edges cut - * ind: vertex index vertex: unique integer - Output: - * res: dictionary with following fields: - - x: indicator vector - - size: number of edges cut - - score: cut score - - energy: cut energy + Spectral cut implementation. + Input: + * CAC: C*A*C where C is the Laplacian of a complete graph and + A is a pairwise squared difference matrix + * L: graph laplacian matrix + * C: laplacian complete graph + * A: pairwise squared difference matrix + * start: initialization + * beta: regularization parameter + * k: max edges cut + * ind: vertex index vertex: unique integer + Output: + * res: dictionary with following fields: + - x: indicator vector + - size: number of edges cut + - score: cut score + - energy: cut energy """ isqrtCL = gsp.sqrtmi(C + beta * L) M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) @@ -192,14 +195,15 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): def eig_vis_opt(G, F, beta): """ - Computes first and second eigenvector of sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. - Input: - * G: graph - * F: graph signal - * beta: regularization parameter - Output: - * v1: first eigenvector - * v2: second eigenvector + Computes first and second eigenvector of + sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. + Input: + * G: graph + * F: graph signal + * beta: regularization parameter + Output: + * v1: first eigenvector + * v2: second eigenvector """ ind = {} i = 0 @@ -225,27 +229,28 @@ def eig_vis_opt(G, F, beta): def trans(L, min_v, max_v): """ - Chebyshev polynomial translation. - Input: - * L: Laplacian matrix - * min_v: lower bound - * max_v: upper bound - Output: - * translation + Chebyshev polynomial translation. + Input: + * L: Laplacian matrix + * min_v: lower bound + * max_v: upper bound + Output: + * translation """ - return (float(2.) / (max_v - min_v)) * L, -(float(max_v + min_v) / (max_v - min_v)) + return (float(2.) / (max_v - min_v)) * L, -(float(max_v + min_v) / + (max_v - min_v)) def fun(k, n, beta, min_v, max_v, x): """ - Function to be integrated in Chebyshev polynomial computation. - Input: - * k: coefficient number - * n: number of polynomials - * min_v: lower bound - * max_v: upper bound - Output: - * function value + Function to be integrated in Chebyshev polynomial computation. + Input: + * k: coefficient number + * n: number of polynomials + * min_v: lower bound + * max_v: upper bound + Output: + * function value """ y = 0.5 * math.cos(x) * float(max_v - min_v) + (0.5 * (max_v + min_v)) @@ -254,28 +259,30 @@ def fun(k, n, beta, min_v, max_v, x): def coef(k, n, beta, min_v, max_v): """ - Chebyshev polynomial coefficients. - Input: - * k: coefficient number - * n: number of polynomials - * min_v: lower bound - * max_v: upper bound - Output: - * coefficient + Chebyshev polynomial coefficients. + Input: + * k: coefficient number + * n: number of polynomials + * min_v: lower bound + * max_v: upper bound + Output: + * coefficient """ - return float(2. * scipy.integrate.quad(lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi + return float(2. * scipy.integrate.quad( + lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi def chebyshev_approx_2d(n, beta, X, L): """ - Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using Chebyshev polynomials (twice) - Input: - * n: number of polynomials - * beta: regularization parameter - * X: matrix - * L: Laplacian matrix - Output: - * P2: approximation + Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using + Chebyshev polynomials (twice) + Input: + * n: number of polynomials + * beta: regularization parameter + * X: matrix + * L: Laplacian matrix + Output: + * P2: approximation """ max_v = beta * L.shape[0] min_v = 1 @@ -310,14 +317,14 @@ def chebyshev_approx_2d(n, beta, X, L): def chebyshev_approx_1d(n, beta, x, L): """ - Approximates x*sqrt(L^+) using Chebyshev polynomials. - Input: - * n: number of polynomials - * beta: regularization parameter - * x: vector - * L: graph Laplacian - Output: - * P: approximation + Approximates x*sqrt(L^+) using Chebyshev polynomials. + Input: + * n: number of polynomials + * beta: regularization parameter + * x: vector + * L: graph Laplacian + Output: + * P: approximation """ max_v = beta * L.shape[0] @@ -340,23 +347,24 @@ def chebyshev_approx_1d(n, beta, x, L): def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): """ - Fast spectral cut implementation using chebyshev polynomials. - Input: - * CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix - * start: initialization - * F: graph signal - * G: graph - * L: graph laplacian matrix - * beta: regularization parameter - * k: max edges cut - * n: number of polynomials - * ind: vertex index vertex: unique integer - Output: - * res: dictionary with following fields: - - x: indicator vector - - size: number of edges cut - - score: cut score - - energy: cut energy + Fast spectral cut implementation using chebyshev polynomials. + Input: + * CAC: C*A*C where C is the Laplacian of a complete graph and + A is a pairwise squared difference matrix + * start: initialization + * F: graph signal + * G: graph + * L: graph laplacian matrix + * beta: regularization parameter + * k: max edges cut + * n: number of polynomials + * ind: vertex index vertex: unique integer + Output: + * res: dictionary with following fields: + - x: indicator vector + - size: number of edges cut + - score: cut score + - energy: cut energy """ L = nx.laplacian_matrix(G) M = chebyshev_approx_2d(n, beta, CAC, L) @@ -377,15 +385,16 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): def fast_search(G, F, k, n, ind): """ - Efficient version of cut computation. Does not perform 1-D search for beta. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * n: number of chebyshev polynomials - * ind: vertex index vertex: unique integer - Output: - * cut + Efficient version of cut computation. + Does not perform 1-D search for beta. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * n: number of chebyshev polynomials + * ind: vertex index vertex: unique integer + Output: + * cut """ start = np.ones(nx.number_of_nodes(G)) C = laplacian_complete(nx.number_of_nodes(G)) @@ -400,15 +409,15 @@ def fast_search(G, F, k, n, ind): def one_d_search(G, F, k, ind): """ - Cut computation. Perform 1-D search for beta using golden search. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * n: number of chebyshev polynomials - * ind: vertex index vertex: unique integer - Output: - * cut + Cut computation. Perform 1-D search for beta using golden search. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * n: number of chebyshev polynomials + * ind: vertex index vertex: unique integer + Output: + * cut """ C = laplacian_complete(nx.number_of_nodes(G)) A = weighted_adjacency_complete(G, F, ind) @@ -457,13 +466,13 @@ def one_d_search(G, F, k, ind): def get_subgraphs(G, cut): """ - Compute subgraphs generated by a cut. - Input: - * G: Original graph - * cut: cut indicator vector - Output: - * G1: subgraph 1 - * G2: subgraph 2 + Compute subgraphs generated by a cut. + Input: + * G: Original graph + * cut: cut indicator vector + Output: + * G1: subgraph 1 + * G2: subgraph 2 """ G1 = nx.Graph() G2 = nx.Graph() @@ -477,7 +486,10 @@ def get_subgraphs(G, cut): P2.append(v) i = i + 1 - G1 = G.subgraph(P1) + G1 = G.subgraph(P1) - x: indicator vector + - size: number of edges cut + - score: cut score + G2 = G.subgraph(P2) return G1, G2 @@ -485,16 +497,16 @@ def get_subgraphs(G, cut): def optimal_wavelet_basis(G, F, k, npol): """ - Computation of optimal graph wavelet basis. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * npol: number of chebyshev polynomials, if 0 run exact version - Output: - * root: tree root - * ind: vertex index vertex: unique integer - * size: number of edges cut + Computation of optimal graph wavelet basis. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * npol: number of chebyshev polynomials, if 0 run exact version + Output: + * root: tree root + * ind: vertex index vertex: unique integer + * size: number of edges cut """ # Creating index @@ -526,7 +538,8 @@ def optimal_wavelet_basis(G, F, k, npol): for i in range(0, len(cand_cuts)): if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["score"] > 0: - if best_cut is None or cand_cuts[i]["score"] > best_cut["score"]: + if (best_cut is None or + cand_cuts[i]["score"] > best_cut["score"]): best_cut = cand_cuts[i] b = i if best_cut is None: From 618481c0432fd0e8d7dd20b060f99ef26d9f6f28 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 18:02:19 +0200 Subject: [PATCH 27/62] Remove not used vars in lib/experiments.py --- lib/experiments.py | 42 ++++++++++++++++++++---------------------- 1 file changed, 20 insertions(+), 22 deletions(-) diff --git a/lib/experiments.py b/lib/experiments.py index fb45a0f..35e55e0 100644 --- a/lib/experiments.py +++ b/lib/experiments.py @@ -2,7 +2,6 @@ import numpy import matplotlib.pyplot as plt -import networkx as nx import lib.optimal_cut as oc import lib.syn as syn @@ -53,19 +52,19 @@ def size_time_experiment(sizes, balance, sparsity, energy, noise, num): k = int(len(G.edges()) * sparsity) start_time = time.time() - c = oc.one_d_search(G, F, k, ind) + oc.one_d_search(G, F, k, ind) time_slow = time.time() - start_time start_time = time.time() - c = oc.fast_search(G, F, k, 5, ind) + oc.fast_search(G, F, k, 5, ind) time_5 = time.time() - start_time start_time = time.time() - c = oc.fast_search(G, F, k, 20, ind) + oc.fast_search(G, F, k, 20, ind) time_20 = time.time() - start_time start_time = time.time() - c = oc.fast_search(G, F, k, 50, ind) + oc.fast_search(G, F, k, 50, ind) time_50 = time.time() - start_time res_t.append([time_slow, time_5, time_20, time_50]) @@ -153,7 +152,6 @@ def noise_acc_experiment(noise, size, sparsity, energy, balance, num): c = oc.one_d_search(G, F, k, ind) acc_slow = c["energy"] - L = nx.laplacian_matrix(G) c = oc.fast_search(G, F, k, 5, ind) acc_5 = c["energy"] @@ -305,14 +303,14 @@ def plot_noise_acc_experiment(results, noise, output_file_name): ax = plt.subplot(111) ncol = 2 width = 0.04 # the width of the bars) - rects1 = ax.bar(numpy.array(noise) - 2 * width, results[:, 0], - width, color='cyan', label="SWT") - rects2 = ax.bar(numpy.array(noise) - width, - results[:, 1], width, color='orangered', label="FSWT-5") - rects3 = ax.bar(numpy.array(noise), results[:, 2], - width, color='darkgreen', label="FSWT-20") - rects4 = ax.bar(numpy.array(noise) + width, results[:, 3], - width, color='k', label="FSWT-50") + ax.bar(numpy.array(noise) - 2 * width, results[:, 0], + width, color='cyan', label="SWT") + ax.bar(numpy.array(noise) - width, + results[:, 1], width, color='orangered', label="FSWT-5") + ax.bar(numpy.array(noise), results[:, 2], + width, color='darkgreen', label="FSWT-20") + ax.bar(numpy.array(noise) + width, results[:, 3], + width, color='k', label="FSWT-50") plt.gcf().subplots_adjust(bottom=0.15) ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) @@ -341,14 +339,14 @@ def plot_energy_acc_experiment(results, energy, output_file_name): ind = numpy.array(list(range(4))) ax = plt.subplot(111) width = 0.2 # the width of the bars) - rects1 = ax.bar(ind - width, results[:, 0], - width, color='cyan', label="SWT", log=True) - rects2 = ax.bar(ind, results[:, 1], - width, color='orangered', label="FSWT-5", log=True) - rects3 = ax.bar(ind + width, results[:, 2], - width, color='darkgreen', label="FSWT-20", log=True) - rects4 = ax.bar(ind + 2 * width, results[:, 3], - width, color='k', label="FSWT-50", log=True) + ax.bar(ind - width, results[:, 0], + width, color='cyan', label="SWT", log=True) + ax.bar(ind, results[:, 1], + width, color='orangered', label="FSWT-5", log=True) + ax.bar(ind + width, results[:, 2], + width, color='darkgreen', label="FSWT-20", log=True) + ax.bar(ind + 2 * width, results[:, 3], + width, color='k', label="FSWT-50", log=True) plt.gcf().subplots_adjust(bottom=0.15) ax.legend(loc='upper left', prop={'size': 20}, ncol=ncol) From cfbc481eb10b6ac3bdc2c35d8e3469591af52a76 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 22:16:57 +0200 Subject: [PATCH 28/62] Make graph_signal_proc and optimal_cut slimmer --- lib/graph_signal_proc.py | 349 +-------------------------------------- lib/optimal_cut.py | 42 +---- 2 files changed, 6 insertions(+), 385 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 03667d0..9e158a6 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -1,6 +1,4 @@ import math -import random -import sys from collections import deque import networkx as nx @@ -10,7 +8,6 @@ import scipy.optimize from scipy import linalg import scipy.fftpack -from sklearn.preprocessing import normalize def compute_eigenvectors_and_eigenvalues(L): @@ -32,26 +29,14 @@ def compute_eigenvectors_and_eigenvalues(L): return U, lamb -def s(x): - """ - Cubic spline, see Hammond, D. K.,Vandergheynst, P., & Gribonval, R. - (2011). "Wavelets on graphs via spectral graph theory". - Input: - * x - Output: - * spline(x) - """ - return -5 + 11 * x - 6 * pow(x, 2) + pow(x, 3) - - def g(x): """ Wavelet generating kernel, see Hammond, D. K.,Vandergheynst, P., & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory". Input: - * x + * x Output: - * kernel of x + * kernel of x """ a = 2 b = 2 @@ -61,7 +46,7 @@ def g(x): if x < x_1: return pow(x_1, -a) * pow(x, a) elif x <= x_2 and x >= x_1: - return s(x) + return -5 + 11 * x - 6 * pow(x, 2) + pow(x, 3) else: return pow(x_2, b) * pow(x, -b) @@ -310,23 +295,6 @@ def add_child(self, obj): self.count = self.count + obj.count -def get_leaves(tree, part, G): - """ - Recursively gets all the leaves of a given tree. - Input: - * tree: tree node - * part: list that will contain children - * G: graph - Output: - * None - """ - if tree.data is not None: - part.append(tree.data) - else: - for c in tree.children: - get_leaves(c, part, G) - - def set_counts(tree): """ Sets counts for intermediate nodes in the tree. @@ -348,227 +316,6 @@ def set_counts(tree): return count -def partitions_level_rec(tree, level, G, l, partitions): - """ - Recursively extracts partitions from the current level "l" - up to a certain level "level" in the tree. - Input: - * tree: tree - * level: max level - * G: graph - * l: current level - * partitions: list of partitions recovered - Output: - None - """ - # Stopping condition - if l >= level: - part = [] - get_leaves(tree, part, G) - if len(part) > 0: - partitions.append(part) - else: - if tree.data is None: - for c in tree.children: - partitions_level_rec(c, level, G, l + 1, partitions) - else: - partitions.append([tree.data]) - - -def partitions_level(tree, level, G): - """ - Recovers partitions up to a certain level of the three. - Input: - * tree: tree - * level: level - * G: graph - Output: - * partitions: set of vertices in each partition - """ - partitions = [] - partitions_level_rec(tree, level, G, 0, partitions) - - return partitions - - -def distance_matrix(G, ind): - """ - Builds graph distance matrix according to an index - Input: - * G: graph - * ind: dictionary vertex: unique integer - Output: - * M: matrix - """ - M = [] - dists = nx.all_pairs_dijkstra_path_length(G) - - M = np.zeros((len(G.nodes()), len(G.nodes()))) - - for v1 in G.nodes(): - for v2 in G.nodes(): - M[ind[v1]][ind[v2]] = dists[v1][v2] - - return M - - -def select_centroids(M, radius): - """ - Selects half of the vertices as centroids. - Input: - * M - * radius - Output: - * centroids - """ - nodes = list(range(M.shape[0])) - random.shuffle(nodes) - nodes = nodes[:int(len(nodes) / 2)] - cents = [nodes[0]] - mn = sys.float_info.min - - for candidate_cent in nodes[1:]: - add = True - for cent in cents: - # If the candidate centroid is too close to a centroid - # it is not added to the list of centroids - if M[cent][candidate_cent] <= radius * mn: - add = False - break - if add: - cents.append(candidate_cent) - - return cents - - -def coarse_matrix(M, H, cents, nodes): - """ - Makes matrix coarser based on centroids. - Input: - * M: distance matrix - * H: - * cents: centroids - * nodes: list of nodes - Output: - * Q: new matrix - * J - * new_nodes: new node list - """ - Q = np.zeros((len(cents), len(cents))) - J = [] - assigns = [] - new_nodes = [] - - for i in range(len(cents)): - J.append([]) - assigns.append([]) - new_nodes.append(Node(None)) - - for i in range(M.shape[0]): - min_dist = M[i][cents[0]] - min_cent = 0 - - for j in range(1, len(cents)): - if M[i][cents[j]] < min_dist: - min_dist = M[i][cents[j]] - min_cent = j - - J[min_cent].append(H[i]) - assigns[min_cent].append(i) - new_nodes[min_cent].add_child(nodes[i]) - - for i in range(len(cents)): - if len(new_nodes[i].children) == 1: - new_nodes[i] = new_nodes[i].children[0] - - for j in range(len(cents)): - if i != j: - for m in assigns[i]: - for k in assigns[j]: - Q[i][j] = Q[i][j] + pow(M[m][k], 2) - - Q = normalize(Q, axis=1, norm='l1') - - return Q, J, new_nodes - - -def get_partitions(x, node_list): - """ - Gets partitions given indicator vector. - if x < 0: partition 1 - if x => 0: partition 2 - Input: - * x: indicator vector - * node_list: list of nodes - Output: - * P1: partition 1 - * P2: partition 2 - """ - P1 = [] - P2 = [] - - for i in range(x.shape[0]): - if x[i] < 0: - P1.append(node_list[i]) - else: - P2.append(node_list[i]) - - return P1, P2 - - -def get_new_laplacians(L, P1, P2, ind): - """ - Compute new Laplacian matrices for partitions P1 and P2. - Input: - * L: Higher-level laplacian - * P1: partition 1 - * P2: partition 2 - * ind: node index vertex: unique integer - Output: - * L1: Laplacian P1 - * L2: Laplacian P2 - """ - data = [] - row = [] - col = [] - - for i in range(len(P1)): - d = 0 - for j in range(len(P1)): - if i != j and L[ind[P1[i]], ind[P1[j]]] != 0: - row.append(i) - col.append(j) - data.append(float(L[ind[P1[i]], ind[P1[j]]])) - d = d - L[ind[P1[i]], ind[P1[j]]] - - row.append(i) - col.append(i) - data.append(float(d)) - - L1 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P1), len(P1))) - - data = [] - row = [] - col = [] - - for i in range(len(P2)): - d = 0 - for j in range(len(P2)): - if i != j and L[ind[P2[i]], ind[P2[j]]] != 0: - row.append(i) - col.append(j) - data.append(float(L[ind[P2[i]], ind[P2[j]]])) - d = d - L[ind[P2[i]], ind[P2[j]]] - - row.append(i) - col.append(i) - data.append(float(d)) - - L2 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P2), len(P2))) - - return L1, L2 - - def laplacian_complete(n): """ Laplacian of a complete graph with n vertices. @@ -585,22 +332,6 @@ def laplacian_complete(n): return C -def sqrtm(mat): - """ - Matrix square root. - Input: - * mat: matrix - Output: - * matrix square root - """ - eigvals, eigvecs = eigh(mat) - - eigvecs = eigvecs[:, eigvals > 0] - eigvals = eigvals[eigvals > 0] - - return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T)) - - def sqrtmi(mat): """ Computes the square-root inverse of a matrix. @@ -706,25 +437,6 @@ def ratio_cut(G): return np.array(x) -def eig_vis_rc(G): - """ - Uses the second and third eigenvectors of the graph - Laplacian for visualization. - Input: - * G: Graph - Output: - * x1: Second eigenvector - * x2: Third eigenvector - """ - L = nx.laplacian_matrix(G).todense() - (eigvals, eigvecs) = scipy.linalg.eigh(L, eigvals=(1, 2)) - - x1 = np.asarray(eigvecs[:, 0]) - x2 = np.asarray(eigvecs[:, 1]) - - return x1, x2 - - def get_subgraphs(G, cut): """ Compute subgraphs generated by a cut. @@ -813,38 +525,6 @@ def ratio_cut_hierarchy(G): return root, ind -def gavish_hierarchy(G, radius): - """ - Builds Gavish's hierarchy of a graph. - Input: - * G: graph - * radius: radius - Output: - * tree root - * ind: vertex index v: unique integer - """ - H = [] - nodes = [] - ind = {} - i = 0 - for v in G.nodes(): - ind[v] = i - nodes.append(Node(i)) - H.append(i) - i = i + 1 - - M = distance_matrix(G, ind) - - while M.shape[0] > 1: - cents = select_centroids(M, radius) - Q, J, new_nodes = coarse_matrix(M, H, cents, nodes) - M = Q - H = J - nodes = new_nodes - - return nodes[0], ind - - def compute_coefficients(tree, F): """ Computes tree coefficients for Gavish's transform. @@ -945,29 +625,6 @@ def get_coefficients(tree, wtr): Q.append(node.children[i]) -def get_cut_sizes(tree): - """ - Recovers cut sizes from tree. - Input: - * tree - Output: - * None - """ - Q = deque() - cut_sizes = [] - - Q.append(tree) - - while len(Q) > 0: - node = Q.popleft() - cut_sizes.append(node.cut) - - for i in range(len(node.children)): - Q.append(node.children[i]) - - return cut_sizes - - def set_coefficients(tree, wtr): """ Sets wavelet tree coefficients. diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 638c34a..cd61881 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -117,7 +117,8 @@ def weighted_adjacency_complete(G, F, ind): def fast_cac(G, F, ind): """ - Computes product C*A*C, where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix. + Computes product C*A*C, where C is the Laplacian of a complete graph + and A is a pairwise squared difference matrix. Input: * G: graph * F: graph signal @@ -193,40 +194,6 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): return res -def eig_vis_opt(G, F, beta): - """ - Computes first and second eigenvector of - sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. - Input: - * G: graph - * F: graph signal - * beta: regularization parameter - Output: - * v1: first eigenvector - * v2: second eigenvector - """ - ind = {} - i = 0 - - for v in G.nodes(): - ind[v] = i - i = i + 1 - - C = laplacian_complete(nx.number_of_nodes(G)) - A = weighted_adjacency_complete(G, F, ind) - CAC = np.dot(np.dot(C, A), C) - L = nx.laplacian_matrix(G).todense() - - isqrtCL = gsp.sqrtmi(C + beta * L) - M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) - - (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 1)) - x1 = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] - x2 = np.asarray(np.dot(eigvecs[:, 1], isqrtCL))[0, :] - - return x1, x2 - - def trans(L, min_v, max_v): """ Chebyshev polynomial translation. @@ -486,10 +453,7 @@ def get_subgraphs(G, cut): P2.append(v) i = i + 1 - G1 = G.subgraph(P1) - x: indicator vector - - size: number of edges cut - - score: cut score - + G1 = G.subgraph(P1) G2 = G.subgraph(P2) return G1, G2 From 78081b05cd0b1a11667126427a6ec1fbb8dd3521 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 22:44:51 +0200 Subject: [PATCH 29/62] Make graph_signal_proc and optimal_cut slimmer --- lib/graph_signal_proc.py | 16 ---------------- lib/optimal_cut.py | 30 +----------------------------- 2 files changed, 1 insertion(+), 45 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 9e158a6..925545c 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -316,22 +316,6 @@ def set_counts(tree): return count -def laplacian_complete(n): - """ - Laplacian of a complete graph with n vertices. - Input: - * n: size - Output: - * C: Laplacian - """ - C = np.ones((n, n)) - C = -1 * C - D = np.diag(np.ones(n)) - C = (n) * D + C - - return C - - def sqrtmi(mat): """ Computes the square-root inverse of a matrix. diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index cd61881..7508a60 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -431,34 +431,6 @@ def one_d_search(G, F, k, ind): return resab -def get_subgraphs(G, cut): - """ - Compute subgraphs generated by a cut. - Input: - * G: Original graph - * cut: cut indicator vector - Output: - * G1: subgraph 1 - * G2: subgraph 2 - """ - G1 = nx.Graph() - G2 = nx.Graph() - i = 0 - P1 = [] - P2 = [] - for v in G.nodes(): - if cut[i] < 0: - P1.append(v) - else: - P2.append(v) - i = i + 1 - - G1 = G.subgraph(P1) - G2 = G.subgraph(P2) - - return G1, G2 - - def optimal_wavelet_basis(G, F, k, npol): """ Computation of optimal graph wavelet basis. @@ -510,7 +482,7 @@ def optimal_wavelet_basis(G, F, k, npol): break else: # Compute cut on left and right side - (G1, G2) = get_subgraphs(best_cut["graph"], best_cut["x"]) + (G1, G2) = gsp.get_subgraphs(best_cut["graph"], best_cut["x"]) best_cut["parent"].cut = best_cut["size"] size = size + best_cut["size"] From 34de5f002c463e72700f20616ec850a65fb53f47 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 22:59:08 +0200 Subject: [PATCH 30/62] Fix imports in lib/static.py --- lib/static.py | 89 +++++++++++++++++++++++++-------------------------- 1 file changed, 43 insertions(+), 46 deletions(-) diff --git a/lib/static.py b/lib/static.py index 8a387ad..ad6c64d 100644 --- a/lib/static.py +++ b/lib/static.py @@ -1,21 +1,11 @@ -import networkx import math -import scipy.optimize -import numpy -import sys -from scipy import linalg -import matplotlib.pyplot as plt -from IPython.display import Image -import pywt -import scipy.fftpack -import random import operator -import copy -from collections import deque -from sklearn.preprocessing import normalize -from sklearn.cluster import SpectralClustering -from lib.graph_signal_proc import * -from lib.optimal_cut import * + +import numpy as np +import networkx as nx + +import lib.graph_signal_proc as gsp +import lib.optimal_cut as oc class Fourier(object): @@ -28,19 +18,19 @@ def name(self): def set_graph(self, _G): self.G = _G - L = networkx.laplacian_matrix(self.G) + L = nx.laplacian_matrix(self.G) L = L.todense() - self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L) + self.U, self.lamb_str = gsp.compute_eigenvectors_and_eigenvalues(L) def transform(self, F): """ """ - return graph_fourier(F, self.U) + return gsp.graph_fourier(F, self.U) def inverse(self, ftr): """ """ - return graph_fourier_inverse(ftr, self.U) + return gsp.graph_fourier_inverse(ftr, self.U) def drop_frequency(self, ftr, n): """ @@ -56,9 +46,10 @@ def drop_frequency(self, ftr, n): for i in range(ftr.shape[0]): coeffs[i] = abs(ftr[i]) - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), + reverse=True) - ftr_copy = numpy.copy(ftr) + ftr_copy = np.copy(ftr) for k in range(n, len(sorted_coeffs)): i = sorted_coeffs[k][0] @@ -80,30 +71,30 @@ def set_graph(self, _G): """ """ self.G = _G - L = networkx.normalized_laplacian_matrix(self.G) + L = nx.normalized_laplacian_matrix(self.G) L = L.todense() - self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L) + self.U, self.lamb_str = gsp.compute_eigenvectors_and_eigenvalues(L) lamb_max = max(self.lamb_str.real) # default parameters defined by author K = 100 J = 4 - gamma = comp_gamma() - self.T = comp_scales(lamb_max, K, J) - self.w = graph_wavelets(self.lamb_str.real, self.U.real, - len(self.G.nodes()), self.T) - self.s = graph_low_pass(self.lamb_str.real, self.U.real, - self.T, gamma, lamb_max, K) + gamma = gsp.comp_gamma() + self.T = gsp.comp_scales(lamb_max, K, J) + self.w = gsp.graph_wavelets(self.lamb_str.real, self.U.real, + len(self.G.nodes()), self.T) + self.s = gsp.graph_low_pass(self.lamb_str.real, self.U.real, + self.T, gamma, lamb_max, K) def transform(self, F): """ """ - return hammond_wavelet_transform(self.w, self.s, self.T, F) + return gsp.hammond_wavelet_transform(self.w, self.s, self.T, F) def inverse(self, wtr): """ """ - return hammond_wavelets_inverse(self.w, self.s, wtr) + return gsp.hammond_wavelets_inverse(self.w, self.s, wtr) def drop_frequency(self, wtr, n): """ @@ -119,9 +110,10 @@ def drop_frequency(self, wtr, n): for j in range(len(wtr[i])): coeffs[(i, j)] = abs(wtr[i][j]) - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), + reverse=True) - wtr_copy = numpy.copy(wtr) + wtr_copy = np.copy(wtr) for k in range(n, len(sorted_coeffs)): i = sorted_coeffs[k][0][0] @@ -144,17 +136,17 @@ def set_graph(self, _G): """ """ self.G = _G - (self.tree, self.ind) = ratio_cut_hierarchy(self.G) + (self.tree, self.ind) = gsp.ratio_cut_hierarchy(self.G) def transform(self, F): """ """ - return gavish_wavelet_transform(self.tree, self.ind, self.G, F) + return gsp.gavish_wavelet_transform(self.tree, self.ind, self.G, F) def inverse(self, wtr): """ """ - return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) + return gsp.gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) def drop_frequency(self, wtr, n): """ @@ -169,9 +161,10 @@ def drop_frequency(self, wtr, n): for i in range(len(wtr)): coeffs[i] = abs(wtr[i]) - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), + reverse=True) - wtr_copy = numpy.copy(wtr) + wtr_copy = np.copy(wtr) for k in range(n, len(sorted_coeffs)): i = sorted_coeffs[k][0] @@ -211,27 +204,31 @@ def transform(self, F): def inverse(self, wtr): """ """ - return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) + return gsp.gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) def drop_frequency(self, wtr, n): coeffs = {} k = n # Computing optimal basis - (self.tree, self.ind, s) = optimal_wavelet_basis(self.G, self.F, k, self.n) + (self.tree, self.ind, s) = oc.optimal_wavelet_basis(self.G, self.F, + k, self.n) # Gavish's wavelet transform - tr = gavish_wavelet_transform(self.tree, self.ind, self.G, self.F) + tr = gsp.gavish_wavelet_transform(self.tree, self.ind, self.G, self.F) for i in range(len(tr)): coeffs[i] = abs(tr[i]) - sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) + sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), + reverse=True) - wtr_copy = numpy.copy(tr) + wtr_copy = np.copy(tr) - # Computing number of integers required to represent the edges cut (rounded) - v = n - int(math.ceil(float(s * math.log2(len(self.G.edges()))) / 64)) + # Computing number of integers required to represent the + # edges cut (rounded) + v = n - int(math.ceil(float(s * + math.log2(len(self.G.edges()))) / 64)) for k in range(v, len(sorted_coeffs)): i = sorted_coeffs[k][0] From bfc51500d01b5d89ef3fcceeb5af155402260aad Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Mon, 4 Sep 2017 23:16:44 +0200 Subject: [PATCH 31/62] Fix imports in lib/io.py and lib/vis.py --- lib/io.py | 274 ++++++++++++++++++++--------------------- lib/vis.py | 356 +++++++++++++++++++++++++++-------------------------- 2 files changed, 313 insertions(+), 317 deletions(-) diff --git a/lib/io.py b/lib/io.py index 10f5c72..e7125a1 100644 --- a/lib/io.py +++ b/lib/io.py @@ -1,154 +1,142 @@ -import networkx -import math -import scipy.optimize -import numpy -import sys -from scipy import linalg -import matplotlib.pyplot as plt -from IPython.display import Image -import pywt -import scipy.fftpack -import random -import operator -import copy -from collections import deque -from sklearn.preprocessing import normalize -from sklearn.cluster import SpectralClustering +import networkx as nx +import numpy as np import pandas as pd -from datetime import datetime, date, time, timedelta import statsmodels.api as sm def read_graph(input_graph_name, input_data_name): - """ - Reads graph from file. - Input: - * input_graph_name: csv edge list - * input_data_name: csv node-value pairs - Output: - * networkx graph - """ - - #Reading input data - input_data = open(input_data_name, 'r') - values = {} - - for line in input_data: - line = line.rstrip() - vec = line.rsplit(',') - - vertex = vec[0] - value = float(vec[1]) - values[vertex] = value - - input_data.close() - - #Reading graph data - G = networkx.Graph() - input_graph = open(input_graph_name, 'r') - - for line in input_graph: - line = line.rstrip() - vec = line.rsplit(',') - v1 = vec[0] - v2 = vec[1] - - if v1 in values and v2 in values: - G.add_edge(v1,v2, weight=1.) - - #Extracting largest connected component from graph - Gcc=sorted(networkx.connected_component_subgraphs(G), key = len, reverse=True) - - G = Gcc[0] - - values_in_graph = {} - - #Setting values as node attributes - for v in values.keys(): - if v in G: - values_in_graph[v] = values[v] - - input_graph.close() - networkx.set_node_attributes(G, "value", values_in_graph) - - return G + """ + Reads graph from file. + Input: + * input_graph_name: csv edge list + * input_data_name: csv node-value pairs + Output: + * networkx graph + """ + + # Reading input data + input_data = open(input_data_name, 'r') + values = {} + + for line in input_data: + line = line.rstrip() + vec = line.rsplit(',') + + vertex = vec[0] + value = float(vec[1]) + values[vertex] = value + + input_data.close() + + # Reading graph data + G = nx.Graph() + input_graph = open(input_graph_name, 'r') + + for line in input_graph: + line = line.rstrip() + vec = line.rsplit(',') + v1 = vec[0] + v2 = vec[1] + + if v1 in values and v2 in values: + G.add_edge(v1, v2, weight=1.) + + # Extracting largest connected component from graph + Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True) + + G = Gcc[0] + + values_in_graph = {} + + # Setting values as node attributes + for v in values.keys(): + if v in G: + values_in_graph[v] = values[v] + + input_graph.close() + nx.set_node_attributes(G, "value", values_in_graph) + + return G + def read_values(input_data_name, G): - """ - Reads node values. - Input: - * input_data_name: csv node-value pairs - * G: networkx graph - Output: - * F: normalized node values array, ordered by G.nodes() - """ - D = {} - input_data = open(input_data_name, 'r') - - #Reading file - for line in input_data: - line = line.rstrip() - vec = line.rsplit(',') - - vertex = vec[0] - value = float(vec[1]) - D[vertex] = value - - input_data.close() - - F = [] - for v in G.nodes(): - if v in D: - F.append(float(D[v])) - else: - F.append(0.) - - #Normalization - F = numpy.array(F) - F = F / numpy.max(F) - F = F - numpy.mean(F) - - return F + """ + Reads node values. + Input: + * input_data_name: csv node-value pairs + * G: networkx graph + Output: + * F: normalized node values array, ordered by G.nodes() + """ + D = {} + input_data = open(input_data_name, 'r') + + # Reading file + for line in input_data: + line = line.rstrip() + vec = line.rsplit(',') + + vertex = vec[0] + value = float(vec[1]) + D[vertex] = value + + input_data.close() + + F = [] + for v in G.nodes(): + if v in D: + F.append(float(D[v])) + else: + F.append(0.) + + # Normalization + F = np.array(F) + F = F / np.max(F) + F = F - np.mean(F) + + return F -def read_dyn_graph(path, num_snapshots, G): - """ - Reads a dynamic graph. - Input: - * path: Path containing a files for each graph snapshot - (e.g. folder/traffic_, for files folder/traffic_0.data ... - folder/traffic_100.data) - * num_snapshots: number of snapshots - * G: networkx graph - Output: - * FT: array #snapshots x #vertices - - """ - FT = [] - for t in range(num_snapshots): - in_file = path + "_" + str(t) + ".data" - F = read_values(in_file, G) - FT.append(F) - - return numpy.array(FT) -def clean_traffic_data(FT): - start_time = datetime.strptime("1/04/11 00:00", "%d/%m/%y %H:%M") - c_FT = [] - for i in range(FT.shape[1]): - #removing daily seasonality - data = pd.DataFrame(FT[:,i], pd.DatetimeIndex(start='1/04/11 00:00', periods=len(FT[:,i]), freq='5min')) - data.interpolate(inplace=True) - - res = sm.tsa.seasonal_decompose(data.values, freq=288) - F = FT[:,i] - res.seasonal - - #removing weekly seasonality - data = pd.DataFrame(F, pd.DatetimeIndex(start='1/04/11 00:00', periods=len(FT[:,i]), freq='5min')) - res = sm.tsa.seasonal_decompose(data.values, freq=288*7) - F = F - res.seasonal - - c_FT.append(F) - - return numpy.array(c_FT).transpose() +def read_dyn_graph(path, num_snapshots, G): + """ + Reads a dynamic graph. + Input: + * path: Path containing a files for each graph snapshot + (e.g. folder/traffic_, for files folder/traffic_0.data ... + folder/traffic_100.data) + * num_snapshots: number of snapshots + * G: networkx graph + Output: + * FT: array #snapshots x #vertices + + """ + FT = [] + for t in range(num_snapshots): + in_file = path + "_" + str(t) + ".data" + F = read_values(in_file, G) + FT.append(F) + + return np.array(FT) +def clean_traffic_data(FT): + start_time = datetime.strptime("1/04/11 00:00", "%d/%m/%y %H:%M") + c_FT = [] + for i in range(FT.shape[1]): + # removing daily seasonality + data = pd.DataFrame(FT[:, i], pd.DatetimeIndex( + start='1/04/11 00:00', periods=len(FT[:, i]), freq='5min')) + data.interpolate(inplace=True) + + res = sm.tsa.seasonal_decompose(data.values, freq=288) + F = FT[:, i] - res.seasonal + + # removing weekly seasonality + data = pd.DataFrame(F, pd.DatetimeIndex( + start='1/04/11 00:00', periods=len(FT[:, i]), freq='5min')) + res = sm.tsa.seasonal_decompose(data.values, freq=288 * 7) + F = F - res.seasonal + + c_FT.append(F) + + return np.array(c_FT).transpose() diff --git a/lib/vis.py b/lib/vis.py index b5e9557..9b6fd08 100644 --- a/lib/vis.py +++ b/lib/vis.py @@ -1,197 +1,205 @@ -import networkx -import math -import scipy.optimize -import numpy import sys -from scipy import linalg -import matplotlib.pyplot as plt -from IPython.display import Image -import pywt -import scipy.fftpack -import random -import operator -import copy -from collections import deque -from sklearn.preprocessing import normalize -from sklearn.cluster import SpectralClustering import os -def set_f(G,F,ids=None): - if ids is None: - i = 0 - for v in G.nodes(): - G.node[v]["value"] = F[i] - i = i + 1 - else: - i = 0 - for v in range(len(ids)): - G.node[ids[v]]["value"] = F[i] - i = i + 1 +import numpy as np + + +def set_f(G, F, ids=None): + if ids is None: + i = 0 + for v in G.nodes(): + G.node[v]["value"] = F[i] + i = i + 1 + else: + i = 0 + for v in range(len(ids)): + G.node[ids[v]]["value"] = F[i] + i = i + 1 + def get_f(G): - F = [] - for v in G.nodes(): - F.append(G.node[v]["value"]) - - return numpy.array(F) + F = [] + for v in G.nodes(): + F.append(G.node[v]["value"]) + + return np.array(F) + + +def rgb_to_hex(r, g, b): + return '#%02x%02x%02x' % (r, g, b) -def rgb_to_hex(r,g,b): - return '#%02x%02x%02x' % (r,g,b) def rgb(minimum, maximum, value): - mi, ma = float(minimum), float(maximum) - ratio = 2 * (value-mi) / (ma - mi) - b = int(max(0, 255*(1 - ratio))) - r = int(max(0, 255*(ratio - 1))) - g = 255 - b - r - - return rgb_to_hex(r, g, b) - -def draw_graph_with_values(G, dot_output_file_name, maximum=None, minimum=None): - output_file = open(dot_output_file_name, 'w') - output_file.write("graph G{\n") - output_file.write("rankdir=\"LR\";\n") - output_file.write("size=\"10,2\";\n") - - if maximum is None: - maximum = -sys.float_info.max - minimum = sys.float_info.max - - for v in G.nodes(): - if G.node[v]["value"] > maximum: - maximum = G.node[v]["value"] - - if G.node[v]["value"] < minimum: - minimum = G.node[v]["value"] - - for v in G.nodes(): - color = rgb(minimum, maximum, G.node[v]["value"]) - if G.node[v]["value"] != 0.0: - output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n") - else: - output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"0\",fixedsize=true,width=\"1\",height=\"1\"];\n") - - for edge in G.edges(): - output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") - - - output_file.write("}") - - output_file.close() + mi, ma = float(minimum), float(maximum) + ratio = 2 * (value - mi) / (ma - mi) + b = int(max(0, 255 * (1 - ratio))) + r = int(max(0, 255 * (ratio - 1))) + g = 255 - b - r + + return rgb_to_hex(r, g, b) + + +def draw_graph_with_values(G, dot_output_file_name, maximum=None, + minimum=None): + output_file = open(dot_output_file_name, 'w') + output_file.write("graph G{\n") + output_file.write("rankdir=\"LR\";\n") + output_file.write("size=\"10,2\";\n") + + if maximum is None: + maximum = -sys.float_info.max + minimum = sys.float_info.max + + for v in G.nodes(): + if G.node[v]["value"] > maximum: + maximum = G.node[v]["value"] + + if G.node[v]["value"] < minimum: + minimum = G.node[v]["value"] + + for v in G.nodes(): + color = rgb(minimum, maximum, G.node[v]["value"]) + if G.node[v]["value"] != 0.0: + output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + + str(color) + "\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n") + else: + output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( + color) + "\",penwidth=\"0\",fixedsize=true,width=\"1\",height=\"1\"];\n") + + for edge in G.edges(): + output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + + "\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") + + output_file.write("}") + + output_file.close() + def draw_graph_dynamic_values(G, FT, fig_output_file_name): - maximum = -sys.float_info.max - minimum = sys.float_info.max - - for i in range(FT.shape[0]): - for j in range(FT.shape[1]): - if FT[i][j] > maximum: - maximum = FT[i][j] - - if FT[i][j] < minimum: - minimum = FT[i][j] - - svg_names = "" - - for i in range(FT.shape[0]): - set_f(G, FT[i]) - #draw_graph_with_values(G, "dyn_graph-"+str(i)+".dot", maximum, minimum) - draw_graph_with_values(G, "dyn_graph-"+str(i)+".dot") - os.system("sfdp -Goverlap=prism -Tsvg dyn_graph-"+str(i)+".dot > dyn_graph-"+str(i)+".svg") - os.system("rm dyn_graph-"+str(i)+".dot") - svg_names = svg_names + " dyn_graph-"+str(i)+".svg" - - os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 "+svg_names+" > "+fig_output_file_name) - - for i in range(FT.shape[0]): - os.system("rm dyn_graph-"+str(i)+".svg") - + maximum = -sys.float_info.max + minimum = sys.float_info.max + + for i in range(FT.shape[0]): + for j in range(FT.shape[1]): + if FT[i][j] > maximum: + maximum = FT[i][j] + + if FT[i][j] < minimum: + minimum = FT[i][j] + + svg_names = "" + + for i in range(FT.shape[0]): + set_f(G, FT[i]) + # draw_graph_with_values(G, "dyn_graph-"+str(i)+".dot", maximum, minimum) + draw_graph_with_values(G, "dyn_graph-" + str(i) + ".dot") + os.system("sfdp -Goverlap=prism -Tsvg dyn_graph-" + + str(i) + ".dot > dyn_graph-" + str(i) + ".svg") + os.system("rm dyn_graph-" + str(i) + ".dot") + svg_names = svg_names + " dyn_graph-" + str(i) + ".svg" + + os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 " + + svg_names + " > " + fig_output_file_name) + + for i in range(FT.shape[0]): + os.system("rm dyn_graph-" + str(i) + ".svg") + + def draw_partitions_with_values(G, partitions, dot_output_file_name, maximum=None, minimum=None): - output_file = open(dot_output_file_name, 'w') - output_file.write("graph G{\n") - output_file.write("rankdir=\"LR\";\n") - output_file.write("size=\"10,2\";\n") - - if maximum is None: - maximum = -sys.float_info.max - minimum = sys.float_info.max - - for v in G.nodes(): - if G.node[v]["value"] > maximum: - maximum = G.node[v]["value"] - - if G.node[v]["value"] < minimum: - minimum = G.node[v]["value"] - - part_map = {} - - for p in range(len(partitions)): - for i in range(len(partitions[p])): - part_map[partitions[p][i]] = p - - for v in G.nodes(): - color = rgb(minimum, maximum, G.node[v]["value"]) - if G.node[v]["value"] != 0.0: - output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"2\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n") - else: - output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"0\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n") - - for edge in G.edges(): - if part_map[edge[0]] == part_map[edge[1]]: - output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"4\"];\n") - else: - output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") - - output_file.write("}") - - output_file.close() + output_file = open(dot_output_file_name, 'w') + output_file.write("graph G{\n") + output_file.write("rankdir=\"LR\";\n") + output_file.write("size=\"10,2\";\n") + + if maximum is None: + maximum = -sys.float_info.max + minimum = sys.float_info.max + + for v in G.nodes(): + if G.node[v]["value"] > maximum: + maximum = G.node[v]["value"] + + if G.node[v]["value"] < minimum: + minimum = G.node[v]["value"] + + part_map = {} + + for p in range(len(partitions)): + for i in range(len(partitions[p])): + part_map[partitions[p][i]] = p + + for v in G.nodes(): + color = rgb(minimum, maximum, G.node[v]["value"]) + if G.node[v]["value"] != 0.0: + output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( + color) + "\",penwidth=\"2\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n") + else: + output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( + color) + "\",penwidth=\"0\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n") + + for edge in G.edges(): + if part_map[edge[0]] == part_map[edge[1]]: + output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + + "\"[dir=\"none\",color=\"black\",penwidth=\"4\"];\n") + else: + output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + + "\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") + + output_file.write("}") + + output_file.close() + def draw_graph(G, dot_output_file_name): - output_file = open(dot_output_file_name, 'w') - output_file.write("graph G{\n") - output_file.write("rankdir=\"LR\";\n") - output_file.write("size=\"10,2\";\n") + output_file = open(dot_output_file_name, 'w') + output_file.write("graph G{\n") + output_file.write("rankdir=\"LR\";\n") + output_file.write("size=\"10,2\";\n") - for v in G.nodes(): - color = rgb_to_hex(0,255,0) - output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n") + for v in G.nodes(): + color = rgb_to_hex(0, 255, 0) + output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( + color) + "\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n") - for edge in G.edges(): - output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") + for edge in G.edges(): + output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + + "\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") - - output_file.write("}") + output_file.write("}") + + output_file.close() - output_file.close() def draw_time_graph(G, fig_output_file_name): - svg_names = "" + svg_names = "" - for i in range(G.num_snaps()): - draw_graph(G.snap(i), "graph-"+str(i)+".dot") - os.system("sfdp -Goverlap=prism -Tsvg graph-"+str(i)+".dot > graph-"+str(i)+".svg") - os.system("rm graph-"+str(i)+".dot") - svg_names = svg_names + " graph-"+str(i)+".svg" - - os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 "+svg_names+" > "+fig_output_file_name) + for i in range(G.num_snaps()): + draw_graph(G.snap(i), "graph-" + str(i) + ".dot") + os.system("sfdp -Goverlap=prism -Tsvg graph-" + str(i) + ".dot > graph-" + str(i) + ".svg") + os.system("rm graph-" + str(i) + ".dot") + svg_names = svg_names + " graph-" + str(i) + ".svg" - for i in range(G.num_snaps()): - os.system("rm graph-"+str(i)+".svg") + os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 " + + svg_names + " > " + fig_output_file_name) -def draw_time_graph_eig(G, eig, fig_output_file_name): - svg_names = "" - G.set_values(eig) - maximum = numpy.max(eig) - minimum = numpy.min(eig) - - for i in range(G.num_snaps()): - draw_graph_with_values(G.snap(i),"graph-"+str(i)+".dot", minimum, maximum) - os.system("sfdp -Goverlap=prism -Tsvg graph-"+str(i)+".dot > graph-"+str(i)+".svg") - os.system("rm graph-"+str(i)+".dot") - svg_names = svg_names + " graph-"+str(i)+".svg" - - os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 "+svg_names+" > "+fig_output_file_name) - - for i in range(G.num_snaps()): - os.system("rm graph-"+str(i)+".svg") + for i in range(G.num_snaps()): + os.system("rm graph-" + str(i) + ".svg") + +def draw_time_graph_eig(G, eig, fig_output_file_name): + svg_names = "" + G.set_values(eig) + maximum = np.max(eig) + minimum = np.min(eig) + + for i in range(G.num_snaps()): + draw_graph_with_values(G.snap(i), "graph-" + str(i) + ".dot", minimum, maximum) + os.system("sfdp -Goverlap=prism -Tsvg graph-" + str(i) + ".dot > graph-" + str(i) + ".svg") + os.system("rm graph-" + str(i) + ".dot") + svg_names = svg_names + " graph-" + str(i) + ".svg" + + os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 " + + svg_names + " > " + fig_output_file_name) + + for i in range(G.num_snaps()): + os.system("rm graph-" + str(i) + ".svg") From b474b9bf18533ed00077d1fd826c850575d3ea43 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Tue, 5 Sep 2017 22:33:23 +0200 Subject: [PATCH 32/62] Make graph_low_pass and graph_wavelets faster --- lib/graph_signal_proc.py | 29 +++++------------------------ 1 file changed, 5 insertions(+), 24 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 925545c..ca9f3e4 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -118,22 +118,9 @@ def graph_low_pass(lamb, U, T, gamma, lamb_max, K): N = len(lamb) - h_vector_form = [h(T[-1] * lamb[x], gamma, lamb_max, K) for x in range(N)] + h_vector = [h(T[-1] * lamb[x], gamma, lamb_max, K) for x in range(N)] - s = [] - - for n in range(N): - s.append([]) - - for m in range(N): - s_n_m = 0 - - for x in range(N): - s_n_m = s_n_m + U[n][x] * U[m][x] * h_vector_form[x] - - s[n].append(s_n_m) - - return s + return np.dot(U, np.dot(np.diag(h_vector), U.T)) def graph_wavelets(lamb, U, N, T): @@ -147,18 +134,12 @@ def graph_wavelets(lamb, U, N, T): Output: * w: wavelets as a len(T) x N x N matrix """ + w = [] for t in range(len(T)): - w.append([]) - for n in range(N): - w[t].append([]) - for m in range(N): - w_t_n_m = 0 - for x in range(N): - w_t_n_m = w_t_n_m + U[n][x] * U[m][x] * g(T[t] * lamb[x]) - - w[t][n].append(w_t_n_m) + g_vector = [g(T[t] * lamb[x]) for x in range(N)] + w.append([np.dot(U, np.dot(np.diag(g_vector), U.T))]) return w From de743e157a733250a57c5dabf71962c087f022d4 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Wed, 6 Sep 2017 08:57:41 +0200 Subject: [PATCH 33/62] Make hammond_wavelet_transform faster --- lib/graph_signal_proc.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index ca9f3e4..9276f3d 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -195,17 +195,10 @@ def hammond_wavelet_transform(w, s, T, F): C = [] for i in range(len(T)): - C.append([]) # Each wavelet is represented by an N x N matrix - for j in range(len(F)): - dotp = np.dot(F, w[i][j]) - C[i].append(dotp) - - C.append([]) + C.append(np.dot(F, w[i].T)) # Append output of scaling function application at the end - for j in range(len(F)): - dotp = np.dot(F, s[j]) - C[-1].append(dotp) + C.append(np.dot(F, s.T)) return np.array(C) From 6355f699444c771d8823cd757155e1060e1636f1 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Wed, 6 Sep 2017 09:53:44 +0200 Subject: [PATCH 34/62] Fix error in hammond_wavelet_transform --- lib/graph_signal_proc.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 9276f3d..44b7124 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -116,9 +116,7 @@ def graph_low_pass(lamb, U, T, gamma, lamb_max, K): * s: Low-pass filter as a N x N matrix """ - N = len(lamb) - - h_vector = [h(T[-1] * lamb[x], gamma, lamb_max, K) for x in range(N)] + h_vector = [h(T[-1] * l, gamma, lamb_max, K) for l in lamb] return np.dot(U, np.dot(np.diag(h_vector), U.T)) @@ -138,10 +136,10 @@ def graph_wavelets(lamb, U, N, T): w = [] for t in range(len(T)): - g_vector = [g(T[t] * lamb[x]) for x in range(N)] - w.append([np.dot(U, np.dot(np.diag(g_vector), U.T))]) + g_vector = [g(T[t] * l) for l in lamb] + w.append(np.dot(U, np.dot(np.diag(g_vector), U.T))) - return w + return np.asarray(w) def graph_fourier(F, U): @@ -200,7 +198,7 @@ def hammond_wavelet_transform(w, s, T, F): # Append output of scaling function application at the end C.append(np.dot(F, s.T)) - return np.array(C) + return np.asarray(C) def hammond_wavelets_inverse(w, s, C): From 8fced1e18ec062ba572b6ae7ce33f0096d8be654 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Wed, 6 Sep 2017 15:15:55 +0200 Subject: [PATCH 35/62] Change code in hammond_wavelets_inverse --- lib/graph_signal_proc.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 44b7124..5f64c43 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -212,17 +212,9 @@ def hammond_wavelets_inverse(w, s, C): Output: * F: Reconstructed signal in the vertex domain """ - w = np.array(w) + nC = np.ravel(C) Wc = np.append(w, np.array([s]), axis=0) - # Creates copies of the inputs - nWc = Wc[0, :, :] - nC = C[0] - for i in range(1, Wc.shape[0]): - nWc = np.append(nWc, Wc[i, :, :], axis=0) - nC = np.append(nC, C[i], axis=0) - - nWc = np.array(nWc) - nC = np.array(nC) + nWc = Wc.reshape(Wc.shape[0] * Wc.shape[1], Wc.shape[2]) # Search a least square solution F, solving: # nWc F = nC F = np.linalg.lstsq(nWc, nC)[0] From c7e5058426d08cdeb6441acf048b85571f95e0d4 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Thu, 7 Sep 2017 16:41:23 +0200 Subject: [PATCH 36/62] Clean compression-experiments.ipynb --- compression-experiments.ipynb | 129 +++++++++++++++++++--------------- 1 file changed, 73 insertions(+), 56 deletions(-) diff --git a/compression-experiments.ipynb b/compression-experiments.ipynb index 106e1ff..2047526 100644 --- a/compression-experiments.ipynb +++ b/compression-experiments.ipynb @@ -17,33 +17,20 @@ { "cell_type": "code", "execution_count": 79, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "import networkx\n", - "import math\n", - "import scipy.optimize\n", "import numpy as np\n", "import sys\n", - "from scipy import linalg\n", - "import matplotlib.pyplot as plt\n", + "\n", "from IPython.display import Image\n", - "import pywt\n", - "import scipy.fftpack\n", - "import random\n", - "import operator\n", - "import copy\n", - "from collections import deque\n", - "from sklearn.preprocessing import normalize\n", - "from sklearn.cluster import SpectralClustering\n", - "from matplotlib.lines import Line2D\n", - "from lib.io import *\n", - "from lib.vis import *\n", - "from lib.graph_signal_proc import *\n", - "from lib.syn import *\n", - "from lib.experiments import *\n", - "from lib.static import *\n", - "from lib.datasets import *" + "\n", + "import lib.io as io\n", + "import lib.experiments as exp\n", + "import lib.static as static\n", + "import lib.datasets as data" ] }, { @@ -56,11 +43,14 @@ { "cell_type": "code", "execution_count": 31, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "G = read_graph(small_traffic[\"path\"] + \"traffic.graph\", small_traffic[\"path\"] + \"traffic.data\")\n", - "F = read_values(small_traffic[\"path\"] + \"traffic.data\", G) " + "G = io.read_graph(data.small_traffic[\"path\"] + \"traffic.graph\",\n", + " data.small_traffic[\"path\"] + \"traffic.data\")\n", + "F = io.read_values(data.small_traffic[\"path\"] + \"traffic.data\", G)" ] }, { @@ -85,14 +75,18 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "algs = [OptWavelets(n=20), OptWavelets(), GRCWavelets(), Fourier(), HWavelets()]\n", + "algs = [static.OptWavelets(n=20), static.OptWavelets(), static.GRCWavelets(),\n", + " static.Fourier(), static.HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_smt, time_smt = compression_experiment_static(G, np.array(F), algs, comp_ratios, 10)" + "res_smt, time_smt = exp.compression_experiment_static(G, np.array(F),\n", + " algs, comp_ratios, 10)" ] }, { @@ -113,7 +107,8 @@ } ], "source": [ - "plot_compression_experiments(res_smt, comp_ratios, \"figs/compression_small_traffic.png\", 4)\n", + "exp.plot_compression_experiments(res_smt, comp_ratios,\n", + " \"figs/compression_small_traffic.png\", 4)\n", "Image(filename=\"figs/compression_small_traffic.png\")" ] }, @@ -183,11 +178,14 @@ { "cell_type": "code", "execution_count": 33, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "G = read_graph(traffic[\"path\"] + \"traffic.graph\", traffic[\"path\"] + \"traffic.data\")\n", - "F = read_values(traffic[\"path\"] + \"traffic.data\", G) " + "G = io.read_graph(data.traffic[\"path\"] + \"traffic.graph\",\n", + " data.traffic[\"path\"] + \"traffic.data\")\n", + "F = io.read_values(data.traffic[\"path\"] + \"traffic.data\", G)" ] }, { @@ -212,14 +210,16 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_t, time_t = compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_t, time_t = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" ] }, { @@ -240,7 +240,8 @@ } ], "source": [ - "plot_compression_experiments(res_t, comp_ratios, \"figs/compression_traffic.png\", 10)\n", + "exp.plot_compression_experiments(res_t, comp_ratios,\n", + " \"figs/compression_traffic.png\", 10)\n", "Image(filename=\"figs/compression_traffic.png\")" ] }, @@ -282,11 +283,14 @@ { "cell_type": "code", "execution_count": 61, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "G = read_graph(human[\"path\"] + \"human.graph\", human[\"path\"] + \"human.data\")\n", - "F = read_values(human[\"path\"] + \"human.data\", G) " + "G = io.read_graph(data.human[\"path\"] + \"human.graph\",\n", + " data.human[\"path\"] + \"human.data\")\n", + "F = io.read_values(data.human[\"path\"] + \"human.data\", G)" ] }, { @@ -311,14 +315,16 @@ { "cell_type": "code", "execution_count": 63, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_h, time_h = compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_h, time_h = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" ] }, { @@ -339,7 +345,8 @@ } ], "source": [ - "plot_compression_experiments(res_h, comp_ratios, \"figs/compression_human.png\", 100.)\n", + "exp.plot_compression_experiments(res_h, comp_ratios,\n", + " \"figs/compression_human.png\", 100.)\n", "Image(filename=\"figs/compression_human.png\")" ] }, @@ -386,21 +393,24 @@ }, "outputs": [], "source": [ - "G = read_graph(wiki[\"path\"] + \"wiki.graph\", wiki[\"path\"] + \"wiki.data\")\n", - "F = read_values(wiki[\"path\"] + \"wiki.data\", G) " + "G = io.read_graph(data.wiki[\"path\"] + \"wiki.graph\",\n", + " data.wiki[\"path\"] + \"wiki.data\")\n", + "F = io.read_values(data.wiki[\"path\"] + \"wiki.data\", G)" ] }, { "cell_type": "code", "execution_count": 42, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "sys.setrecursionlimit(G.number_of_nodes())\n", - "res_w, time_w = compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_w, time_w = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" ] }, { @@ -421,7 +431,8 @@ } ], "source": [ - "plot_compression_experiments(res_w, comp_ratios, \"figs/compression_wiki.png\", 20.)\n", + "exp.plot_compression_experiments(res_w, comp_ratios,\n", + " \"figs/compression_wiki.png\", 20.)\n", "Image(filename=\"figs/compression_wiki.png\")" ] }, @@ -456,11 +467,14 @@ { "cell_type": "code", "execution_count": 47, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "G = read_graph(polblogs[\"path\"] + \"polblogs.graph\", polblogs[\"path\"] + \"polblogs.data\")\n", - "F = read_values(polblogs[\"path\"] + \"polblogs.data\", G) " + "G = io.read_graph(data.polblogs[\"path\"] + \"polblogs.graph\",\n", + " data.polblogs[\"path\"] + \"polblogs.data\")\n", + "F = io.read_values(data.polblogs[\"path\"] + \"polblogs.data\", G)" ] }, { @@ -485,14 +499,16 @@ { "cell_type": "code", "execution_count": 49, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_b, time_b = compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_b, time_b = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" ] }, { @@ -513,7 +529,8 @@ } ], "source": [ - "plot_compression_experiments(res_b, comp_ratios, \"figs/compression_blog.png\", 4000.)\n", + "exp.plot_compression_experiments(res_b, comp_ratios,\n", + " \"figs/compression_blog.png\", 4000.)\n", "Image(filename=\"figs/compression_blog.png\")" ] }, From 5441ccbdb7168df95495432b439def069351edb9 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Thu, 7 Sep 2017 16:51:03 +0200 Subject: [PATCH 37/62] Clean synthetic-data.ipynb --- synthetic-data.ipynb | 67 +++++++++++++++++++++----------------------- 1 file changed, 32 insertions(+), 35 deletions(-) diff --git a/synthetic-data.ipynb b/synthetic-data.ipynb index 2984f86..ec98299 100644 --- a/synthetic-data.ipynb +++ b/synthetic-data.ipynb @@ -17,33 +17,17 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "import networkx\n", - "import math\n", - "import scipy.optimize\n", "import numpy as np\n", - "import sys\n", - "from scipy import linalg\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import Image\n", - "import pywt\n", - "import scipy.fftpack\n", "import random\n", - "import operator\n", - "import copy\n", - "from collections import deque\n", - "from sklearn.preprocessing import normalize\n", - "from sklearn.cluster import SpectralClustering\n", - "from matplotlib.lines import Line2D\n", - "from lib.io import *\n", - "from lib.vis import *\n", - "from lib.graph_signal_proc import *\n", - "from lib.syn import *\n", - "from lib.experiments import *\n", - "from lib.static import *\n", - "from lib.datasets import *" + "\n", + "from IPython.display import Image\n", + "\n", + "import lib.experiments as exp" ] }, { @@ -56,7 +40,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "sizes = range(200, 1001, 200)\n", @@ -68,7 +54,7 @@ "energy = 100\n", "random.seed(3)\n", "np.random.seed(7)\n", - "res_t = size_time_experiment(sizes, balance, sparsity, energy, noise, num)" + "res_t = exp.size_time_experiment(sizes, balance, sparsity, energy, noise, num)" ] }, { @@ -89,7 +75,8 @@ } ], "source": [ - "plot_size_time_experiment(np.array(res_t), sizes, \"figs/size_time_synthetic.png\")\n", + "exp.plot_size_time_experiment(np.array(res_t), sizes,\n", + " \"figs/size_time_synthetic.png\")\n", "Image(filename=\"figs/size_time_synthetic.png\")" ] }, @@ -103,7 +90,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "sparsity = [0.2, 0.4, 0.6, 0.8]\n", @@ -114,7 +103,8 @@ "energy = 100\n", "random.seed(3)\n", "np.random.seed(7)\n", - "res_acc = sparsity_acc_experiment(sparsity, size, balance, energy, noise, num)" + "res_acc = exp.sparsity_acc_experiment(sparsity, size, balance,\n", + " energy, noise, num)" ] }, { @@ -135,7 +125,8 @@ } ], "source": [ - "plot_sparsity_acc_experiment(res_acc, sparsity, \"figs/sparsity_acc_synthetic.png\")\n", + "exp.plot_sparsity_acc_experiment(res_acc, sparsity,\n", + " \"figs/sparsity_acc_synthetic.png\")\n", "Image(filename=\"figs/sparsity_acc_synthetic.png\")" ] }, @@ -149,7 +140,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "energy = [10, 100, 1000, 10000]\n", @@ -160,7 +153,8 @@ "balance = 1.\n", "random.seed(3)\n", "np.random.seed(7)\n", - "res_acc = energy_acc_experiment(energy, size, sparsity, noise, balance, num)" + "res_acc = exp.energy_acc_experiment(energy, size, sparsity,\n", + " noise, balance, num)" ] }, { @@ -181,7 +175,8 @@ } ], "source": [ - "plot_energy_acc_experiment(res_acc, energy, \"figs/energy_acc_synthetic.png\")\n", + "exp.plot_energy_acc_experiment(res_acc, energy,\n", + " \"figs/energy_acc_synthetic.png\")\n", "\n", "Image(filename=\"figs/energy_acc_synthetic.png\")" ] @@ -196,7 +191,9 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "noise = [.2, .4, .6, .8]\n", @@ -207,7 +204,7 @@ "energy = 100\n", "random.seed(3)\n", "np.random.seed(7)\n", - "res_acc = noise_acc_experiment(noise, size, sparsity, energy, balance, num)" + "res_acc = exp.noise_acc_experiment(noise, size, sparsity, energy, balance, num)" ] }, { @@ -228,7 +225,7 @@ } ], "source": [ - "plot_noise_acc_experiment(res_acc, noise, \"figs/noise_acc_synthetic.png\")\n", + "exp.plot_noise_acc_experiment(res_acc, noise, \"figs/noise_acc_synthetic.png\")\n", "Image(filename=\"figs/noise_acc_synthetic.png\")" ] } From 1491ec2a59e0e78dbefc216b676932443a65525c Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Thu, 7 Sep 2017 20:34:36 +0200 Subject: [PATCH 38/62] Review code for ratio_cut_hierarchy --- lib/graph_signal_proc.py | 66 ++++++++++++++++------------------------ 1 file changed, 27 insertions(+), 39 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 5f64c43..ea9af2f 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -118,7 +118,7 @@ def graph_low_pass(lamb, U, T, gamma, lamb_max, K): h_vector = [h(T[-1] * l, gamma, lamb_max, K) for l in lamb] - return np.dot(U, np.dot(np.diag(h_vector), U.T)) + return dot(U, dot(diag(h_vector), U.T)) def graph_wavelets(lamb, U, N, T): @@ -137,7 +137,7 @@ def graph_wavelets(lamb, U, N, T): for t in range(len(T)): g_vector = [g(T[t] * l) for l in lamb] - w.append(np.dot(U, np.dot(np.diag(g_vector), U.T))) + w.append(dot(U, dot(diag(g_vector), U.T))) return np.asarray(w) @@ -154,7 +154,7 @@ def graph_fourier(F, U): F_hat = [] for i in range(0, len(U)): - F_hat.append(np.dot(F, U[:, i])) + F_hat.append(dot(F, U[:, i])) F_hat = np.array(F_hat) @@ -194,9 +194,9 @@ def hammond_wavelet_transform(w, s, T, F): for i in range(len(T)): # Each wavelet is represented by an N x N matrix - C.append(np.dot(F, w[i].T)) + C.append(dot(F, w[i].T)) # Append output of scaling function application at the end - C.append(np.dot(F, s.T)) + C.append(dot(F, s.T)) return np.asarray(C) @@ -231,8 +231,8 @@ def __init__(self, data): """ Input: * data: Anything to be stored in a node. - Usually only leaf nodes have data != None - data != None often used as stopping condition + Usually only leaf nodes have data != None + data != None often used as stopping condition """ self.data = data self.children = [] @@ -242,7 +242,7 @@ def __init__(self, data): # Level on the tree. The root has scale = 0 self.scale = 0 self.cut = 0 - # count: number of leaves (data != None) in the subtree + # count: number of leaves (data != None) of its subtree if data is None: self.count = 0 else: @@ -261,7 +261,6 @@ def add_child(self, obj): def set_counts(tree): """ - Sets counts for intermediate nodes in the tree. Input: * tree: tree node Output: @@ -305,24 +304,23 @@ def sweep(x, G): Output: * vec: indicator vector """ - best_val = nx.number_of_nodes(G) - 1 + sorted_x = np.argsort(x) - size_one = 0 + part_one = set() + N = nx.number_of_nodes(G) + best_val = N - 1 edges_cut = 0 - nodes_one = {} - - for i in range(x.shape[0]): - size_one = size_one + 1 - nodes_one[G.nodes()[sorted_x[i]]] = True + for i in range(N - 1): + part_one.add(G.nodes()[sorted_x[i]]) for v in G.neighbors(G.nodes()[sorted_x[i]]): - if v not in nodes_one: + if v not in part_one: edges_cut = edges_cut + 1 else: edges_cut = edges_cut - 1 - den = size_one * (nx.number_of_nodes(G) - size_one) + den = len(part_one) * (N - len(part_one)) if den > 0: val = float(edges_cut) / den @@ -330,15 +328,7 @@ def sweep(x, G): best_cand = i best_val = val - vec = np.zeros(nx.number_of_nodes(G)) - - for i in range(x.shape[0]): - if i <= best_cand: - vec[sorted_x[i]] = -1. - else: - vec[sorted_x[i]] = 1. - - return vec + return np.array([-1. if i <= best_cand else 1. for i in sorted_x]) def separate_lcc(G, G0): @@ -351,15 +341,8 @@ def separate_lcc(G, G0): Output: * x: indicator vector """ - x = [] - - for v in G.nodes(): - if v in G0: - x.append(-1) - else: - x.append(1.) - return np.array(x) + return np.array([-1. if v in G0 else 1. for v in G.nodes()]) def ratio_cut(G): @@ -376,7 +359,6 @@ def ratio_cut(G): if nx.number_of_nodes(G) == nx.number_of_nodes(G0): x = nx.fiedler_vector(G, method='lobpcg', tol=1e-5) - x = sweep(x, G) else: # In case G is not connected @@ -387,7 +369,7 @@ def ratio_cut(G): def get_subgraphs(G, cut): """ - Compute subgraphs generated by a cut. + Return the two subgraphs as two lists of nodes Input: * G: Original graph * cut: cut indicator vector @@ -417,10 +399,13 @@ def get_subgraphs(G, cut): def rc_recursive(node, G, ind): """ Recursively computes ratio-cut. + The leaves store, as data, the integer returned by ind for the + inserted node. Input: * node: tree node * G: graph - * ind: vertex index v: unique integer + * ind: index with unique integers as values + (see ratio_cut_hierarchy for definition) Output: * none """ @@ -454,11 +439,14 @@ def rc_recursive(node, G, ind): def ratio_cut_hierarchy(G): """ Computes ratio-cut hierarchy for a graph. + The leaves store, as data, the integer returned by ind for the + inserted node. Input: * G: graph Output: * root: tree root - * ind: graph index v: unique integer + * ind: index with unique integers as values + """ i = 0 ind = {} From fa099ac0475e8216c106dfa77de68e3225f84fcd Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Thu, 7 Sep 2017 22:54:33 +0200 Subject: [PATCH 39/62] Remove avgs and counts attributes from Node --- lib/graph_signal_proc.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index ea9af2f..fd6b709 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -236,8 +236,6 @@ def __init__(self, data): """ self.data = data self.children = [] - self.avgs = [] - self.counts = [] self.diffs = [] # Level on the tree. The root has scale = 0 self.scale = 0 @@ -479,11 +477,8 @@ def compute_coefficients(tree, F): count = count + tree.children[i].count if i > 0: - tree.avgs.append(float(avg) / count) - tree.counts.append(count) tree.diffs.append(2 * tree.children[i].count * (tree.children[i].avg - float(avg) / count)) - tree.avgs = list(reversed(tree.avgs)) tree.avg = float(avg) / tree.count else: tree.avg = F[tree.data] @@ -511,9 +506,7 @@ def reconstruct_values(tree, F): reconstruct_values(tree.children[i], F) count = count - tree.children[i].count avg = avg - tree.children[i].avg * tree.children[i].count - tree.avgs.append(float(avg) / count) - tree.avgs = list(reversed(tree.avgs)) else: F[tree.data] = tree.avg @@ -528,7 +521,6 @@ def clear_tree(tree): """ tree.avg = 0 tree.diffs = [] - tree.avgs = [] if tree.data is None: for i in range(len(tree.children)): From 6a1ff2bb69c39e8d110635a2cb17cf759e640d74 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 8 Sep 2017 12:36:41 +0200 Subject: [PATCH 40/62] Review code for Gavish tranform --- lib/graph_signal_proc.py | 41 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index fd6b709..7af3b45 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -461,7 +461,7 @@ def ratio_cut_hierarchy(G): def compute_coefficients(tree, F): """ - Computes tree coefficients for Gavish's transform. + Compute tree coefficients for Gavish's transform. Input: * tree: tree * F: graph signal @@ -469,24 +469,24 @@ def compute_coefficients(tree, F): * None """ if tree.data is None: - avg = 0 + tot = 0 count = 0 - for i in range(len(tree.children)): - compute_coefficients(tree.children[i], F) - avg = avg + tree.children[i].avg * tree.children[i].count - count = count + tree.children[i].count + for i, child in enumerate(tree.children): + compute_coefficients(child, F) + tot += child.avg * child.count + count += child.count if i > 0: - tree.diffs.append(2 * tree.children[i].count * - (tree.children[i].avg - float(avg) / count)) - tree.avg = float(avg) / tree.count + tree.diffs.append(2 * child.count * + (child.avg - float(tot) / count)) + tree.avg = float(tot) / tree.count else: tree.avg = F[tree.data] def reconstruct_values(tree, F): """ - Reconstructs values for Gavish's transform based on a tree. + Reconstruct values for Gavish's transform based on a tree. Input: * tree: tree * F: graph signal @@ -494,18 +494,18 @@ def reconstruct_values(tree, F): * None """ if tree.data is None: - avg = tree.avg * tree.count + tot = tree.avg * tree.count count = tree.count for i in reversed(range(len(tree.children))): if i == 0: - tree.children[i].avg = avg / tree.children[i].count + tree.children[i].avg = tot / tree.children[i].count reconstruct_values(tree.children[i], F) else: - tree.children[i].avg = float(avg) / count + 0.5 * \ + tree.children[i].avg = float(tot) / count + 0.5 * \ float(tree.diffs[i - 1]) / tree.children[i].count reconstruct_values(tree.children[i], F) count = count - tree.children[i].count - avg = avg - tree.children[i].avg * tree.children[i].count + tot = tot - tree.children[i].avg * tree.children[i].count else: F[tree.data] = tree.avg @@ -513,7 +513,8 @@ def reconstruct_values(tree, F): def clear_tree(tree): """ - Clears tree info. + Clear tree info. + tree.count is kept Input: * tree Output: @@ -529,22 +530,20 @@ def clear_tree(tree): def get_coefficients(tree, wtr): """ - Recovers wavelet coefficients from the wavelet tree. + Recover wavelet coefficients from the wavelet tree. Input: * tree - * wtr: wavelet coefficients + * wtr: list of wavelet coefficients Output: * None """ Q = deque() - scales = [] wtr.append(tree.count * tree.avg) Q.append(tree) while len(Q) > 0: node = Q.popleft() - scales.append(node.scale) for j in range(len(node.diffs)): wtr.append(node.diffs[j]) @@ -558,7 +557,7 @@ def set_coefficients(tree, wtr): Sets wavelet tree coefficients. Input: * tree - * wtr: wavelet coefficients + * wtr: list of wavelet coefficients """ Q = deque() tree.avg = float(wtr[0]) / tree.count @@ -570,7 +569,7 @@ def set_coefficients(tree, wtr): for j in range(len(node.children) - 1): node.diffs.append(wtr[p]) - p = p + 1 + p += 1 for i in range(len(node.children)): Q.append(node.children[i]) From e262f03e67dc90027f9ad00f9338abf4e6ff024e Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 8 Sep 2017 12:57:07 +0200 Subject: [PATCH 41/62] Remove unused functions from io.py --- lib/io.py | 47 ----------------------------------------------- 1 file changed, 47 deletions(-) diff --git a/lib/io.py b/lib/io.py index e7125a1..7a2e32f 100644 --- a/lib/io.py +++ b/lib/io.py @@ -1,7 +1,5 @@ import networkx as nx import numpy as np -import pandas as pd -import statsmodels.api as sm def read_graph(input_graph_name, input_data_name): @@ -95,48 +93,3 @@ def read_values(input_data_name, G): F = F - np.mean(F) return F - - -def read_dyn_graph(path, num_snapshots, G): - """ - Reads a dynamic graph. - Input: - * path: Path containing a files for each graph snapshot - (e.g. folder/traffic_, for files folder/traffic_0.data ... - folder/traffic_100.data) - * num_snapshots: number of snapshots - * G: networkx graph - Output: - * FT: array #snapshots x #vertices - - """ - FT = [] - for t in range(num_snapshots): - in_file = path + "_" + str(t) + ".data" - F = read_values(in_file, G) - FT.append(F) - - return np.array(FT) - - -def clean_traffic_data(FT): - start_time = datetime.strptime("1/04/11 00:00", "%d/%m/%y %H:%M") - c_FT = [] - for i in range(FT.shape[1]): - # removing daily seasonality - data = pd.DataFrame(FT[:, i], pd.DatetimeIndex( - start='1/04/11 00:00', periods=len(FT[:, i]), freq='5min')) - data.interpolate(inplace=True) - - res = sm.tsa.seasonal_decompose(data.values, freq=288) - F = FT[:, i] - res.seasonal - - # removing weekly seasonality - data = pd.DataFrame(F, pd.DatetimeIndex( - start='1/04/11 00:00', periods=len(FT[:, i]), freq='5min')) - res = sm.tsa.seasonal_decompose(data.values, freq=288 * 7) - F = F - res.seasonal - - c_FT.append(F) - - return np.array(c_FT).transpose() From 093c8bde3592d6815eecdb607d660b0fa0ae25ca Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 8 Sep 2017 15:55:47 +0200 Subject: [PATCH 42/62] Review comments in io.py --- lib/io.py | 42 ++++++++++++++++++++++++++++-------------- 1 file changed, 28 insertions(+), 14 deletions(-) diff --git a/lib/io.py b/lib/io.py index 7a2e32f..3685efe 100644 --- a/lib/io.py +++ b/lib/io.py @@ -1,13 +1,25 @@ +""" +This module read graph's info from files with the following format: + +input_graph_name has info about edges. +Row format: "vertex_A, vertex_B[, edge_weight]" + +input_data_name has info about the graph signal. +Row format : "vertex_id, vertex_value" +""" + import networkx as nx import numpy as np def read_graph(input_graph_name, input_data_name): """ - Reads graph from file. + Read graph from file. Input: - * input_graph_name: csv edge list - * input_data_name: csv node-value pairs + * input_graph_name has info about edges. + Row format: "vertex_A, vertex_B[, edge_weight]" + * input_data_name has info about the graph signal. + Row format : "vertex_id, vertex_value" Output: * networkx graph """ @@ -35,7 +47,8 @@ def read_graph(input_graph_name, input_data_name): vec = line.rsplit(',') v1 = vec[0] v2 = vec[1] - + # Note that the edge weight is always set to 1 + # even when provided available in input_graph_name if v1 in values and v2 in values: G.add_edge(v1, v2, weight=1.) @@ -44,29 +57,30 @@ def read_graph(input_graph_name, input_data_name): G = Gcc[0] - values_in_graph = {} + graph_signal = {} - # Setting values as node attributes + # Setting the graph_signal as node attribute for v in values.keys(): if v in G: - values_in_graph[v] = values[v] + graph_signal[v] = values[v] input_graph.close() - nx.set_node_attributes(G, "value", values_in_graph) + nx.set_node_attributes(G, "value", graph_signal) return G def read_values(input_data_name, G): """ - Reads node values. + Read the graph signal from file Input: - * input_data_name: csv node-value pairs + * input_data_name has info about the graph signal. + Row format : "vertex_id, vertex_value" * G: networkx graph Output: * F: normalized node values array, ordered by G.nodes() """ - D = {} + graph_signal = {} input_data = open(input_data_name, 'r') # Reading file @@ -76,14 +90,14 @@ def read_values(input_data_name, G): vertex = vec[0] value = float(vec[1]) - D[vertex] = value + graph_signal[vertex] = value input_data.close() F = [] for v in G.nodes(): - if v in D: - F.append(float(D[v])) + if v in graph_signal: + F.append(float(graph_signal[v])) else: F.append(0.) From 4413882f9a32ae21b2a65b5831ce376048a5987d Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 8 Sep 2017 23:16:54 +0200 Subject: [PATCH 43/62] Clean code for sweep_opt --- lib/optimal_cut.py | 148 +++++++++++++++++++-------------------------- 1 file changed, 62 insertions(+), 86 deletions(-) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 7508a60..cca56c3 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -19,100 +19,47 @@ def sweep_opt(x, F, G, k, ind): * ind: vertex index v: unique integer Output: * vec: indicator vector - * best_val: score value + * best_energy: max energy value * best_edges_cut: number of edges cut - * energy: wavelet energy value """ - best_val = 0. - best_edges_cut = 0 sorted_x = np.argsort(x) - size_one = 0 - sum_one = 0 - sum_two = 0 - - for v in G.nodes(): - sum_two = sum_two + F[ind[v]] - + part_one = set() + N = nx.number_of_nodes(G) + best_energy = 0. edges_cut = 0 - nodes_one = {} - total_size = nx.number_of_nodes(G) + best_edges_cut = 0 + sum_one = 0 # sum of the graph signal values for the nodes in part_one + sum_two = 0 # sum of the graph signal values for the nodes in part_two - for i in range(x.shape[0]): - size_one = size_one + 1 - sum_one = sum_one + F[ind[G.nodes()[sorted_x[i]]]] - sum_two = sum_two - F[ind[G.nodes()[sorted_x[i]]]] + for v in G.nodes(): + sum_two += F[ind[v]] - nodes_one[G.nodes()[sorted_x[i]]] = True + for i in range(N): + part_one.add(G.nodes()[sorted_x[i]]) + sum_one += F[ind[G.nodes()[sorted_x[i]]]] + sum_two -= F[ind[G.nodes()[sorted_x[i]]]] for v in G.neighbors(G.nodes()[sorted_x[i]]): - if v not in nodes_one: + if v not in part_one: edges_cut = edges_cut + 1 else: edges_cut = edges_cut - 1 - den = size_one * (total_size - size_one) * total_size + den = N * len(part_one) * (N - len(part_one)) if den > 0: - val = math.pow(sum_one * (total_size - size_one) - sum_two * - size_one, 2) / den + energy = math.pow(sum_one * (N - len(part_one)) - sum_two * + len(part_one), 2) / den else: - val = 0 + energy = 0 - if val >= best_val and edges_cut <= k: + if energy >= best_energy and edges_cut <= k: best_cand = i - best_val = val + best_energy = energy best_edges_cut = edges_cut - if total_size * size_one * (total_size - size_one) > 0: - energy = math.pow(sum_one * (total_size - size_one) - sum_two * - size_one, 2) / (total_size * size_one * - (total_size - size_one)) - else: - energy = 0 - - vec = np.zeros(total_size) - - for i in range(x.shape[0]): - if i <= best_cand: - vec[sorted_x[i]] = -1. - else: - vec[sorted_x[i]] = 1. - - return vec, best_val, best_edges_cut, energy - - -def laplacian_complete(n): - """ - Laplacian of a complete graph with n vertices. - Input: - * n: size - Output: - * C: Laplacian - """ - C = np.ones((n, n)) - C = -1 * C - D = np.diag(np.ones(n)) - C = (n) * D + C - - return C - - -def weighted_adjacency_complete(G, F, ind): - """ - Computes weighted adjacency complete matrix (w(v)-w(u))^2 - Input: - * G: graph - * F: graph signal - * ind: vertex index vertex: unique integer - Output: - * A: nxn matrix - """ - A = [] - for v in G.nodes(): - A.append([]) - for u in G.nodes(): - A[-1].append(pow(F[ind[v]] - F[ind[u]], 2)) + vec = np.array([-1. if i <= best_cand else 1. for i in sorted_x]) - return np.array(A) + return vec, best_energy, best_edges_cut def fast_cac(G, F, ind): @@ -174,7 +121,6 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): * res: dictionary with following fields: - x: indicator vector - size: number of edges cut - - score: cut score - energy: cut energy """ isqrtCL = gsp.sqrtmi(C + beta * L) @@ -183,12 +129,11 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 0)) x = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] - (x, score, size, energy) = sweep_opt(x, F, G, k, ind) + (x, energy, size) = sweep_opt(x, F, G, k, ind) res = {} res["x"] = np.array(x) res["size"] = size - res["score"] = score res["energy"] = energy return res @@ -330,7 +275,6 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): * res: dictionary with following fields: - x: indicator vector - size: number of edges cut - - score: cut score - energy: cut energy """ L = nx.laplacian_matrix(G) @@ -339,17 +283,51 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): eigvec = power_method(-M, start, 10) x = chebyshev_approx_1d(n, beta, eigvec, L) - (x, score, size, energy) = sweep_opt(x, F, G, k, ind) + (x, energy, size) = sweep_opt(x, F, G, k, ind) res = {} res["x"] = np.array(x) res["size"] = size - res["score"] = score res["energy"] = energy return res +def laplacian_complete(n): + """ + Laplacian of a complete graph with n vertices. + Input: + * n: size + Output: + * C: Laplacian + """ + C = np.ones((n, n)) + C = -1 * C + D = np.diag(np.ones(n)) + C = (n) * D + C + + return C + + +def weighted_adjacency_complete(G, F, ind): + """ + Computes weighted adjacency complete matrix (w(v)-w(u))^2 + Input: + * G: graph + * F: graph signal + * ind: vertex index vertex: unique integer + Output: + * A: nxn matrix + """ + A = [] + for v in G.nodes(): + A.append([]) + for u in G.nodes(): + A[-1].append(pow(F[ind[v]] - F[ind[u]], 2)) + + return np.array(A) + + def fast_search(G, F, k, n, ind): """ Efficient version of cut computation. @@ -364,8 +342,6 @@ def fast_search(G, F, k, n, ind): * cut """ start = np.ones(nx.number_of_nodes(G)) - C = laplacian_complete(nx.number_of_nodes(G)) - A = weighted_adjacency_complete(G, F, ind) CAC = fast_cac(G, F, ind) return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind) @@ -410,7 +386,7 @@ def one_d_search(G, F, k, ind): resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) if resc["size"] <= k: - if resc["score"] > resd["score"]: + if resc["energy"] > resd["energy"]: start = np.array(resc["x"]) b = d d = c @@ -473,9 +449,9 @@ def optimal_wavelet_basis(G, F, k, npol): b = 0 for i in range(0, len(cand_cuts)): - if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["score"] > 0: + if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["energy"] > 0: if (best_cut is None or - cand_cuts[i]["score"] > best_cut["score"]): + cand_cuts[i]["energy"] > best_cut["energy"]): best_cut = cand_cuts[i] b = i if best_cut is None: From e07d731b9e9c9cabe39287be470f4cde7ee3b14c Mon Sep 17 00:00:00 2001 From: Diego Sacconi Date: Fri, 8 Sep 2017 23:39:50 +0200 Subject: [PATCH 44/62] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index fbc90b8..192ba22 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,9 @@ Implementation of graph wavelets via sparse cuts with some baselines, datasets and evaluation. -Evaluation is performed using python notebooks. +Evaluation is performed using IPython Notebook. + +After code review some results may differ from those presented in the paper. Scalability and approximation experiments: ----------------------- From c4c764ac67136ccdb1d52603b55ef964f178c361 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sat, 9 Sep 2017 09:11:00 +0200 Subject: [PATCH 45/62] Make fast_cac faster --- lib/optimal_cut.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index cca56c3..d5cd2d0 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -73,16 +73,8 @@ def fast_cac(G, F, ind): Output: * CAC: matrix product """ - CAC = [] - for v in G.nodes(): - CAC.append([]) - for u in G.nodes(): - CAC[-1].append(F[ind[v]] * F[ind[u]]) - - CAC = np.array(CAC) - CAC = -2 * math.pow(nx.number_of_nodes(G), 2) * CAC - - return CAC + signal = np.array([F[ind[v]] for v in G.nodes()]) + return np.outer(signal, signal) def power_method(mat, start, maxit): From d878cbd8219d4a0e4e914f5c0980da487d125f7e Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sat, 9 Sep 2017 09:28:31 +0200 Subject: [PATCH 46/62] Review code for fast_cac --- lib/optimal_cut.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index d5cd2d0..3bf6c61 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -73,8 +73,8 @@ def fast_cac(G, F, ind): Output: * CAC: matrix product """ - signal = np.array([F[ind[v]] for v in G.nodes()]) - return np.outer(signal, signal) + sorted_F = np.array([F[ind[v]] for v in G.nodes()]) + return -2 * math.pow(len(sorted_F), 2) * np.outer(sorted_F, sorted_F) def power_method(mat, start, maxit): From 3c72ac44286c3fd00c061c63f5294080cb71c36f Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sun, 17 Sep 2017 18:45:31 +0200 Subject: [PATCH 47/62] Clean code in lib/syn.py --- lib/syn.py | 269 +++++++++-------------------------------------------- 1 file changed, 45 insertions(+), 224 deletions(-) diff --git a/lib/syn.py b/lib/syn.py index 6823197..c31aed1 100644 --- a/lib/syn.py +++ b/lib/syn.py @@ -1,27 +1,57 @@ -import random import math +import random import networkx as nx import numpy as np -from scipy import linalg def synthetic_graph(size, num_edges, sparsity, energy, balance, noise, seed=None): + r""" + Build a synthetic connected graph and a graph signal starting from two + sets of vertices. These sets have sizes depending on size and balance. + Input: + * size: number of vertices + * num_edges: number of edges + * sparsity: higher values penalize the creation of edges + having vertices in different sets + * energy: is the coefficient's energy obtained from the generated + graph signal and using as partitions the two vertex sets + * balance: takes values in (0,2). If 1 the two sets from which the + synthetic graph is built have similar size. + * noise: part of the std dev used for generating the noisy signal + * seed: optional seed for the random module + Output: + * G: connected graph + * np.array(F): graph signal + * edges_accross + 1: number of edges having vertices in different + sets + """ if seed: random.seed(seed) - size_part_a = int(math.ceil(float(size * balance) / 2)) + if balance <= 0 and balance >= 2: + raise ValueError("'balance' should be in (0,2)") + + if num_edges < size: + raise ValueError("The graph returned by synthetic_graph() is assumed " + + "to be connected: choose num_edges >= size - 1") + + size_part_a = int(math.ceil(size * balance / 2.)) size_part_b = size - size_part_a - F = [] - edges = {} - avg_a = float(np.sqrt(float(energy * size) / - (size_part_a * size_part_b))) / 2. + if size_part_a == 0: + raise ValueError("'size_part_a'==0 try bigger values " + + "for 'size' or 'balance'") + if size_part_b == 0: + raise ValueError("'size_part_b'==0 try a bigger value " + + "for 'size' or a smaller value for 'balance'") - avg_b = -float(np.sqrt(float(energy * size) / - (size_part_a * size_part_b))) / 2. + avg_a = np.sqrt(float(energy * size) / + (size_part_a * size_part_b)) / 2. + avg_b = - avg_a + F = [] for v in range(size): if v < size_part_a: F.append(random.gauss(avg_a, noise * avg_a)) @@ -30,9 +60,10 @@ def synthetic_graph(size, num_edges, sparsity, energy, balance, noise, G = nx.Graph() + edges_set = set({}) for v in range(size - 1): G.add_edge(v, v + 1) - edges[(v, v + 1)] = True + edges_set.add((v, v + 1)) remaining_edges = num_edges - len(G.edges()) edges_accross = int((size_part_a * size_part_b * (1. - sparsity) @@ -43,12 +74,12 @@ def synthetic_graph(size, num_edges, sparsity, energy, balance, noise, v1 = random.randint(0, size_part_a - 1) v2 = random.randint(size_part_a, size - 1) - while (v1, v2) in edges or v1 == v2: + while (v1, v2) in edges_set or v1 == v2: v1 = random.randint(0, size_part_a - 1) v2 = random.randint(size_part_a, size_part_a + size_part_b - 1) G.add_edge(v1, v2) - edges[(v1, v2)] = True + edges_set.add((v1, v2)) for e in range(edges_within): v1 = random.randint(0, size - 1) @@ -59,7 +90,7 @@ def synthetic_graph(size, num_edges, sparsity, energy, balance, noise, v1 = v2 v2 = tmp - while ((v1, v2) in edges or v1 == v2 or + while ((v1, v2) in edges_set or v1 == v2 or (v1 < size_part_a and v2 >= size_part_a) or (v1 >= size_part_a and v2 < size_part_a)): v1 = random.randint(0, size - 1) @@ -71,216 +102,6 @@ def synthetic_graph(size, num_edges, sparsity, energy, balance, noise, v2 = tmp G.add_edge(v1, v2) - edges[(v1, v2)] = True + edges_set.add((v1, v2)) return G, np.array(F), edges_accross + 1 - - -def compute_distances(center, graph): - distances = nx.shortest_path_length(graph, center) - - return distances - - -def compute_embedding(distances, radius, graph): - B = [] - s = 0 - for v in graph.nodes(): - if distances[v] <= radius: - B.append(1) - s = s + 1 - else: - B.append(0) - - return np.array(B) - - -def generate_dyn_cascade(G, diam, duration, n, seed=None): - - if seed: - random.seed(seed) - - Fs = [] - - for j in range(n): - v = random.randint(0, len(G.nodes()) - 1) - distances = compute_distances(G.nodes()[v], G) - - if diam > duration: - num_snaps = diam - else: - num_snaps = duration - - for i in range(num_snaps): - r = int(i * math.ceil(float(diam) / duration)) - - F = compute_embedding(distances, r, G) - Fs.append(F) - - return np.array(Fs) - - -def generate_dyn_heat(G, s, jump, n, seed=None): - if seed: - random.seed(seed) - Fs = [] - L = nx.normalized_laplacian_matrix(G) - L = L.todense() - F0s = [] - seeds = [] - - for i in range(s): - F0 = np.zeros(len(G.nodes())) - v = random.randint(0, len(G.nodes()) - 1) - seeds.append(v) - F0[v] = len(G.nodes()) - F0s.append(F0) - - Fs.append(np.sum(F0s, axis=0)) - - for j in range(n): - FIs = [] - for i in range(s): - FI = np.multiply(linalg.expm(-j * jump * L), F0s[i])[:, seeds[i]] - FIs.append(FI) - - Fs.append(np.sum(FIs, axis=0)) - - return np.array(Fs)[1:] - - -def generate_dyn_gaussian_noise(G, n, seed=None): - if seed: - np.random.seed(seed) - Fs = [] - for j in range(n): - F = np.random.rand(len(G.nodes())) - Fs.append(F) - - return np.array(Fs) - - -def generate_dyn_bursty_noise(G, n, seed1=None, seed2=None): - if seed1: - random.seed(seed1) - if seed2: - np.random.seed(seed2) - Fs = [] - bursty_beta = 1 - non_bursty_beta = 1000 - bursty_bursty = 0.7 - non_bursty_non_bursty = 0.9 - bursty = False - - for j in range(n): - r = random.random() - - if not bursty: - if r > non_bursty_non_bursty: - bursty = True - else: - if r > bursty_bursty: - bursty = False - - if bursty: - F = np.random.exponential(bursty_beta, len(G.nodes())) - else: - F = np.random.exponential(non_bursty_beta, len(G.nodes())) - - Fs.append(F) - - return np.array(Fs) - - -def generate_dyn_indep_cascade(G, s, p, seed1=None, seed2=None): - if seed1: - random.seed(seed1) - if seed2: - np.random.seed(seed2) - Fs = [] - - seeds = np.random.choice(len(G.nodes()), s, replace=False) - - F0 = np.zeros(len(G.nodes())) - - ind = {} - i = 0 - - for v in G.nodes(): - ind[v] = i - i = i + 1 - - for s in seeds: - F0[s] = 2.0 - - while True: - F1 = np.zeros(len(G.nodes())) - new_inf = 0 - for v in G.nodes(): - if F0[ind[v]] > 1.0: - for u in G.neighbors(v): - r = random.random() - if r <= p and F0[ind[u]] < 1.0: - F1[ind[u]] = 2.0 - new_inf = new_inf + 1 - F1[ind[v]] = 1.0 - F0[ind[v]] = 1.0 - elif F0[ind[v]] > 0.0: - F1[ind[v]] = 1.0 - - Fs.append(F0) - - if new_inf == 0 and len(Fs) > 1: - break - - F0 = np.copy(F1) - - return np.array(Fs) - - -def generate_dyn_linear_threshold(G, s, seed1=None, seed2=None): - if seed1: - random.seed(seed1) - if seed2: - np.random.seed(seed2) - Fs = [] - - seeds = np.random.choice(len(G.nodes()), s, replace=False) - - F0 = np.zeros(len(G.nodes())) - thresholds = np.random.uniform(0.0, 1.0, len(G.nodes())) - - ind = {} - i = 0 - - for v in G.nodes(): - ind[v] = i - i = i + 1 - - for s in seeds: - F0[s] = 1.0 - - while True: - F1 = np.zeros(len(G.nodes())) - new_inf = 0 - for v in G.nodes(): - if F0[ind[v]] < 1.0: - n = 0 - for u in G.neighbors(v): - if F0[ind[u]] > 0: - n = n + 1 - - if (float(n) / len(G.neighbors(v))) >= thresholds[ind[v]]: - F1[ind[v]] = 1.0 - new_inf = new_inf + 1 - else: - F1[ind[v]] = 1.0 - - Fs.append(F0) - - if new_inf == 0 and len(Fs) > 1: - break - - F0 = np.copy(F1) - - return np.array(Fs) From a5a5c61ff5e1a3ffd08cab2b1106018d38efecd0 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sun, 24 Sep 2017 16:58:12 +0200 Subject: [PATCH 48/62] Add DOCTEST to compression_experiment --- lib/experiments.py | 221 ++++++++++++++++-------------- lib/graph_signal_proc.py | 41 ++++-- lib/optimal_cut.py | 289 +++++++++++++++++++-------------------- lib/static.py | 126 +++++++---------- lib/vis.py | 33 +++++ 5 files changed, 362 insertions(+), 348 deletions(-) diff --git a/lib/experiments.py b/lib/experiments.py index 35e55e0..6faa277 100644 --- a/lib/experiments.py +++ b/lib/experiments.py @@ -1,6 +1,6 @@ import time -import numpy +import numpy as np import matplotlib.pyplot as plt import lib.optimal_cut as oc @@ -11,11 +11,7 @@ def L2(F, F_approx): """ Sum of squared errors """ - e = 0 - for i in range(F.shape[0]): - e = e + ((F[i] - F_approx[i])**2).sum() - - return float(e) + return sum([(F[i] - F_approx[i])**2 for i in range(F.shape[0])]) def size_time_experiment(sizes, balance, sparsity, energy, noise, num): @@ -35,21 +31,13 @@ def size_time_experiment(sizes, balance, sparsity, energy, noise, num): for s in range(len(sizes)): res_t = [] - num_edges = 3 * sizes[s] for i in range(num): # synthetic_graph(size, num_edges, sparsity, energy, balance, # noise, seed=None) - (G, F, cut) = syn.synthetic_graph(sizes[s], num_edges, sparsity, - energy, balance, noise) - - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 + (G, F, k) = syn.synthetic_graph(sizes[s], 3 * sizes[s], sparsity, + energy, balance, noise) - # k = max number of edges to be cut - k = int(len(G.edges()) * sparsity) + ind = {v: i for i, v in enumerate(G.nodes())} start_time = time.time() oc.one_d_search(G, F, k, ind) @@ -69,10 +57,10 @@ def size_time_experiment(sizes, balance, sparsity, energy, noise, num): res_t.append([time_slow, time_5, time_20, time_50]) - r = numpy.mean(numpy.array(res_t), axis=0) + r = np.mean(np.array(res_t), axis=0) res_time.append(r) - return numpy.array(res_time) + return np.array(res_time) def sparsity_acc_experiment(sparsity, size, balance, energy, noise, num): @@ -96,11 +84,7 @@ def sparsity_acc_experiment(sparsity, size, balance, energy, noise, num): (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity[s], energy, balance, noise) - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 + ind = {v: i for i, v in enumerate(G.nodes())} c = oc.one_d_search(G, F, k, ind) acc_slow = c["energy"] @@ -116,17 +100,17 @@ def sparsity_acc_experiment(sparsity, size, balance, energy, noise, num): res_a.append([acc_slow, acc_5, acc_20, acc_50]) - r = numpy.mean(numpy.array(res_a), axis=0) + r = np.mean(np.array(res_a), axis=0) res.append(r) - return numpy.array(res) + return np.array(res) def noise_acc_experiment(noise, size, sparsity, energy, balance, num): """ Noise x accuracy experiments using synthetic data. Input: - * noise: many + * noise: list of noises * size * sparsity * energy @@ -143,11 +127,7 @@ def noise_acc_experiment(noise, size, sparsity, energy, balance, num): (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity, energy, balance, noise[s]) - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 + ind = {v: i for i, v in enumerate(G.nodes())} c = oc.one_d_search(G, F, k, ind) acc_slow = c["energy"] @@ -163,17 +143,17 @@ def noise_acc_experiment(noise, size, sparsity, energy, balance, num): res_a.append([acc_slow, acc_5, acc_20, acc_50]) - r = numpy.mean(numpy.array(res_a), axis=0) + r = np.mean(np.array(res_a), axis=0) res.append(r) - return numpy.array(res) + return np.array(res) def energy_acc_experiment(energy, size, sparsity, noise, balance, num): """ Energy x accuracy experiments using synthetic data. Input: - * energy: many + * energy: list of energies * size * sparsity * noise @@ -190,11 +170,7 @@ def energy_acc_experiment(energy, size, sparsity, noise, balance, num): (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity, energy[s], balance, noise) - j = 0 - ind = {} - for v in G.nodes(): - ind[v] = j - j = j + 1 + ind = {v: i for i, v in enumerate(G.nodes())} c = oc.one_d_search(G, F, k, ind) acc_slow = c["energy"] @@ -210,10 +186,10 @@ def energy_acc_experiment(energy, size, sparsity, noise, balance, num): res_a.append([acc_slow, acc_5, acc_20, acc_50]) - r = numpy.mean(numpy.array(res_a), axis=0) + r = np.mean(np.array(res_a), axis=0) res.append(r) - return numpy.array(res) + return np.array(res) def plot_size_time_experiment(results, sizes, output_file_name): @@ -230,7 +206,6 @@ def plot_size_time_experiment(results, sizes, output_file_name): ax = plt.subplot(111) - ncol = 2 ax.plot(sizes, results[:, 0], marker="x", color="cyan", label="SWT", markersize=15) ax.plot(sizes, results[:, 1], marker="o", color="orangered", @@ -240,7 +215,7 @@ def plot_size_time_experiment(results, sizes, output_file_name): ax.plot(sizes, results[:, 3], marker="o", color="k", label="FSWT-50", markersize=15) plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.legend(loc='upper center', prop={'size': 20}, ncol=2) ax.set_ylabel('time (sec.)', fontsize=30) ax.set_xlabel('#vertices', fontsize=30) ax.tick_params(labelsize=23) @@ -265,19 +240,18 @@ def plot_sparsity_acc_experiment(results, sparsity, output_file_name): """ plt.clf() - ncol = 2 ax = plt.subplot(111) width = 0.04 # the width of the bars) - ax.bar(numpy.array(sparsity) - 2 * width, + ax.bar(np.array(sparsity) - 2 * width, results[:, 0], width, color='cyan', label="SWT", hatch="/") - ax.bar(numpy.array(sparsity) - width, results[:, 1], + ax.bar(np.array(sparsity) - width, results[:, 1], width, color='orangered', label="FSWT-5", hatch="\\") - ax.bar(numpy.array(sparsity), results[:, 2], width, + ax.bar(np.array(sparsity), results[:, 2], width, color='darkgreen', label="FSWT-20", hatch="-") - ax.bar(numpy.array(sparsity) + width, results[:, 3], + ax.bar(np.array(sparsity) + width, results[:, 3], width, color='k', label="FSWT-50", hatch="*") plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.legend(loc='upper center', prop={'size': 20}, ncol=2) ax.set_ylabel(r'L$_2$ energy', fontsize=30) ax.set_xlabel('sparsity', fontsize=30) plt.rcParams['xtick.labelsize'] = 20 @@ -301,19 +275,19 @@ def plot_noise_acc_experiment(results, noise, output_file_name): plt.clf() ax = plt.subplot(111) - ncol = 2 + width = 0.04 # the width of the bars) - ax.bar(numpy.array(noise) - 2 * width, results[:, 0], + ax.bar(np.array(noise) - 2 * width, results[:, 0], width, color='cyan', label="SWT") - ax.bar(numpy.array(noise) - width, + ax.bar(np.array(noise) - width, results[:, 1], width, color='orangered', label="FSWT-5") - ax.bar(numpy.array(noise), results[:, 2], + ax.bar(np.array(noise), results[:, 2], width, color='darkgreen', label="FSWT-20") - ax.bar(numpy.array(noise) + width, results[:, 3], + ax.bar(np.array(noise) + width, results[:, 3], width, color='k', label="FSWT-50") plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.legend(loc='upper center', prop={'size': 20}, ncol=2) ax.set_ylabel(r'L$_2$ energy', fontsize=30) ax.set_xlabel('noise', fontsize=30) plt.rcParams['xtick.labelsize'] = 20 @@ -335,8 +309,8 @@ def plot_energy_acc_experiment(results, energy, output_file_name): * None """ plt.clf() - ncol = 2 - ind = numpy.array(list(range(4))) + + ind = np.array(list(range(4))) ax = plt.subplot(111) width = 0.2 # the width of the bars) ax.bar(ind - width, results[:, 0], @@ -349,7 +323,7 @@ def plot_energy_acc_experiment(results, energy, output_file_name): width, color='k', label="FSWT-50", log=True) plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper left', prop={'size': 20}, ncol=ncol) + ax.legend(loc='upper left', prop={'size': 20}, ncol=2) ax.set_ylabel(r'L$_2$ energy', fontsize=30) ax.set_xlabel(r'L$_2$ energy (data)', fontsize=30) plt.rcParams['xtick.labelsize'] = 20 @@ -364,6 +338,87 @@ def plot_energy_acc_experiment(results, energy, output_file_name): plt.savefig(output_file_name, dpi=300, bbox_inches='tight') +def get_children(tree, chil_list): + c = tree.children + if c != []: + chil_list.append(c[0]) + if len(c) == 2: + chil_list.append(c[1]) + get_children(c[0], chil_list) + if len(c) == 2: + get_children(c[1], chil_list) + + +def compression_experiment(G, F, algs, comp_ratios, num): + r""" + Runs compression experiment static. + Input: + * G: graph + * F: graph signal + * algs: compression algorithms/transforms + * comp_ratios: compression ratios + * num: number of repetitions + Output: + * results: compression results + * times: compression times + + To run the following DOCTEST set PYTHONHASHSEED=0 + + >>> from lib import io, static + >>> import lib.datasets as data + >>> + >>> G = io.read_graph(data.small_traffic["path"] + "traffic.graph", + ... data.small_traffic["path"] + "traffic.data") + >>> F = io.read_values(data.small_traffic["path"] + "traffic.data", G) + >>> algs = [static.OptWavelets(n=5, method='lobpcg'),\ + ... static.OptWavelets(method='tracemin_lu'), static.Fourier(),\ + ... static.GRCWavelets(method='tracemin_lu')] + >>> comp_ratios = [0.1, 0.2] + >>> res_smt, time_smt = \ + ... compression_experiment(G, np.array(F), algs, + ... comp_ratios, 1) + >>> print(res_smt['FT']) + [ 0.17681431 0.1036391 ] + >>> print(res_smt['FSWT']) + [ 0.16306688 0.03567735] + >>> print(res_smt['SWT']) + [ 0.15091373 0.05312383] + >>> print(res_smt['GWT']) + [ 0.1988195 0.10399963] + + #>> print(res_smt['FT']) + #[ 0.17681431 0.1036391 ] + #>> print(res_smt['GWT']) + #[ 0.20987113 0.13070782] + """ + results = {} + times = {} + + for alg in algs: + results[alg.name()] = [] + times[alg.name()] = [] + for r in range(len(comp_ratios)): + T = [] + R = [] + for i in range(num): + start_time = time.time() + alg.set_graph(G) + tr = alg.transform(F) + size = int(F.size * comp_ratios[r]) + appx_tr = alg.drop_frequency(tr, size) + appx_F = alg.inverse(appx_tr) + t = time.time() - start_time + T.append(t) + R.append(L2(F, appx_F)) + T = np.array(T) + R = np.array(R) + times[alg.name()].append(np.mean(T)) + results[alg.name()].append(np.mean(R)) + results[alg.name()] = np.array(results[alg.name()]) + times[alg.name()] = np.array(times[alg.name()]) + return results, times + + def plot_compression_experiments(results, comp_ratios, output_file_name, max_y): """ @@ -384,7 +439,6 @@ def plot_compression_experiments(results, comp_ratios, output_file_name, results[alg][i] = results[alg][i - 1] ax = plt.subplot(111) - ncol = 3 ax.semilogy(comp_ratios, results["FSWT"], marker="o", color="r", label="FSWT", markersize=15) @@ -403,7 +457,7 @@ def plot_compression_experiments(results, comp_ratios, output_file_name, label="HWT", markersize=15) plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size': 20}, ncol=ncol) + ax.legend(loc='upper center', prop={'size': 20}, ncol=3) ax.set_ylabel(r'L$_2$ error', fontsize=30) ax.set_xlabel('size', fontsize=30) plt.rcParams['xtick.labelsize'] = 20 @@ -412,48 +466,3 @@ def plot_compression_experiments(results, comp_ratios, output_file_name, ax.set_ylim(0., max_y) plt.savefig(output_file_name, dpi=300, bbox_inches='tight') - - -def compression_experiment_static(G, F, algs, comp_ratios, num): - """ - Runs compression experiment static. - Input: - * G: graph - * F: graph signal - * algs: compression algorithms/transforms - * comp_ratios: compression ratios - * num: number of repetitions - Output: - * results: compression results - * times: compression times - """ - results = {} - times = {} - - for alg in algs: - results[alg.name()] = [] - times[alg.name()] = [] - - for r in range(len(comp_ratios)): - T = [] - R = [] - for i in range(num): - start_time = time.time() - alg.set_graph(G) - tr = alg.transform(F) - size = int(F.size * comp_ratios[r]) - appx_tr = alg.drop_frequency(tr, size) - appx_F = alg.inverse(appx_tr) - t = time.time() - start_time - T.append(t) - R.append(L2(F, appx_F)) - T = numpy.array(T) - R = numpy.array(R) - times[alg.name()].append(numpy.mean(T)) - results[alg.name()].append(numpy.mean(R)) - - results[alg.name()] = numpy.array(results[alg.name()]) - - times[alg.name()] = numpy.array(times[alg.name()]) - - return results, times diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 7af3b45..6d1346b 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -239,13 +239,19 @@ def __init__(self, data): self.diffs = [] # Level on the tree. The root has scale = 0 self.scale = 0 - self.cut = 0 # count: number of leaves (data != None) of its subtree if data is None: self.count = 0 else: self.count = 1 + def __str__(self): + descr = "Node id: {}, data: {}, scale: {}, count: {}" + return descr.format(id(self), self.data, self.scale, self.count) + + def __repr__(self): + return self.__str__() + def add_child(self, obj): """ Adds obj as a child to a node. @@ -292,6 +298,11 @@ def sqrtmi(mat): return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) +def set_fiedler_method(method): + global _method + _method = method + + def sweep(x, G): """ Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian). @@ -302,7 +313,6 @@ def sweep(x, G): Output: * vec: indicator vector """ - sorted_x = np.argsort(x) part_one = set() N = nx.number_of_nodes(G) @@ -326,7 +336,13 @@ def sweep(x, G): best_cand = i best_val = val - return np.array([-1. if i <= best_cand else 1. for i in sorted_x]) + vec = np.ones(nx.number_of_nodes(G)) + + for i in range(x.shape[0]): + if i <= best_cand: + vec[sorted_x[i]] = -1. + + return vec def separate_lcc(G, G0): @@ -356,12 +372,12 @@ def ratio_cut(G): G0 = Gcc[0] if nx.number_of_nodes(G) == nx.number_of_nodes(G0): - x = nx.fiedler_vector(G, method='lobpcg', tol=1e-5) + scipy.random.seed(1) + x = nx.fiedler_vector(G, method=_method, tol=1e-5) x = sweep(x, G) else: # In case G is not connected x = separate_lcc(G, G0) - return np.array(x) @@ -377,7 +393,6 @@ def get_subgraphs(G, cut): """ G1 = nx.Graph() G2 = nx.Graph() - i = 0 P1 = [] P2 = [] @@ -434,7 +449,7 @@ def rc_recursive(node, G, ind): node.add_child(r) -def ratio_cut_hierarchy(G): +def ratio_cut_hierarchy(G, method='lobpcg'): """ Computes ratio-cut hierarchy for a graph. The leaves store, as data, the integer returned by ind for the @@ -446,11 +461,10 @@ def ratio_cut_hierarchy(G): * ind: index with unique integers as values """ - i = 0 - ind = {} - for v in G.nodes(): - ind[v] = i - i = i + 1 + global _method + _method = method + + ind = {v: i for i, v in enumerate(G.nodes())} root = Node(None) @@ -575,7 +589,7 @@ def set_coefficients(tree, wtr): Q.append(node.children[i]) -def gavish_wavelet_transform(tree, ind, G, F): +def gavish_wavelet_transform(tree, G, F): """ Gavish's wavelet transform. Input: @@ -590,7 +604,6 @@ def gavish_wavelet_transform(tree, ind, G, F): clear_tree(tree) compute_coefficients(tree, F) get_coefficients(tree, wtr) - return np.array(wtr) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 3bf6c61..dcab500 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -1,7 +1,7 @@ import math -import numpy as np import networkx as nx +import numpy as np import scipy from lib.graph_signal_proc import Node @@ -20,14 +20,14 @@ def sweep_opt(x, F, G, k, ind): Output: * vec: indicator vector * best_energy: max energy value - * best_edges_cut: number of edges cut + * best_cut_size: number of edges in the returned cut """ sorted_x = np.argsort(x) part_one = set() N = nx.number_of_nodes(G) best_energy = 0. - edges_cut = 0 - best_edges_cut = 0 + cut_size = 0 + best_cut_size = 0 sum_one = 0 # sum of the graph signal values for the nodes in part_one sum_two = 0 # sum of the graph signal values for the nodes in part_two @@ -41,9 +41,9 @@ def sweep_opt(x, F, G, k, ind): for v in G.neighbors(G.nodes()[sorted_x[i]]): if v not in part_one: - edges_cut = edges_cut + 1 + cut_size += 1 else: - edges_cut = edges_cut - 1 + cut_size -= 1 den = N * len(part_one) * (N - len(part_one)) if den > 0: @@ -52,14 +52,18 @@ def sweep_opt(x, F, G, k, ind): else: energy = 0 - if energy >= best_energy and edges_cut <= k: + if energy >= best_energy and cut_size <= k: best_cand = i best_energy = energy - best_edges_cut = edges_cut + best_cut_size = cut_size + + vec = np.ones(nx.number_of_nodes(G)) - vec = np.array([-1. if i <= best_cand else 1. for i in sorted_x]) + for i in range(x.shape[0]): + if i <= best_cand: + vec[sorted_x[i]] = -1. - return vec, best_energy, best_edges_cut + return vec, best_energy, best_cut_size, best_energy def fast_cac(G, F, ind): @@ -121,12 +125,8 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 0)) x = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] - (x, energy, size) = sweep_opt(x, F, G, k, ind) - res = {} - res["x"] = np.array(x) - res["size"] = size - res["energy"] = energy + res["x"], res["energy"], res["size"], res["score"] = sweep_opt(x, F, G, k, ind) return res @@ -139,46 +139,48 @@ def trans(L, min_v, max_v): * min_v: lower bound * max_v: upper bound Output: - * translation + * translation as tuple of a sparse matrix and a scalar """ - return (float(2.) / (max_v - min_v)) * L, -(float(max_v + min_v) / - (max_v - min_v)) + return (2. / (max_v - min_v)) * L, -(float(max_v + min_v) / + (max_v - min_v)) -def fun(k, n, beta, min_v, max_v, x): +def isqrt(k, beta, min_v, max_v, x): """ - Function to be integrated in Chebyshev polynomial computation. + Inverse square root function to be integrated in + Chebyshev polynomial computation. Input: * k: coefficient number - * n: number of polynomials + * beta: regularization parameter * min_v: lower bound * max_v: upper bound + * x: function variable Output: * function value """ - y = 0.5 * math.cos(x) * float(max_v - min_v) + (0.5 * (max_v + min_v)) + y = 0.5 * (math.cos(x) * float(max_v - min_v) + (max_v + min_v)) - return math.cos(k * x) * (float(1.) / math.sqrt(beta * y)) + return math.cos(k * x) * (1. / math.sqrt(beta * y)) -def coef(k, n, beta, min_v, max_v): +def coef(k, beta, min_v, max_v): """ - Chebyshev polynomial coefficients. + Get the k-th Chebyshev polynomial coefficient for the inverse + square root Input: * k: coefficient number - * n: number of polynomials * min_v: lower bound * max_v: upper bound Output: * coefficient """ - return float(2. * scipy.integrate.quad( - lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi + return (2. / math.pi) * (scipy.integrate.quad( + lambda x: isqrt(k, beta, min_v, max_v, x), 0., math.pi)[0]) def chebyshev_approx_2d(n, beta, X, L): """ - Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using + Approximates sqrt((beta * L)^+) * X * sqrt((beta * L)^+) using Chebyshev polynomials (twice) Input: * n: number of polynomials @@ -188,31 +190,35 @@ def chebyshev_approx_2d(n, beta, X, L): Output: * P2: approximation """ - max_v = beta * L.shape[0] + N = L.shape[0] + max_v = beta * N min_v = 1 - + # ts1 is a sparse matrix, ts2 is a scalar ts1, ts2 = trans(L, min_v, max_v) - P1 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * X + # Recurrence relation for Chebyshev polynomials + # Tk = 2 * y * Tk_1 - Tk_2 + # 0,5 * c_n0 * x + sum_{k=1}^{inf} (c_nk * Tk) * x + P1 = 0.5 * coef(0, beta, min_v, max_v) * X tkm2 = X tkm1 = scipy.sparse.csr_matrix.dot(ts1, X) + ts2 * X - P1 = P1 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 + P1 = P1 + coef(1, beta, min_v, max_v) * tkm1 for i in range(2, n): Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 - P1 = P1 + coef(i, L.shape[0], beta, min_v, max_v) * Tk + P1 = P1 + coef(i, beta, min_v, max_v) * Tk tkm2 = tkm1 tkm1 = Tk P1 = P1.transpose() - P2 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * P1 + P2 = 0.5 * coef(0, beta, min_v, max_v) * P1 tkm2 = P1 tkm1 = scipy.sparse.csr_matrix.dot(ts1, P1) + ts2 * P1 - P2 = P2 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 + P2 = P2 + coef(1, beta, min_v, max_v) * tkm1 for i in range(2, n): Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 - P2 = P2 + coef(i, L.shape[0], beta, min_v, max_v) * Tk + P2 = P2 + coef(i, beta, min_v, max_v) * Tk tkm2 = tkm1 tkm1 = Tk @@ -221,7 +227,7 @@ def chebyshev_approx_2d(n, beta, X, L): def chebyshev_approx_1d(n, beta, x, L): """ - Approximates x*sqrt(L^+) using Chebyshev polynomials. + Approximates x*sqrt((beta * L)^+) using Chebyshev polynomials. Input: * n: number of polynomials * beta: regularization parameter @@ -231,20 +237,25 @@ def chebyshev_approx_1d(n, beta, x, L): * P: approximation """ - max_v = beta * L.shape[0] + N = L.shape[0] + max_v = beta * N min_v = 1 - + # ts1 is a sparse matrix, ts2 is a scalar ts1, ts2 = trans(L, min_v, max_v) - P = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * x - tkm2 = x - tkm1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x - P = P + coef(1, L.shape[0], beta, min_v, max_v) * tkm1 - + # Recurrence relation for Chebyshev polynomials + # Tk = 2 * y * Tk_1 - Tk_2 + # 0,5 * c_n0 * x + sum_{k=1}^{inf} (c_nk * Tk) * x + Tk_2 = x + Tk_1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x + P = 0.5 * coef(0, beta, min_v, max_v) * x + P += coef(1, beta, min_v, max_v) * Tk_1 + """(2./ (max_v - min_v)) * L, -(float(max_v + min_v) / + (max_v - min_v))""" for i in range(2, n): - Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2 - P = P + coef(i, L.shape[0], beta, min_v, max_v) * Tk - tkm2 = tkm1 - tkm1 = Tk + Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, Tk_1) + ts2 * Tk_1) - Tk_2 + P += coef(i, beta, min_v, max_v) * Tk + Tk_2 = Tk_1 + Tk_1 = Tk return P @@ -258,7 +269,6 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): * start: initialization * F: graph signal * G: graph - * L: graph laplacian matrix * beta: regularization parameter * k: max edges cut * n: number of polynomials @@ -274,17 +284,34 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): eigvec = power_method(-M, start, 10) x = chebyshev_approx_1d(n, beta, eigvec, L) - - (x, energy, size) = sweep_opt(x, F, G, k, ind) - res = {} - res["x"] = np.array(x) - res["size"] = size - res["energy"] = energy + res["x"], res["energy"], res["size"], res["score"] = sweep_opt(x, F, G, k, ind) return res +def fast_search(G, F, k, n, ind): + """ + Efficient version of cut computation. + Does not perform 1-D search for beta. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * n: number of chebyshev polynomials + * ind: vertex index vertex: unique integer + Output: + * cut + """ + start = np.ones(nx.number_of_nodes(G)) + CAC = fast_cac(G, F, ind) + + return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind) + + +gr = (math.sqrt(5) - 1) / 2 + + def laplacian_complete(n): """ Laplacian of a complete graph with n vertices. @@ -320,28 +347,6 @@ def weighted_adjacency_complete(G, F, ind): return np.array(A) -def fast_search(G, F, k, n, ind): - """ - Efficient version of cut computation. - Does not perform 1-D search for beta. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * n: number of chebyshev polynomials - * ind: vertex index vertex: unique integer - Output: - * cut - """ - start = np.ones(nx.number_of_nodes(G)) - CAC = fast_cac(G, F, ind) - - return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind) - - -gr = (math.sqrt(5) - 1) / 2 - - def one_d_search(G, F, k, ind): """ Cut computation. Perform 1-D search for beta using golden search. @@ -360,7 +365,8 @@ def one_d_search(G, F, k, ind): start = np.ones(nx.number_of_nodes(G)) L = nx.laplacian_matrix(G).todense() - # Upper and lower bounds for search + # Upper and lower bounds for beta + gr = (math.sqrt(5) - 1) / 2 a = 0. b = 1000. c = b - gr * (b - a) @@ -377,19 +383,18 @@ def one_d_search(G, F, k, ind): resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind) resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) - if resc["size"] <= k: - if resc["energy"] > resd["energy"]: - start = np.array(resc["x"]) - b = d - d = c - c = b - gr * (b - a) - else: - start = np.array(resd["x"]) - a = c - c = d - d = a + gr * (b - a) + if resc["size"] <= k and (resc["energy"] > resd["energy"]): + start = resc["x"] + b = d + d = c + c = b - gr * (b - a) + elif resd["size"] <= k and (resc["energy"] < resd["energy"]): + start = resd["x"] + a = c + c = d + d = a + gr * (b - a) else: - start = np.array(resc["x"]) + start = resc["x"] a = c c = d d = a + gr * (b - a) @@ -399,26 +404,26 @@ def one_d_search(G, F, k, ind): return resab -def optimal_wavelet_basis(G, F, k, npol): +def optimal_wavelet_basis(G, F, k, npol, method='lobpcg'): """ - Computation of optimal graph wavelet basis. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * npol: number of chebyshev polynomials, if 0 run exact version - Output: - * root: tree root - * ind: vertex index vertex: unique integer - * size: number of edges cut + Computation of optimal graph wavelet basis. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * npol: number of chebyshev polynomials, if 0 run exact version + Output: + * root: tree root + * ind: vertex index vertex: unique integer + * size: number of edges cut """ + global _method + _method = method + + gsp.set_fiedler_method(_method) # Creating index - ind = {} - i = 0 - for v in G.nodes(): - ind[v] = i - i = i + 1 + ind = {v: i for i, v in enumerate(G.nodes())} # First cut root = Node(None) @@ -434,63 +439,47 @@ def optimal_wavelet_basis(G, F, k, npol): c["graph"] = G cand_cuts.append(c) - - # Recursively compute new cuts + # Recursively find the best cut. Each time it tries first the supposedly + # biggest subgraphs inserted earlier in cand_cuts until the list is emptied + # or no new best cut is found while size <= k and len(cand_cuts) > 0: best_cut = None - b = 0 - - for i in range(0, len(cand_cuts)): - if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["energy"] > 0: - if (best_cut is None or - cand_cuts[i]["energy"] > best_cut["energy"]): - best_cut = cand_cuts[i] - b = i + for i, c in enumerate(cand_cuts): + if (c["size"] + size <= k and c["energy"] > 0 and + (best_cut is None or c["energy"] > best_cut["energy"])): + best_cut = c + b = i + + # Exit iteration if no better cut is found if best_cut is None: break else: # Compute cut on left and right side - (G1, G2) = gsp.get_subgraphs(best_cut["graph"], best_cut["x"]) - best_cut["parent"].cut = best_cut["size"] - size = size + best_cut["size"] - - if nx.number_of_nodes(G1) == 1: - n = Node(ind[G1.nodes()[0]]) - best_cut["parent"].add_child(n) - elif nx.number_of_nodes(G1) > 0: - n = Node(None) - - if npol == 0: - c = one_d_search(G1, F, k, ind) - else: - c = fast_search(G1, F, k, npol, ind) - - c["parent"] = n - c["graph"] = G1 - cand_cuts.append(c) + G1, G2 = gsp.get_subgraphs(best_cut["graph"], best_cut["x"]) - best_cut["parent"].add_child(n) + size += best_cut["size"] - if nx.number_of_nodes(G2) == 1: - n = Node(ind[G2.nodes()[0]]) - best_cut["parent"].add_child(n) - elif nx.number_of_nodes(G2) > 0: - n = Node(None) + for Gi in (G1, G2): + if nx.number_of_nodes(Gi) == 1: + best_cut["parent"].add_child(Node(ind[Gi.nodes()[0]])) + elif nx.number_of_nodes(Gi) > 1: + n = Node(None) - if npol == 0: - c = one_d_search(G2, F, k, ind) - else: - c = fast_search(G2, F, k, npol, ind) + if npol == 0: + c = one_d_search(Gi, F, k, ind) + else: + c = fast_search(Gi, F, k, npol, ind) - c["parent"] = n - c["graph"] = G2 - cand_cuts.append(c) + c["parent"] = n + c["graph"] = Gi + cand_cuts.append(c) - best_cut["parent"].add_child(n) + best_cut["parent"].add_child(n) del cand_cuts[b] - # Compute remaining cuts using ratio cuts once budget is over (not optimal) + # Compute remaining cuts using ratio cuts once the budget k is over + # until the tree is completely built. (Not optimal search) for i in range(0, len(cand_cuts)): gsp.rc_recursive(cand_cuts[i]["parent"], cand_cuts[i]["graph"], ind) diff --git a/lib/static.py b/lib/static.py index ad6c64d..196a43e 100644 --- a/lib/static.py +++ b/lib/static.py @@ -10,7 +10,7 @@ class Fourier(object): """ - Graph Fourier transform. + Graph Fourier transform. """ def name(self): @@ -23,45 +23,34 @@ def set_graph(self, _G): self.U, self.lamb_str = gsp.compute_eigenvectors_and_eigenvalues(L) def transform(self, F): - """ - """ return gsp.graph_fourier(F, self.U) def inverse(self, ftr): - """ - """ return gsp.graph_fourier_inverse(ftr, self.U) def drop_frequency(self, ftr, n): """ - Keeps only the n top-energy coefficients of ftr. - Input: - * ftr: transform - * n: number of coefficients - Output: - * ftr_copy: changed transform + Keeps only the n top-energy coefficients of ftr. + Input: + * ftr: transform + * n: number of coefficients + Output: + * ftr_copy: changed transform """ - coeffs = {} - - for i in range(ftr.shape[0]): - coeffs[i] = abs(ftr[i]) - + coeffs = {i: abs(ftr[i]) for i in range(ftr.shape[0])} sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) - ftr_copy = np.copy(ftr) - + # Set the other coefficients to 0 for k in range(n, len(sorted_coeffs)): - i = sorted_coeffs[k][0] - - ftr_copy[i] = 0 + ftr_copy[sorted_coeffs[k][0]] = 0 return ftr_copy class HWavelets(object): """ - Hammond's wavelets (spectral theory) + Hammond's wavelets (spectral theory) """ def name(self): @@ -87,23 +76,19 @@ def set_graph(self, _G): self.T, gamma, lamb_max, K) def transform(self, F): - """ - """ return gsp.hammond_wavelet_transform(self.w, self.s, self.T, F) def inverse(self, wtr): - """ - """ return gsp.hammond_wavelets_inverse(self.w, self.s, wtr) def drop_frequency(self, wtr, n): """ - Keeps only the n top-energy coefficients of wtr. - Input: - * wtr: transform - * n: number of coefficients - Output: - * wtr_copy: changed transform + Keeps only the n top-energy coefficients of wtr. + Input: + * wtr: transform + * n: number of coefficients + Output: + * wtr_copy: changed transform """ coeffs = {} for i in range(len(wtr)): @@ -119,48 +104,42 @@ def drop_frequency(self, wtr, n): i = sorted_coeffs[k][0][0] j = sorted_coeffs[k][0][1] - wtr_copy[i][j] = 0.0 + wtr_copy[i][j] = 0. return wtr_copy class GRCWavelets(object): """ - Gavish's wavelet transform. + Gavish's wavelet transform. """ + def __init__(self, method='lobpcg'): + self.method = method + def name(self): return "GWT" - def set_graph(self, _G): - """ - """ - self.G = _G - (self.tree, self.ind) = gsp.ratio_cut_hierarchy(self.G) + def set_graph(self, G): + self.G = G + (self.tree, self.ind) = gsp.ratio_cut_hierarchy(self.G, self.method) def transform(self, F): - """ - """ - return gsp.gavish_wavelet_transform(self.tree, self.ind, self.G, F) + return gsp.gavish_wavelet_transform(self.tree, self.G, F) def inverse(self, wtr): - """ - """ return gsp.gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) def drop_frequency(self, wtr, n): """ - Keeps only the n top-energy coefficients of wtr. - Input: - * wtr: transform - * n: number of coefficients - Output: - * wtr_copy: changed transform + Keeps only the n top-energy coefficients of wtr. + Input: + * wtr: transform + * n: number of coefficients + Output: + * wtr_copy: changed transform """ - coeffs = {} - for i in range(len(wtr)): - coeffs[i] = abs(wtr[i]) - + coeffs = {i: abs(wtr[i]) for i in range(len(wtr))} sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) @@ -169,57 +148,48 @@ def drop_frequency(self, wtr, n): for k in range(n, len(sorted_coeffs)): i = sorted_coeffs[k][0] - wtr_copy[i] = 0.0 + wtr_copy[i] = 0. return wtr_copy class OptWavelets(object): """ - Sparse wavelet transform. + Sparse wavelet transform. """ - def __init__(self, n=0): - """ - """ + def __init__(self, n=0, method='lobpcg'): self.n = n + self.method = method def name(self): - """ - """ if self.n == 0: return "SWT" else: return "FSWT" - def set_graph(self, _G): - self.G = _G + def set_graph(self, G): + self.G = G def transform(self, F): - """ - """ self.F = F return None def inverse(self, wtr): - """ - """ return gsp.gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr) def drop_frequency(self, wtr, n): - coeffs = {} - + # The number of edges to be cut is set equal to the number of + # Chebyshev polynomials k = n # Computing optimal basis - (self.tree, self.ind, s) = oc.optimal_wavelet_basis(self.G, self.F, - k, self.n) - + (self.tree, self.ind, size) = \ + oc.optimal_wavelet_basis(self.G, self.F, k, + self.n, self.method) # Gavish's wavelet transform - tr = gsp.gavish_wavelet_transform(self.tree, self.ind, self.G, self.F) - - for i in range(len(tr)): - coeffs[i] = abs(tr[i]) + tr = gsp.gavish_wavelet_transform(self.tree, self.G, self.F) + coeffs = {i: abs(tr[i]) for i in range(len(tr))} sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True) @@ -227,12 +197,12 @@ def drop_frequency(self, wtr, n): # Computing number of integers required to represent the # edges cut (rounded) - v = n - int(math.ceil(float(s * + v = n - int(math.ceil(float(size * math.log2(len(self.G.edges()))) / 64)) for k in range(v, len(sorted_coeffs)): i = sorted_coeffs[k][0] - wtr_copy[i] = 0.0 + wtr_copy[i] = 0. return wtr_copy diff --git a/lib/vis.py b/lib/vis.py index 9b6fd08..9bcd0a8 100644 --- a/lib/vis.py +++ b/lib/vis.py @@ -203,3 +203,36 @@ def draw_time_graph_eig(G, eig, fig_output_file_name): for i in range(G.num_snaps()): os.system("rm graph-" + str(i) + ".svg") + + +def eig_vis_opt(G, F, beta): + """ + Computes first and second eigenvector of sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. + Input: + * G: graph + * F: graph signal + * beta: regularization parameter + Output: + * v1: first eigenvector + * v2: second eigenvector + """ + ind = {} + i = 0 + + for v in G.nodes(): + ind[v] = i + i = i + 1 + + C = laplacian_complete(nx.number_of_nodes(G)) + A = weighted_adjacency_complete(G, F, ind) + CAC = np.dot(np.dot(C, A), C) + L = nx.laplacian_matrix(G).todense() + + isqrtCL = sqrtmi(C + beta * L) + M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) + + (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 1)) + x1 = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] + x2 = np.asarray(np.dot(eigvecs[:, 1], isqrtCL))[0, :] + + return x1, x2 From 3836f1af37ed81ba701483d0d075aee4cd89422a Mon Sep 17 00:00:00 2001 From: Diego Sacconi Date: Sun, 24 Sep 2017 17:38:30 +0200 Subject: [PATCH 49/62] Update README.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index 192ba22..41868e1 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,13 @@ Compression experiments: ----------------------- https://nbviewer.jupyter.org/github/arleilps/sparse-wavelets/blob/master/compression-experiments.ipynb +Test: +------ +To run the DOCTEST in lib/experiments.py set PYTHONHASHSEED=0 and then +``` +python -m doctest lib/experiments.py -v +``` +
For more details, see the paper: [Graph Wavelets via Sparse Cuts ](http://arxiv.org/abs/1602.03320 "") Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K Singh, Ananthram Swami From 8c4ee9b5b892f6d40b5136e416a07ac41b60c956 Mon Sep 17 00:00:00 2001 From: Diego Sacconi Date: Sun, 24 Sep 2017 17:40:31 +0200 Subject: [PATCH 50/62] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 41868e1..3cbfe34 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,7 @@ To run the DOCTEST in lib/experiments.py set PYTHONHASHSEED=0 and then python -m doctest lib/experiments.py -v ```
+ For more details, see the paper: [Graph Wavelets via Sparse Cuts ](http://arxiv.org/abs/1602.03320 "") Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K Singh, Ananthram Swami From ba7bc199a3a324f619dd1e76afb363ea2fe7fd4e Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sun, 24 Sep 2017 22:28:54 +0200 Subject: [PATCH 51/62] Group notebooks in a folder --- lib/experiments.py | 9 +- lib/optimal_cut.py | 6 +- .../compression-experiments.ipynb | 74 +++-- notebooks/synthetic-data.ipynb | 257 ++++++++++++++++++ notebooks/test.py | 2 + 5 files changed, 301 insertions(+), 47 deletions(-) rename compression-experiments.ipynb => notebooks/compression-experiments.ipynb (99%) create mode 100644 notebooks/synthetic-data.ipynb create mode 100644 notebooks/test.py diff --git a/lib/experiments.py b/lib/experiments.py index 6faa277..3354fd3 100644 --- a/lib/experiments.py +++ b/lib/experiments.py @@ -372,7 +372,7 @@ def compression_experiment(G, F, algs, comp_ratios, num): >>> F = io.read_values(data.small_traffic["path"] + "traffic.data", G) >>> algs = [static.OptWavelets(n=5, method='lobpcg'),\ ... static.OptWavelets(method='tracemin_lu'), static.Fourier(),\ - ... static.GRCWavelets(method='tracemin_lu')] + ... static.GRCWavelets(method='tracemin_lu'), static.HWavelets()] >>> comp_ratios = [0.1, 0.2] >>> res_smt, time_smt = \ ... compression_experiment(G, np.array(F), algs, @@ -385,11 +385,8 @@ def compression_experiment(G, F, algs, comp_ratios, num): [ 0.15091373 0.05312383] >>> print(res_smt['GWT']) [ 0.1988195 0.10399963] - - #>> print(res_smt['FT']) - #[ 0.17681431 0.1036391 ] - #>> print(res_smt['GWT']) - #[ 0.20987113 0.13070782] + >>> print(res_smt['HWT']) + [ 0.27003969 0.23748869] """ results = {} times = {} diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index dcab500..e859395 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -63,7 +63,7 @@ def sweep_opt(x, F, G, k, ind): if i <= best_cand: vec[sorted_x[i]] = -1. - return vec, best_energy, best_cut_size, best_energy + return vec, best_energy, best_cut_size def fast_cac(G, F, ind): @@ -126,7 +126,7 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): x = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] res = {} - res["x"], res["energy"], res["size"], res["score"] = sweep_opt(x, F, G, k, ind) + res["x"], res["energy"], res["size"] = sweep_opt(x, F, G, k, ind) return res @@ -285,7 +285,7 @@ def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): eigvec = power_method(-M, start, 10) x = chebyshev_approx_1d(n, beta, eigvec, L) res = {} - res["x"], res["energy"], res["size"], res["score"] = sweep_opt(x, F, G, k, ind) + res["x"], res["energy"], res["size"] = sweep_opt(x, F, G, k, ind) return res diff --git a/compression-experiments.ipynb b/notebooks/compression-experiments.ipynb similarity index 99% rename from compression-experiments.ipynb rename to notebooks/compression-experiments.ipynb index 2047526..f636f22 100644 --- a/compression-experiments.ipynb +++ b/notebooks/compression-experiments.ipynb @@ -16,10 +16,8 @@ }, { "cell_type": "code", - "execution_count": 79, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -27,6 +25,8 @@ "\n", "from IPython.display import Image\n", "\n", + "sys.path.append('../')\n", + "\n", "import lib.io as io\n", "import lib.experiments as exp\n", "import lib.static as static\n", @@ -42,20 +42,18 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "G = io.read_graph(data.small_traffic[\"path\"] + \"traffic.graph\",\n", - " data.small_traffic[\"path\"] + \"traffic.data\")\n", - "F = io.read_values(data.small_traffic[\"path\"] + \"traffic.data\", G)" + "G = io.read_graph(\"../\" + data.small_traffic[\"path\"] + \"traffic.graph\",\n", + " \"../\" + data.small_traffic[\"path\"] + \"traffic.data\")\n", + "F = io.read_values(\"../\" + data.small_traffic[\"path\"] + \"traffic.data\", G)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -108,8 +106,8 @@ ], "source": [ "exp.plot_compression_experiments(res_smt, comp_ratios,\n", - " \"figs/compression_small_traffic.png\", 4)\n", - "Image(filename=\"figs/compression_small_traffic.png\")" + " \"../figs/compression_small_traffic.png\", 4)\n", + "Image(filename=\"../figs/compression_small_traffic.png\")" ] }, { @@ -183,9 +181,9 @@ }, "outputs": [], "source": [ - "G = io.read_graph(data.traffic[\"path\"] + \"traffic.graph\",\n", - " data.traffic[\"path\"] + \"traffic.data\")\n", - "F = io.read_values(data.traffic[\"path\"] + \"traffic.data\", G)" + "G = io.read_graph(\"../\" + data.traffic[\"path\"] + \"traffic.graph\",\n", + " \"../\" + data.traffic[\"path\"] + \"traffic.data\")\n", + "F = io.read_values(\"../\" + data.traffic[\"path\"] + \"traffic.data\", G)" ] }, { @@ -241,8 +239,8 @@ ], "source": [ "exp.plot_compression_experiments(res_t, comp_ratios,\n", - " \"figs/compression_traffic.png\", 10)\n", - "Image(filename=\"figs/compression_traffic.png\")" + " \"../figs/compression_traffic.png\", 10)\n", + "Image(filename=\"../figs/compression_traffic.png\")" ] }, { @@ -282,15 +280,15 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "G = io.read_graph(data.human[\"path\"] + \"human.graph\",\n", - " data.human[\"path\"] + \"human.data\")\n", - "F = io.read_values(data.human[\"path\"] + \"human.data\", G)" + "G = io.read_graph(\"../\" + data.human[\"path\"] + \"human.graph\",\n", + " \"../\" + data.human[\"path\"] + \"human.data\")\n", + "F = io.read_values(\"../\" + data.human[\"path\"] + \"human.data\", G)" ] }, { @@ -346,8 +344,8 @@ ], "source": [ "exp.plot_compression_experiments(res_h, comp_ratios,\n", - " \"figs/compression_human.png\", 100.)\n", - "Image(filename=\"figs/compression_human.png\")" + " \"../figs/compression_human.png\", 100.)\n", + "Image(filename=\"../figs/compression_human.png\")" ] }, { @@ -387,15 +385,15 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "G = io.read_graph(data.wiki[\"path\"] + \"wiki.graph\",\n", - " data.wiki[\"path\"] + \"wiki.data\")\n", - "F = io.read_values(data.wiki[\"path\"] + \"wiki.data\", G)" + "G = io.read_graph(\"../\" + data.wiki[\"path\"] + \"wiki.graph\",\n", + " \"../\" + data.wiki[\"path\"] + \"wiki.data\")\n", + "F = io.read_values(\"../\" + data.wiki[\"path\"] + \"wiki.data\", G)" ] }, { @@ -432,8 +430,8 @@ ], "source": [ "exp.plot_compression_experiments(res_w, comp_ratios,\n", - " \"figs/compression_wiki.png\", 20.)\n", - "Image(filename=\"figs/compression_wiki.png\")" + " \"../figs/compression_wiki.png\", 20.)\n", + "Image(filename=\"../figs/compression_wiki.png\")" ] }, { @@ -466,20 +464,20 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "G = io.read_graph(data.polblogs[\"path\"] + \"polblogs.graph\",\n", - " data.polblogs[\"path\"] + \"polblogs.data\")\n", - "F = io.read_values(data.polblogs[\"path\"] + \"polblogs.data\", G)" + "G = io.read_graph(\"../\" + data.polblogs[\"path\"] + \"polblogs.graph\",\n", + " \"../\" + data.polblogs[\"path\"] + \"polblogs.data\")\n", + "F = io.read_values(\"../\" + data.polblogs[\"path\"] + \"polblogs.data\", G)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -530,8 +528,8 @@ ], "source": [ "exp.plot_compression_experiments(res_b, comp_ratios,\n", - " \"figs/compression_blog.png\", 4000.)\n", - "Image(filename=\"figs/compression_blog.png\")" + " \"../figs/compression_blog.png\", 4000.)\n", + "Image(filename=\"../figs/compression_blog.png\")" ] }, { diff --git a/notebooks/synthetic-data.ipynb b/notebooks/synthetic-data.ipynb new file mode 100644 index 0000000..15c5f32 --- /dev/null +++ b/notebooks/synthetic-data.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiments Synthetic Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import random\n", + "import sys\n", + "\n", + "from IPython.display import Image\n", + "\n", + "sys.path.append('../')\n", + "\n", + "import lib.experiments as exp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Size x Time Experiments using Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sizes = range(200, 1001, 200)\n", + "\n", + "num = 1\n", + "balance = 1.\n", + "sparsity = 0.8\n", + "noise = .1\n", + "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", + "res_t = exp.size_time_experiment(sizes, balance, sparsity, energy, noise, num)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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I/QXktm3bqnfv3iFtEwDgv9LSUo0ePfr3WXdGS2oS6ar80ER1tbaV9u/fr9NP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\niqaet9nkRkK9fv16P0bUfP7eGxsArGZ1QYuBwhb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oAAAg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\nE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CAY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\nlJUikQj33HMPt912W4ApJaltS9eyUlAlpc+AXwNHAKOBGcDWZtb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/hEIhpk6dyq233hp0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NWGn9/rcrliUEnw7YbrEMagkrzBwUFkZ2fP6r45OTlRroaIiIiIiIiIiJKV1+uF3W6XDSJd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\ncnKS6urqiNsmJiaoqqpKUEUiIiJ7NzIyEhFKOn/+PPPz83s+XmlpK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IysrydtcNRZ6o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3Z169daQUFB6NmzJ7Zu3YqtW7eibt26didJyORlXnvttYJBb3dQrD+KmY3Omfdy1ibnNmnSBAcP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\ne+eh9957T5f2z58/L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3uO6dmzp3799Vc99NBDlvdkGIb++te/KjEx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\n5Iv2/OLpp5/OvhdmdlXaunWrJk+e7MsWYcKpU6fcGl+2bFkvdQIAAADAX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+ "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp.plot_size_time_experiment(np.array(res_t), sizes,\n", + " \"../figs/size_time_synthetic.png\")\n", + "Image(filename=\"../figs/size_time_synthetic.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sparsity x L2 energy Experiments using Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sparsity = [0.2, 0.4, 0.6, 0.8]\n", + "size = 500\n", + "num = 10\n", + "balance = 1.\n", + "noise = .5\n", + "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", + "res_acc = exp.sparsity_acc_experiment(sparsity, size, balance,\n", + " energy, noise, num)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FFzx+zrNv4DDzRow//vGPLtv22P/MtWnTRu+8847f19ukSRO9++67atmypUNs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\nKuN9M3PmTE2YMCFoebRu3drr+eLi4qC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qTZs2OT1ufb1ddNFFAV0/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TF9+vTo27dvRDQMzdTv+4MPPoiJEyfGD3/4w3atdcEFF8SSJUvi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\niXp/eZ1f2tH9T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CicjG8Q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\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+ "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp.plot_sparsity_acc_experiment(res_acc, sparsity,\n", + " \"../figs/sparsity_acc_synthetic.png\")\n", + "Image(filename=\"../figs/sparsity_acc_synthetic.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## L2 energy data x L2 energy Experiments using Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "energy = [10, 100, 1000, 10000]\n", + "noise = .5\n", + "size = 500\n", + "num = 10\n", + "sparsity = .5\n", + "balance = 1.\n", + "random.seed(3)\n", + "np.random.seed(7)\n", + "res_acc = exp.energy_acc_experiment(energy, size, sparsity,\n", + " noise, balance, num)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S6qmpqcrKyrI9srOz1b59++BczP8ZNWqUYmJiVF1dbbse\n+22frl69qm3btjV49RT7AIr95FyPHj1sS7W7M27cOL355psOrzeCOdXV1WrVqpXPdRQVFenQoUNu\n/64kJscBAACCacOGDQFdNXH06NHKyMho8Ot+85vf6OOPP9bJkycbPNa/cuWKVq1apVWrVtmel5aW\nZhvnjxw5UllZWSGZSHc11rUf7xcWFur06dPq2rWrz21eu3ZNW7dudRjnG22OHj1akZGRbl/bpk0b\njRgxwiHAYrzXjVkZJi8vT1999ZXD35d9XdHR0RozZkyD20Xzc/z4cT399NMeV4WJjo7WU0895Vc/\nrsIw9hITE/1q31eJiYmqqKhwe95bnQAAoHkgDAMAAICgiIqK0j/+8Q+NGTPGtrKLMbHmaaLceF79\nSbnz589r5cqVWrlype1Y//79NXHiRE2dOlVTpkxpUPCjMWJiYjRq1CiPq6esX7++QWGYoqIiHTx4\n0DYhbf9fX9oxwjCSnII5W7dubVAtru4utb/OlJQUDRgwwOf2AAAA0HDGuNhqtWrhwoVauHBhwPr6\n/e9/36gwTLt27bR8+XJNmDBBV65ckeTfWP/kyZM6ceKE/vGPf0iSIiIiNHjwYE2ePFnTpk1Tbm6u\nbWumQOrdu7e6deum06dPuw34rF+/XjNnzvS5zfoBFPtr93W8v23bNkmO4/2jR482OJjjbjUZo93M\nzEy1adPG5/bQPFmtVs2ePVtlZWUuP+fG5+Hxxx9Xenq6X31dunTJbQ2SbNsrB1pCQoLOnj3r9vzF\nixeDUgcAAAgstkkCAABA0HTr1k3r169X//79nVZS8bQ0t/E8+4f0zcS58Th06JBeffVV3XrrrUpJ\nSdGcOXNsE8WB4m1llIbeoenp+ePGjfP6+rFjxwa8loaEcwAAAGCe+uNfsx5G2/4YNmyY1qxZo9TU\nVKeVRvwd61utVn3++ed6+eWXNXHiRHXp0kWPPfaY9u3b51fNvsjNzXW7SqIUnuN9A6tAtgzPPfec\n7QaP+kE1Q1pamv7f//t/fvf11VdfeTwfrBWg4uPjPX5dV1VVBaUOAAAQWIRhAAAAEFTp6enavn27\n7r77bofJbftQjC8T8d4mzcvKyrRo0SLl5OQoOztbq1evDsj1uJsgNq5r27Ztqq6u9rk9TxPSvoRP\n+vfvr5SUFFsN9tzd+empFk9/F0yOAwAABJerMbC/DzNlZWUpPz9f48ePdxjn+zvWlxzDMcXFxZo3\nb56uu+46TZs2TTt27DD1Oux5G+83Zoxt34YhJiZG2dnZXl9/ww032FbF8We8X1NToy1btjDeb+E+\n+ugj/fKXv3S7PZLValVERIT+8pe/mBJU8bTNm8ViUVRUcDYz8NZPQ36GBwAATRdhGAAAAARdYmKi\nFi9erJUrV2rAgAFOoZj6k+WNmTSXvpkw37Fjh6ZNm6bp06fr2LFjpl7LqFGjFBMTY+vPqMNw9epV\nbd++3ef27AMo9tfcpUsX9erVy6c2xo4d63RHn9Vq1fbt2z1OPtorLi7W/v37Jble2l5ichwAACDY\nArUqjJm6du2qtWvXauHCheratavDGNndCo/eeArHrF69WtnZ2ZozZ45KS0tNvx5XY1778fGXX37p\ncbsVe7W1tU4BFONnn+zsbJ+2eW3btq0GDx7scrzfkJVh8vPzVVlZaavBaMcQHR2tMWPG+Nwemp99\n+/bpnnvucdiKzZ7x2fzxj3+sCRMmmNKnt5AJYRgAAGAmwjAAAAAImWnTpqmgoEBLlixRTk6Ow2S4\nq7tVGzJ57ypYs2rVKg0dOlT/+Mc/TLuGmJgYjRo1yuNdtb7eoVlSUmJb6t1+QtJisfi0ZLrB/rn2\ndVVVVfm8bdRnn33mdMz+/U5NTdWAAQN8rgkAAAD+C8TKMGavDmO4//77dfToUf3xj3/U4MGDXQbg\n3YVjvAVkXI31Fy1apCFDhmjTpk2mXkfv3r3VtWtXW52u+Drez8/PV0VFhSTn4IEZ4/0jR474HMzx\ntiVqVlaW2rRp43NNaF5KSkp0yy23qKysTJLj58j+cz5y5Ej95je/Ma3furo6j+cjIyNN68uffrzV\nCQAAmgfCMAAAAAi5GTNmaOPGjdq7d6/+4z/+QxkZGQ4T4Z4m7H0NxhjPvXLliu6880797ne/M61+\nb9sX+XqHpr9bJBk8TaT7Wou7CX1jcrwh9QAAAMAcgVgZJlArxEhfry7y4IMP6vPPP9fWrVv12GOP\nqUePHj6N9RsSgjeef+bMGU2aNEnvvvuuqdeRm5vrMTTk7xjb6MNXgRzvG1gFMnxVVlZq+vTpKiws\nlOQ6CGO1WpWUlKSlS5eaulqLt7ZqampM6+v/s3ff8VFV+f/H35OQntB7CZ0ICAokAUIJXcqusljA\niiDq7qJYHvu1910XfejiiiL2LKBRsCxYUASBAEFqKNJb6L2ZZghJ5vcHv5m9ybRMpqW8no/HPJR7\n51mK1vMAACAASURBVJ7zuZOZ5DPnfu45nvQTEhLilzgAAIBvUQwDAACACqNjx4566aWXtG3bNh08\neFAffPCB7rrrLnXo0EFBQUFOB80l54UxxjtHzWaz/u///k8ffPCBV+J2NFBs6WvNmjVlWp7I2cC1\nO3eKdu3aVbVr17bGYFTWu1ZdDaIzOA4AAOAfxiU0U1JSVFRU5LPHlClTfHYeiYmJmjZtmjIzM7Vj\nxw5Nnz5dt9xyi01xjKtc3x7jcwoKCnT77bfrp59+8lrsrvL98uTYpZck6t27d5njcfbdoCyxFBUV\nKT093WmREfl+1XT58mWNGTNG69evt75/LYyFMJGRkfrmm2/UvHlzr/bvaikwfxXDuPp+TjEMAABV\nA8UwAAAAqJBatGihiRMnKiUlRbt27dKFCxe0dOlSvfbaa7r11lt11VVXlSiQsTdY7ojl+ZMnT9ba\ntWs9jrV3794KCwsr0a9xUPH333/XunXrXLbjaHC8QYMGbi1JZDKZ1KdPH5uBTUthjqsBxvPnz2v7\n9u0MjgMAAMAn4uLiNHnyZH322WfKzMzUqVOntHDhQv3jH//QmDFj1KpVK4fFMc6K36Uree/ly5c1\nduxYHT582Cvx2st9jbn23r17derUKadtFBcX2xSgWM6nR48ebi1J1KBBA8XFxUkqWSxlNpvLNDNM\nRkaGcnJySpxH6eKcPn36lDkeVA5ms9laKOasECY0NFRffvmlevXq5fUYnBXDmM1mFRQUeL1Pe1wV\nw7gq2gEAAJWD9+a3AwAAAHwoJiZGycnJJaYPv3DhgtLS0pSWlqb58+dbB7tLD+gaB/mMA+hFRUW6\n6667tH37do+mfg4LC1PPnj21YsUKhwUkaWlpTgeUz58/r23bttkdHO/Xr5/bMfXv31/ff/99iXak\n/xXmJCUlOTx2xYoVJWbRkUoOjjdq1Mg6+A4AAAB4qn79+rruuut03XXXWbedOHFCaWlpWrZsmebP\nn6+zZ89KKpnrl166yJj3ZmVl6Z577tHixYs9jq9du3Zq1qyZjh8/brdf6Uq+f8sttzhsIyMjQ9nZ\n2dbjjfm1O7NAGo/ZvXu3TXt79uzR6dOn1bBhQ4fHOiqYsbSTkJCg8PBwt2Oqij766COvtxkTE+P0\nveIrkyZN0pdffum0ECY4OFhz5szR8OHDfRJDVFSU3e2WmCxFWr5m+Sw6Eh0d7Zc4AACAb1EMAwAA\ngEqrTp06Gj16tEaPHq033nhDq1at0vvvv6/PPvtMxcXFNoPMFsZCj3379mnmzJl68MEHPYplwIAB\nWrFihcP9y5cv11NPPeVwf+kCFGPcxgKgsnI1dbqzYhhHU6tb4ipPPAAAAIA7mjRponHjxmncuHGa\nOXOmFi1apHfeeUcLFy6UJJvibQvj9qVLl+qHH37QiBEjPI4nOTlZqampDi+gL1++3GmBg7Pli8qb\n7zta9tWTWCRmgTS69957vd5mq1at/F4M8/DDDyslJcVhMZflc/Pee+/p5ptv9lkcdevWdbo/KyvL\nZ32704+rOAEAQOXAMkkAAACoMvr27avZs2dr27ZtGj58uN3CEiPL/jfeeMPugKA7HA0YW/r45Zdf\nVFRU5PB4Z9OZl+dO0R49eigyMtIaQ1n7Kst+BscBAADgT0FBQRoxYoS+/fZbrV69WvHx8S5zfYvX\nXnvNKzE4y4HLsjyRoyVRg4KC1LdvX7fjcfYdwVksxcXFWrVqFUuiusG4ZJc3Hv72zDPPaPr06XYL\nYYyfo2nTpmnixIk+jaVevXpO91+8eNGn/Vv89ttvTve7ihMAAFQOFMMAAACgyomLi9P333+vqVOn\nWgcbSw86GgcBDx06pKVLl3rUZ+/evRUWFlaiL2MfeXl5WrduncPjHQ2O165dW127dnU7nho1aqh3\n794201+bzWatXr3aYWHOxYsX9euvvzI4DgAAgAqpZ8+eSk9P11/+8heHzzHODpOWlqaDBw963K+9\nHNhYjLNr1y6dOXPGYTylC1Asefq1115briVZWrRooZYtW0qSTbvOZn7ZvHmzdVYMe0uihoSEOF3e\ntboym80ePYzt+NOrr76qf/7zny4LYV566SU99NBDPo+nfv36NtuMcV26dMnns8NcuHBBBQUFNn0b\n2YsTAABUPhTDAAAAoMp67LHH9Pe//71MA47ffPONR32FhYWpZ8+eTvtydIfmb7/9pq1bt9oMYptM\npnLdJWphvFu0dGHO+vXr7R6zcuVKFRcXlzjGGFejRo0UFxdX7pgAAAAAT9WoUUNvv/22JkyYUKbZ\nYb799luP+2zXrp2aNm0qybbQ3sJRvr9582brTBSli9XLMwukRf/+/a3tlbUwx1GMluMTEhIUHh5e\n7piqqso2G4wkTZ8+XU8++aTLQpjHHntMTz/9tF9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XAAAg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\nX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5xzzvF6vob6HPYlNjZWt956qxYvXqzMzEzdf//9ql27tmPtt23bVkuW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\nu/ddk8mkd955R2+//XaW1+dqL774on755ReH7Wz335kzZ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+ "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp.plot_energy_acc_experiment(res_acc, energy,\n", + " \"../figs/energy_acc_synthetic.png\")\n", + "\n", + "Image(filename=\"../figs/energy_acc_synthetic.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Noise data x L2 energy Experiments using Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "noise = [.2, .4, .6, .8]\n", + "size = 500\n", + "num = 10\n", + "sparsity = 1.\n", + "balance = 1.\n", + "energy = 100\n", + "random.seed(3)\n", + "np.random.seed(7)\n", + "res_acc = exp.noise_acc_experiment(noise, size, sparsity, energy, balance, num)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Xy33Fx8drypQpkQqt1jwV/1itVtvFaqh5K/7xVqQEAADgq3//+9/6+uuv3S71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\nsvPo3bu3XnzxRRUXF2vDhg169dVXdc0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xcbE+++wz3X33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dmWWX8mLVu2rD755BOvtmkymfTdd98pIiIiU3vW++727dsaO3asV9uFf5Cj3hceHq7//Oc/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\nNuQ4XkfS09PNo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+ "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp.plot_noise_acc_experiment(res_acc, noise, \"../figs/noise_acc_synthetic.png\")\n", + "Image(filename=\"../figs/noise_acc_synthetic.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/test.py b/notebooks/test.py new file mode 100644 index 0000000..e7cc498 --- /dev/null +++ b/notebooks/test.py @@ -0,0 +1,2 @@ +from lib import io + From 65cc719f149a414b73bd13e360c7abc81e8b59dc Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sun, 24 Sep 2017 22:32:08 +0200 Subject: [PATCH 52/62] Remove redundant notebook --- synthetic-data.ipynb | 254 ------------------------------------------- 1 file changed, 254 deletions(-) delete mode 100644 synthetic-data.ipynb diff --git a/synthetic-data.ipynb b/synthetic-data.ipynb deleted file mode 100644 index ec98299..0000000 --- a/synthetic-data.ipynb +++ /dev/null @@ -1,254 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Experiments Synthetic Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import random\n", - "\n", - "from IPython.display import Image\n", - "\n", - "import lib.experiments as exp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Size x Time Experiments using Synthetic Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "sizes = range(200, 1001, 200)\n", - "\n", - "num = 1\n", - "balance = 1.\n", - "sparsity = 0.8\n", - "noise = .1\n", - "energy = 100\n", - "random.seed(3)\n", - "np.random.seed(7)\n", - "res_t = exp.size_time_experiment(sizes, balance, sparsity, energy, noise, num)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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I/QXktm3bqnfv3iFtEwDgv9LSUo0ePfr3WXdGS2oS6ar80ER1tbaV9u/fr9NP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\niqaet9nkRkK9fv16P0bUfP7eGxsArGZ1QYuBwhb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oAAAg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\nE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CAY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\nlJUikQj33HMPt912W4ApJaltS9eyUlAlpc+AXwNHAKOBGcDWZtb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/hEIhpk6dyq233hp0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NWGn9/rcrliUEnw7YbrEMagkrzBwUFkZ2fP6r45OTlRroaIiIiIiIiIiJKV1+uF3W6XDSJd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\ncnKS6urqiNsmJiaoqqpKUEUiIiJ7NzIyEhFKOn/+PPPz83s+XmlpK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IysrydtcNRZ6o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3Z169daQUFB6NmzJ7Zu3YqtW7eibt26didJyORlXnvttYJBb3dQrD+KmY3Omfdy1ibnNmnSBAcP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\ne+eh9957T5f2z58/L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3uO6dmzp3799Vc99NBDlvdkGIb++te/KjEx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\n5Iv2/OLpp5/OvhdmdlXaunWrJk+e7MsWYcKpU6fcGl+2bFkvdQIAAADAX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- "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp.plot_size_time_experiment(np.array(res_t), sizes,\n", - " \"figs/size_time_synthetic.png\")\n", - "Image(filename=\"figs/size_time_synthetic.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sparsity x L2 energy Experiments using Synthetic Data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "sparsity = [0.2, 0.4, 0.6, 0.8]\n", - "size = 500\n", - "num = 10\n", - "balance = 1.\n", - "noise = .5\n", - "energy = 100\n", - "random.seed(3)\n", - "np.random.seed(7)\n", - "res_acc = exp.sparsity_acc_experiment(sparsity, size, balance,\n", - " energy, noise, num)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FFzx+zrNv4DDzRow//vGPLtv22P/MtWnTRu+8847f19ukSRO9++67atmypUNs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\nKuN9M3PmTE2YMCFoebRu3drr+eLi4qC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qTZs2OT1ufb1ddNFFAV0/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TF9+vTo27dvRDQMzdTv+4MPPoiJEyfGD3/4w3atdcEFF8SSJUvi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\niXp/eZ1f2tH9T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CicjG8Q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\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- "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp.plot_sparsity_acc_experiment(res_acc, sparsity,\n", - " \"figs/sparsity_acc_synthetic.png\")\n", - "Image(filename=\"figs/sparsity_acc_synthetic.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## L2 energy data x L2 energy Experiments using Synthetic Data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "energy = [10, 100, 1000, 10000]\n", - "noise = .5\n", - "size = 500\n", - "num = 10\n", - "sparsity = .5\n", - "balance = 1.\n", - "random.seed(3)\n", - "np.random.seed(7)\n", - "res_acc = exp.energy_acc_experiment(energy, size, sparsity,\n", - " noise, balance, num)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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S6qmpqcrKyrI9srOz1b59++BczP8ZNWqUYmJiVF1dbbse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\n7piqqso2G4wkTZ8+XU8++aTLQpjHHntMTz/9tF9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XAAAg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\nX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5xzzvF6vob6HPYlNjZWt956qxYvXqzMzEzdf//9ql27tmPtt23bVkuW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\nu/ddk8mkd955R2+//XaW1+dqL774on755ReH7Wz335kzZ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- "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp.plot_energy_acc_experiment(res_acc, energy,\n", - " \"figs/energy_acc_synthetic.png\")\n", - "\n", - "Image(filename=\"figs/energy_acc_synthetic.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Noise data x L2 energy Experiments using Synthetic Data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "noise = [.2, .4, .6, .8]\n", - "size = 500\n", - "num = 10\n", - "sparsity = 1.\n", - "balance = 1.\n", - "energy = 100\n", - "random.seed(3)\n", - "np.random.seed(7)\n", - "res_acc = exp.noise_acc_experiment(noise, size, sparsity, energy, balance, num)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Xy33Fx8drypQpkQqt1jwV/1itVtvFaqh5K/7xVqQEAADgq3//+9/6+uuv3S71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\nsvPo3bu3XnzxRRUXF2vDhg169dVXdc0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xcbE+++wz3X33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dmWWX8mLVu2rD755BOvtmkymfTdd98pIiIiU3vW++727dsaO3asV9uFf5Cj3hceHq7//Oc/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\nNuQ4XkfS09PNo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- "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp.plot_noise_acc_experiment(res_acc, noise, \"figs/noise_acc_synthetic.png\")\n", - "Image(filename=\"figs/noise_acc_synthetic.png\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.4.6" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From 141ade6f5503dd3dd74d55eca1ddb44b70bb87c9 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Tue, 26 Sep 2017 10:03:53 +0200 Subject: [PATCH 53/62] Update compression-experiments.ipynb and figures --- figs/compression_blog.png | Bin 158985 -> 63521 bytes figs/compression_blog2.png | Bin 0 -> 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max_y): +def plot_compression_experiments(results, comp_ratios, output_file_name): """ Plots compression size x accuracy experiment. Input: * results: accuracy results * comp_ratios: compression ratios * output_file_name: output file name - * max_y: maximum y-axis Output: * None """ - plt.clf() - for alg in results: - for i in range(1, results[alg].shape[0]): - if results[alg][i] > results[alg][i - 1]: - results[alg][i] = results[alg][i - 1] + # Get range of values + highest_val = max([max(results[res]) for res in results]) + lowest_val = min([min(results[res]) for res in results]) + plt.clf() ax = plt.subplot(111) + legend_columns = 3 + + # Add some white space at the top to place the legend + white_space = 0.3 * (len(results) // legend_columns + + bool((len(results) % legend_columns))) + max_y = 10**(1 * math.log(highest_val, 10) + + white_space * (math.log(highest_val, 10) + - math.log(lowest_val, 10))) + ax.semilogy(comp_ratios, results["FSWT"], marker="o", color="r", - label="FSWT", markersize=15) + label="FSWT") ax.semilogy(comp_ratios, results["FT"], marker="*", color="c", - label="FT", markersize=15) - + label="FT") if "SWT" in results: ax.semilogy(comp_ratios, results["SWT"], marker="x", color="b", - label="SWT", markersize=15) - + label="SWT") ax.semilogy(comp_ratios, results["GWT"], marker="s", color="g", - label="GWT", markersize=15) - + label="GWT") if "HWT" in results: ax.semilogy(comp_ratios, results["HWT"], marker="v", color="y", - label="HWT", markersize=15) + label="HWT") - plt.gcf().subplots_adjust(bottom=0.15) - ax.legend(loc='upper center', prop={'size': 20}, ncol=3) - ax.set_ylabel(r'L$_2$ error', fontsize=30) - ax.set_xlabel('size', fontsize=30) - plt.rcParams['xtick.labelsize'] = 20 - plt.rcParams['ytick.labelsize'] = 20 - ax.set_xlim(0., 0.65) - ax.set_ylim(0., max_y) + ax.legend(loc='upper center', ncol=legend_columns) + ax.set_ylabel(r'L$_2$ error') + ax.set_xlabel('size') + + ax.set_ylim(lowest_val, max_y) plt.savefig(output_file_name, dpi=300, bbox_inches='tight') diff --git a/notebooks/compression-experiments.ipynb b/notebooks/compression-experiments.ipynb index f636f22..6c04be2 100644 --- a/notebooks/compression-experiments.ipynb +++ b/notebooks/compression-experiments.ipynb @@ -17,7 +17,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -43,7 +45,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "G = io.read_graph(\"../\" + data.small_traffic[\"path\"] + \"traffic.graph\",\n", @@ -72,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -83,30 +87,30 @@ "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_smt, time_smt = exp.compression_experiment_static(G, np.array(F),\n", - " algs, comp_ratios, 10)" + "res_smt, time_smt = exp.compression_experiment(G, np.array(F),\n", + " algs, comp_ratios, 10)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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lFl/HxGQEkJgSvYsE3SOaYjxhd6EU4UYz3zH4y72MuMwuSmFJPuA8CkEAIEm9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\nx5q9rz59+qhLly6S/BedeN7hsHHjRq/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VuBkNRWkG984xv69re/XahRgSmZXvgAmEMQhCAIAACYvem2gtAGAiCdZJDk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uZP2h9Vhtrv392rx2c+Ki7KGQ3k174+PlU6ZzqggKgigIIiIiIiIiImL0/dBqEQSp7hQEkcrG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3kEOPw6BFIiKwuefhomTgRXv/Jq08bYHSQ+3vzeRERETuF0Olm2bxmJKYks2LQAh9P97+9r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\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/FCwecHxbRDqtkC7Pz0/HEsetNFSq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qDB0GkwtH+EwJwMInK/Jj97LWXlrn25YPvJ7Ww/uZ1nvnyGTk06kRiTSEJsAkPbDcXsZ/bwpxAR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WrVtHeHh4hfUjIiIiIiJVSFgYAb98jlGH/8rGr/3Z+sgfedrPRQAlX1TMpTox+3vR3dmB3sHf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\nX6RVq1Y5WoeUKJKh74sXL84RRxzBJZdcwpgxY/jss8/C/oE3ffp0Lrvsshy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"text/plain": [ "" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.plot_compression_experiments(res_smt, comp_ratios,\n", - " \"../figs/compression_small_traffic.png\", 4)\n", + " \"../figs/compression_small_traffic.png\")\n", "Image(filename=\"../figs/compression_small_traffic.png\")" ] }, @@ -114,56 +118,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### FSWT x GWT" + "### Reconstruction Error: SWT vs GWT" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 1.37420233, 2.25282744, 2.45563924, 4.13498675, 3.74994093,\n", - " 5.76065338])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " GWT error| SWT error| Reduction\n", + "-----------------------------------------------\n", + " 0.2258367| 0.1280286| -0.4330920\n", + " 0.1142181| 0.0512101| -0.5516460\n", + " 0.0530751| 0.0226301| -0.5736211\n", + " 0.0279236| 0.0080920| -0.7102105\n", + " 0.0107338| 0.0031385| -0.7076054\n", + " 0.0045087| 0.0015076| -0.6656293\n", + "\n" + ] } ], "source": [ - "np.divide(res_smt['GWT'], res_smt['FSWT'])" + "reduction = np.divide(res_smt['SWT'], res_smt['GWT']) - 1\n", + "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'SWT error', 'Reduction')\n", + "text += \"-\"*47 + \"\\n\"\n", + "for i in range(len(comp_ratios)):\n", + " text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_smt['GWT'][i], res_smt['SWT'][i], reduction[i])\n", + "print(text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### SWT x GWT" + "### Reconstruction Error: FSWT vs GWT" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 1.19979191, 2.75735034, 3.38438616, 4.34140944, 4.56971652,\n", - " 5.93517785])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " GWT error| SWT error| Reduction\n", + "-----------------------------------------------\n", + " 0.2258367| 0.1269770| -0.4377488\n", + " 0.1142181| 0.0474275| -0.5847640\n", + " 0.0530751| 0.0212761| -0.5991323\n", + " 0.0279236| 0.0082267| -0.7053843\n", + " 0.0107338| 0.0042162| -0.6072049\n", + " 0.0045087| 0.0012525| -0.7222055\n", + "\n" + ] } ], "source": [ - "np.divide(res_smt['GWT'], res_smt['SWT'])" + "reduction = np.divide(res_smt['FSWT'], res_smt['GWT']) - 1\n", + "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n", + "text += \"-\"*47 + \"\\n\"\n", + "for i in range(len(comp_ratios)):\n", + " text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_smt['GWT'][i], res_smt['FSWT'][i], reduction[i])\n", + "print(text)" ] }, { @@ -175,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -188,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -207,39 +229,39 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_t, time_t = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_t, time_t = exp.compression_experiment(G, F, algs, comp_ratios, 10)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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imx0TraZzMbbWefvtt80tyM8YUwSTyspKl/fHxsb6qRLvysnJcRna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\n3oM6bVdtaXXIIYdsawX9ZuzqPMmCBQu2hWPaayV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oVUp9P1QYBqAE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\nz+fjlBTesdnYlOW+8Q0g0GLh7og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zTZg+HU6edF9Xpw78618wdCgUNk1ImSqwF/D59s+Z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\nSCoicAkjkn79FYYMgcREKleuzNy5c/noo4/46KOPmDNnDiEhIbB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DlAp5p+QOHDigixcvSnJfSOS4zNqxY8fUp08f9evXL++7mqLmIsk5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\nd9F3ABiNvmO88+fPq2fPniXfKLQXhKl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Hb0x7/RNgOoZhyOeLyOeLSFo57fHJ\nUCc6FNhcmGAdnYvj9jlOn/cXkkO23S3b7tbAwO/SOt6ySiZcO8eyymTbXVkbVyBQo5qaj2btfAAA\nAAAwHxVsSHP+/Hm5ritJKi8vJ6ABFoJXPy1V3CKV3SSVNUm+krSaxV1XL/X26qXeXv3d0Lawaerm\nkhJtjkRSFTcri4pkGoZ34wcWoIaGzykUasz3MLCAJUOdEvl8JSoqWp5WG9vumzTAmSjcse1ej68i\nt2y7V7bdq4GBE57209DwGVlWkad9AAAAAMBcV7AhTUlJ8uasYRhqaGjI82gA5ETvG8mf05JkSMUr\npLL1Q6HNeilYmfap+hxH+7q7ta+7O7WtzLJ067X1bYbCm/pgUAbBDTBjpaW35HsIQMYsKyzLalAo\nlN53TNsemLY6Z+S+bFajzGXHjv0/OnHic/L5KuTzlQ+ttTP8enj7RNvKx03LBgAAAADzUcGGNEuX\nLs33EADklStFf5f8Ofuj5KZQTTKsKV+f/G+oRsogYOmybf3y6lX98urV1Lbr/P5R69tsjkS0JDC7\nBZgBAPOLZYVkWXUKherSOt5xBhWPd6Y19Vo8flGJxBWPryBfXCUSl5VIXJ5Ra9MMTRHiTBf8lMkw\nrCxfDwAAAABkX8GGNGvWrJGUnGf89OnTeR4NgIIwcDb5c/6nyfeBRcNVNmXrpeLlUoY3ZC7G4/rJ\n5cv6yeXhG0jLgsHU2jabhn4q/P5sXgkAYB4zzaCCwVoFg7VpHe84ccXjlxSPXxgT5owMd4b3JUMP\n19uLKACOM6BY7JxisXMzam9ZkSkCnYmrd669tqxiKm0BAAAA5ETBhjTr1q3TunXr1NbWpitXruiF\nF17Q1q1b8z0sAIUkdlm6uDv5I0lWcXItm2vBTelqycy8KuatwUG9NTioH3V2pratLCoaVXFzS2mp\nii2e0AUAzJ5p+hUMVisYrE7reMdJKJG4PGGAM3G4c0mS4+1FFCDb7pZtd2twsD3jtobhS3tatomC\nH3MG3z8ALyUSXervPyHLCss0w7KsYllWWIYRIJAEAADIs4INaSTpwx/+sB5++GFJ0he+8AX99Kc/\nzfOIABQ0OypdfiH5I0mGX4qsGV7XJrJO8hXP6NRv9vfrzf5+PXXhgiTJlLQmHNbmSCQV3mwoKVHQ\nNLN0MQAATMw0fQoEFisQWKziNP5Zc11b8fiVMQHOhQmmYRvetxBDnZFcN6F4vFPxeOf0B0/ANMPj\nAp2Jp2WbKASKyDD4PoFsM3T48K4J1swyh4Kb4hEBTjLEufb6Wqgz9v3I40e3H72PIAgAAGBqBR3S\nfPSjH9UPf/hD7du3T88++6weeeQRPfHEE/keFgCPvGv1u3TgzAF19HZk54RuXOr6TfJHkmRKJdcP\nhzZl66VAxYxO7Uhq6+tTW1+fWs4lp2HxG4bWFxcnq22Gwpu14bB8BDcAgDwyDEuBQJUCgSpJa6Y9\n3nUdvfrqu3T58jPeD26ecpw+xWJ9isXOzqC1IZ+vLK1p2SYKfkyziBviGMfni6i29iNqb//6mD2O\nbLtXtt2reNybvg0jqC1bXldR0XJvOgAAAJjjDNd1C3pC66tXr+pd73qX9u7dK8MwdMcdd+hLX/qS\ndu3ale+hAaO0tbWpqakp9f7IkSNat25dHkc0cxejF7X4icU57/fCIxdUFa7S8SvH1dreqtZTrdrT\nvkdvXn7Tu06L6kavaxOqlrJ4Y6PINHVzScnwVGmRiFYVFcnk5gkAoIB1de3XoUO3ZdQmErlN69b9\nu2z7qhKJq0okrgz996ri8eHXw9tHb3PdhEdXs7AYRiDtadnGvy6TabIO33w1OHhO+/c3yHVjOe23\nuvqPdeONT+a0TwAAgOkU0r3cgq6k+dKXviRJ2rlzp44dO6bz589r3759etvb3qYlS5Zo06ZNWr58\nuSKRiPwZLur9+c9/3oshA5glwzB0/aLrdf2i69W8sVmSdK73nFpPtSaDm/ZWvXLuFbnZWjC5/3Ty\n59xPku8DVaMrbYobpVlMOdLvOHquu1vPdXentkUsS7cOrW1zLbxpCIV46hUAUDDKyraprGy7urpa\n026zYsXXFAwulpT5gx6u68px+iYJcyYOdkYeO34Kp4XLdWNDaxFdmFF7yypJc1q28ZU8llXK95kC\nFgxWq7r6g+ro+E7O+jSMgBob+d0bAABgKgVdSWOa5rgv+SOHO5tfAGzbnnFbLGwtLS1qaWkZtz0a\njergwYOp91TSZO7CIxd0XfF10x7XNdCl504/lwptXjzzomK2R08E+kqlSNNwaFN6g+TBE6bX+f3J\nSptrFTelpaoOBrPeDwAA6ers/LGOHHlnWsdWVLxdGzb8zOMRTc51bSUS3RMGO+kEP44zkLexzy/m\niNAm8/V4LCuU7wuY9/r6jurFF9dI2XrgaRq1tR/TqlXfzklfAAAAmaCSZhZm+2SW67o83YVZOXny\npHbv3p3vYSxoZaEyvWPVO/SOVe+QJA0kBvTimRdT1TbPnX5OPbGe7HSW6JEuP5/8kSQzKEXWDoc2\nkbWSVTTrbi7G4/q/ly/r/16+nNq2LBhMBTabS0t1a2mpFmVYNQgAwExVVt6jcHit+vpem/bY5cu/\nkoMRTc4wLPn9FfL7Z7bWnG0PyLa70pqWbaJKHokHwJIcJRKXlUhcnv7QCRhGcJJAJ531eMpkGFaW\nr2f+CYdXq6rq3ers/JHnfZlmkerrH/e8HwAAgLmu4EOaAi70wQLV2NionTt3jts+tpIGuRPyhbSj\nYYd2NOyQJCWchH5z/jejpki7EJ3ZlB/jOIPS1UPJH0kyLJklq+Sk1rW5SfKXZaWrtwYH9dbgoP69\nszO17fpQSJsjkVR4c0tJiUp8Bf9XOQBgDjIMU3V1n9LRox+a8rjKyvsUiWzJ0ai8YVkhWVZIgcCS\njNu6rivb7k1rWraJ9tt2lh4smQdcd1Cx2DnFYudm1N6ySqeZlm3yih7LKl4wD/PV1T2ak5Bm2bKH\nFQxWe94PAADAXFfQ0515Wa0w0U12YDYKqURutgp9urNMua6rY5ePqfVUq/a071HrqVaduHoi6/1c\nEypZrlhp01Bwc5MU8u6XU0PSmnA4WW0zFN5sKC5WyOJJUgDA7DlOTPv3L1csdnaSIwxt2vSKSkpu\nyum45hPHSci2u8YEOlNP0TbyuFwvAj9fGYZvwqna0l2PxzQD+b6EjBw6tCOjNacyZVkRbdt2Qn7/\nIs/6AAAAmI1Cupdb0I9fE6QAyAbDMHRD5Q26ofIG/fEtfyxJOtN9JlllM1Rtc+TCEblZmpt7oPeE\n1HtC6vg/kqTi8FL5yzeoq3iN3LL1UrhBytKTmq6k1/r69Fpfn75//rwkyW8Yuqm4eHiqtEhE68Jh\n+UwzK30CABYO0wxo2bJP6PjxT024f/HiBwhoZsk0fTLNSvn9lSqawQyqtt2f5rRsE+/P1dokhc51\nE4rHOxWPd05/8ARMsyjNadkmCn4iMozcfk+rq3vU05Cmru4RAhoAAIA0FXRIAwBeqY3U6oGmB/RA\n0wOSpCv9V7Tv9L5UaHPg7AElnERW+or2dUh9HZJ+KkkqDlaoovJmDZSuU2d4jVSySjKz99dx3HX1\ncm+vXu7t1d93dEiSQqapm0tKUuvbbCot1Q3hsMwFMq0HAGDmamo+rFOnviLb7hqzx1Jj4xfzsvAu\ngQAAIABJREFUMiYMs6wiWVaRgsGlGbd1XUe23TPFtGxTT+HmOFEPrmhucpx+xWL9U1SdTcWQZUVm\nvB6PaRZlPFVbJmtOZcrvr9KyZX+e9fMCAADMV4Q0AMapDFfqwiNZWsMlw37zpaKoQvfecK/uveFe\nSVJfvE8vvPVCak2b508/r2g8OzciooNXFD37S0m/lCSF/MVaVrVRgYoN6gyv0YXQSskKZaWvawYc\nR893d+v57u7Utohl6dahwOZaeNMQCi2Y+dgBAOnx+SKqrf2I2tu/Pmr70qUfUji8Kk+jQjYYhimf\nr0w+X5lCoYaM2ztOPM1p2Sbaf0Wum50HYuY+V7bdNUEQmh7D8Kc9LdvI/9bU/JnefPPjWb4Wqb7+\nMfl8pVk/LwAAwHxV0GvSAHNJIc1jiOyL23EdPnc4Fdrsbd+rzr6ZTYcxHb/p1/XXbdCiqlsUjzTp\nZGClLmoG85/MQJXfnwptrv13aTCYk74BAIVrcLBD+/c3ptY/MYyAtm49plCoPs8jw1zluq4cp2+C\nadkmrt4ZG/zMNNCAtwKBGm3d+qYsKzffXQEAAGaqkO7lzotKmt7eXvX09Ki0tFQlJSX5Hg6wMLmu\n1NMjxWJSICCVlmZt3ZVC4Lf82ly7WZtrN+svbvsLua6rNzrfUGt7q/ac2qPW9la1d7Vnpa+4E9cb\n5w9K5w+mtq2uWqfGJZsVrNigy8VrdSQR1tVE9p8+7YzH9dPLl/XTy5dT22oDgWRgE4mkwptFfn/W\n+wYAFK5gcKmqqz+gjo4nJUk1NX9GQINZMQxDllUsyypWMFibcXvXtZVIdKc1LdtE+x2n34OrgmH4\n9Oqr96YqtCyrLPV68veRGU3ZBgAAMF/MuUqanp4e/dM//ZP27Nmj/fv36/Tp07JtO7XfsizV19dr\n27Zt2rlzp973vvcR3CAnCil9zZlXX5Weekp68UXp5ZelK1eG91VUSLfcIm3ZIj34oDTif5v5qr2r\nPbWmTWt7q167mP05vq9ZXr5cG2tvU1XVrYpHmvSmqnSot1dRx/Gsz5FWhELJKdIiEW0qLdUtJSUq\n9c2L3B8AMIm+vqN68cU1Ms0ibdt2XIHAknwPCZgxxxkcEeCMDHTSW49HsqftA+kzDL8sK5JGoDP5\ne9MME/QAAIC0FdK93DkT0vT19emzn/2snnzySUWjyXUhphr6tS9nJSUleuihh/TlL39ZRUWUXMM7\nhfTB9twzz0jf+IbU2pp+m+3bpccek+65x7txFZjOvk7ta9+XCm1eOvuSbNebX+gXFy/WHXV36oal\nWxUq36DzgTq9FO3XK729iuXgr3lD0ppweHh9m0hEG4qLFbIsz/sGAOTOkSO/r3D4Rq1Y8bV8DwXI\nG9d1ZdvRKadlmyr4se2efF/CPGVNWKWTSeBjWSUEPZgXEoku9fefkGWFZZrhocrFsAwjwJ9xABhS\nSPdy50RI88orr+j+++/X7373u1Qwk84/KiOPXblypX74wx9qw4YNno4VC1chfbA9c+mS9LGPJatn\nZurBB6Vvf1uqrMzeuOaI3liv9r+1P1Vts/+t/epPeDPVRmmgVLfV3aY76rZr6eJNGgiv0iv9cR3o\n6VFbNJqTZz99hqGbiotHrW+zrrhYftPMQe8AAC/09LysUKhRfv+ifA8FmLMcJyHb7sp4PZ5rx7ru\nYL4vYR4z5fNF0p6mbeJ9pTIMvu8ivxKJbj3/fP0E63eZQ8FN8YgAJxniXHt9LdQZ+37k8aPbj95H\nEARgriike7kFH9IcPXpUd955py5duiQpGbiMHHJpaakqKytVXFysaDSqS5cuqadn+MmkkcdXVVVp\n3759WrVqVW4vAgtCIX2wPfGb30jveId09uzsz1VTI/30p9JNN83+XHNYzI7p5Y6XU6HN3va9ujJw\nZfqGMxCwAtpcs1nb67drc93tKqnYoNdjpg50d+tgT4+O9udmXvaQaWpjSUmy2mYovFkdDsvkSzwA\nAEBabHtg0hDn2uvJg58uSbmZHnfhMmRZpTOatm14yreIDIOKdMzO8eOfVnv713Per2EEtWXL6yoq\nWp7zvgEgE4V0L7egQ5p4PK6mpiYdO3YslcK7rqtt27bpj/7oj/S2t71Ny5eP/0v/xIkT+uUvf6nv\nfe97ev7550e1Xb16tV599VX5WDsBWVZIH+ys+81vpF27Rq85M1sVFdLu3Qs+qBnJcR29dvE1tZ5q\n1Z72PWo91aozPWc86cuQofVL1mt7/XZtb9iuDTW36YxKdLCnRwd6enSgu1unBnPzlGapZenWEaHN\n5tJSNYZCPH0FAACQZa7ryLZ7x1Xn9PW9rhMnPiOpYG8PLDiWVZLxujxjAx/T5L7HQjY4eE779zfI\ndWM57be6+o91441P5rRPAJiJQrqXW9Ahzbe+9S39xV/8RaoaJhKJ6O///u/13ve+N+1zPP3003ro\noYfU3d0t13VlGIa++c1v6uGHH/Zw5FiICumDnVWXLknr12engmasmppkALQApz5Lh+u6Onn1ZHJN\nm6Fqm6OXjnrW3/UV12tHw45UcBMprtNLvb060NOTCm/OxXLzBb/S50sGNpFIKrypCQZz0jcAAMBC\ndOTIf1dn548ybtfQ8AWFwzcokehSItE1NJVb15j33aNeU82TG6ZZnKrMmWngY5r+fF8GZuHo0Q+r\no+M7OevPMALauvWYQqH6nPUJADNVSPdyCzqkueGGG1Lr0ITDYe3Zs0e33HJLxuc5fPiw7rzzTvX3\n98t1Xa1cuVK//e1vPRgxFrJC+mBn1YMPzm4NmnTO/4//6N3555kL0Qva2743FdocOndIjuvNL7nV\nJdXJwGYotGm6rknn4olRoc3Bnh5dSSQ86X+smkAgOU1aJKJNQ8FNpZ9fGgEAALKhq2u/Dh26LaM2\nkcjtuvnmvRlVQLuuO1TNM1mgk877biknqyzCNIumCXWmD4BMk4et8qWv76hefHGNclUlV1v7Ma1a\n9e2c9AUAs1VI93ILNqQ5duyYVq9enfqy91d/9Vf65Cc/OePzPfHEE3r00UclJdepeeONN1ibBllV\nSB/srHnmGenee73v58c/lv7bf/O+n3moe7Bbz59+Pllt096qF956QYO2N9OUlQXLdHvd7alqm001\nmxSwAjo+MKAD3d2p0Oalnh5Fndw8HbkiFEpNkbY5EtEtJSUqZTpLAACAGTl0aIe6ulrTPn7jxl+r\nvHynhyOamOu6cpy+GQY8w+9dNzcPGy10hhGc4bRtkREVPUyHPFMzrZLLlGkWaevW4woGqz3vCwCy\noZDu5RZsSPPDH/5QDzzwgCQpEAjo3LlzKi8vn/H5rl69qiVLligej8swDP3gBz/Q/fffn63hAgX1\nwc6aHTuk1vR/SZtVP7t3e9/PAjCYGNTBswdToc2+9n3qGuzypK+gFdTWZVtT1Ta3192u0mCpbNfV\nG319yWqbofDmcG+vYjn458aQdGM4PGp9m40lJQpZLLwKAAAwnc7OH+vIkXemdWxFxdu1YcPPPB6R\nd5JBT/+EVTqZBD65Xu9joTIMf8br8ox9b5pFCzLomUmV3EzU1z+mFSu+5nk/AJAthXQvt2AfN75w\n4YKkZNXL8uXLZxXQSFJ5eblWrFiho0eT6zmcP39+1mME5rVXX81NQCNJe/ZIR45II/5ixMwEfUHd\nUX+H7qi/Q4/pMdmOrSMXjmjPqT2p4OZc77ms9DVoD2rPqT3ac2qPJMk0TG2s3pgKbd7RsF0frL5B\nkhRzHB2JRoenSuvu1pFoNOuTVLiSXu/r0+t9ffrfQ3/P+wxDTcXFyWqbofCmqbhYftPMcu8AAABz\nW2XlPQqH16qv77Vpj12+/Cs5GJF3DMOQZYVlWWEFg0tnfB7bHkgj1OmeMvBxnIEsXtn85LpxxeOd\nisc7Z3wOw/ANhTaRGQc+llU854KesrJtKivbnlGVXKYsK6K6uk95dn4AmO8KNqTp7e1NvY5EIlk5\nZ2lpaep1NBrNyjmBecvLdWgm6++rX81tnwuAZVraUL1BG6o36GNbPybXdfW7K79LrWnT2t6qNy+/\nmZW+HNfRyx0v6+WOl/U3L/yNJGl15erUmjbb67frw0sb9ac1NZKkPtvWK729OjC0vs2B7m4d7e/P\nylhGSriuDvf26nBvr77T0SFJCpmmNpaUDE+VVlqq1eGwzDn2CxcAAEA2GYapurpP6ejRD015XGXl\nfYpEtuRoVIXNskKyrJACgSUzPofjxGY0bdvI8Mdx+rJ4VfOT6yaUSFxSInFpFmexJgx5LCuSduBj\nWSUyjNw+MFZX96inIU1d3SPy+xd5dn4AmO8KNqSpqqqSlCxBPnPmTFbOefbs2dTrysrKrJwTmLde\nfHF+97dAGYahlYtWauWilfrQzclfvjt6OrS3fa9a21u159Qe/eb8b+RmaWHJo5eO6uilo3ry0JOS\npNrS2lRgs71+u7YuXqfbyspSx3cnEnppKLQ5OPTfkwPZf7JwwHG0v7tb+7u7U9tKLEu3lpRocySS\nqrhZHmLuawAAsLAsWfKgTpz4jGKxs5McYWj58i/ndEzznWkGFAhcp0Dguhmfw3Hio0KbmazT4zg8\nzDo9W4nEFSUSV2ZxDiPDUGeiYyMZBT2ZVMllyu+v0rJlf5718wLAQlKwIU3N0JPWktTR0aEjR46M\nmiMuU21tbaNCmpHnBzCG60ovv5zbPl96KdkvN8RzbmnpUt2/7n7dvy65TtfVgat67vRzqWqbA2cP\nKGZnZ67tMz1n9IMjP9APjvxAklQRqtAd9XekQptba27VXRUVuquiItXmYiyWCm6u/ZyLZX/u717b\n1u6uLu3uGl7Dp9Ln06Zr69sMhTc1wWDW+wYAACgUphnQsmWf0PHjE09dtHjxAyopuSnHo8J0TNMv\n06yU3z/zB1IdJzEU9GS2Ls/I97bdk8Wrmq9c2Xbyf7PBwZmfxbJKM5q2rarqPrW3Zz+kqa9/TD5f\n6fQHAgAmZbhuDlZynoGuri5dd911su3kigW///u/r6effnrG57v//vv1r//6r5Ikv9+vixcvZm0a\nNUAqrMWmZq27WxpR3ZDTfkv5cldoBhIDevHMi6nQ5rnTz6kn5s0vX0W+Im1bti01Rdq2ZdtUEigZ\nd9yZwUEd6O5OVdsc6OnRlUTCkzGNtTQQGLW+zeZIRJV+f076BgAAyIVEolvPP18v2+4as8fSli2v\nKxxelZdxofC5rq1EomeSEKc7zcCnW8pSZT+8FQgs1ZYtb8rnC+d7KACQsUK6l1uwIY0kve1tb9Ov\nfvUrSckper7whS/o85//fMbn+epXv6rPfe5zqSlr7r77bj377LNZHStQSB/sWevslK6bean9jO3Y\nIf2X/yLddZe0aZMUCOR+DJhWwknolXOvpNa0aT3Vqot9Fz3pyzIs3bL0llRoc2f9naoKV407znVd\nnRgYSK1tc7CnRy/19qp3KOj32vJQaNT6NreUliriK9hiVQAAgGkdP/5ptbd/fdS2pUv/RKtXfydP\nI8JC4bqObLs343V5xu6TnHxfyoJgGL7UFGzDU7NFUtuGp2ybaNvwPtPk938AuVVI93ILOqTZs2eP\ndu3aJcMw5LquDMPQO9/5Tv31X/+1rr/++mnbHz9+XI888oj+4z/+Q5JS5/j1r3+t7du3ez18LDCF\n9MGetXxV0owUDkt33int2pUMbW69VaJaoSC5rqvfXvrtqNDmxNUTnvW3pmqNdjTsSAU39WX1Ex5n\nu66O9vUNr2/T3a3Dvb0azME/e4ak1eHwqIqbjSUlKrIsz/sGAADIhsHBDu3f3yjXTU4zaxgBbd16\nTKHQxN+9gELiuq5sOzrjaduuTfnmurmp1odkmqExwc3o4GeiAGj8tlIZBr9zYWFKJLrU339ClhWW\naYZlWcWyrLAMI8Bau5MopHu5BR3SSNIHPvAB/cM//MOooMYwDN155526++67tX79elVVVam4uFjR\naFSXLl3SK6+8ol/+8pfau3evXNdNtZOk97///fr+97+f56vCfFRIH+xZc12pslK6MpvFELOspCQZ\n2tx1VzK4ueUWiUqFgnWm+0wqsNnTvkdHLhzxrK/6svrUmjbbG7ZrTdWaSb+AxBxHbdFoaoq0gz09\nerW3V7mot/EZhpqKi0dV3DQVF8tvpr/gJwAAQC4dPfqQOjqelCTV1n5cq1b9TZ5HBOSO67pynP4Z\nBDyj37tuPN+XsqBYVklalTtTbbOsEm5qY86ZfKpScyi4KR4R4CRDnGuvr4U6Y9+PPH50+9H75moQ\nVEj3cgs+pInH47rnnnv0i1/8IvV/9sjQZSojj3NdV29/+9v1zDPPyMeNXXigkD7YWfF7vyf94hf5\nHsXkIhFp+/bhSpuNGyWqFArW5f7L2te+L1Vtc/DsQSUcb55Kqyyq1J31d2p7/XbtaNihm5feLJ85\n+d/7/batw729o9a3OdrXl5NZsIOGoY0lJdociaTCm9XhsKw58OXmcjyuP3xt9MKj/7x2rRZR8QYA\nwLzR13dUL764RqZZpG3bjisQWJLvIQFzSjLoGRg3NVumgY/rDub7UhYYU5ZVOsNqnuFjTTM0J29c\nY+6aaKrSXDCMoLZseV1FRctz3vdsFNK93IIPaSQpFovp05/+tL71rW+NC14mM/IY0zT1iU98Ql/9\n6lcVYI0LeKSQPthZ8fjj0te+lu9RpK+sLLmmzbVKmw0bJCoUClZfvE8vvPVCKrR5/vTzisajnvRV\n7C/WbXW3paptti7bqrB/6oUtuxMJvTyi2uZAT49ODAx4Mr6xSixLt5SUJKtthsKbFaHC+3L//545\no/9x7NjobatW6SO1tXkaEQAA8MKRI7+vcPhGrVgxh343AOYZxxmc0bo8I987Tn++L2PBMQx/WpU7\n003xZpo8CIf0DA6e0/79DampSnOluvqPdeONT+a0z2wopHu5cyKkuebgwYP65je/qX/7t39TLDb9\nH7ZAIKA/+IM/0Cc+8QndeuutORghFrJC+mBnxauvSuvX53sUM1dRMRza3HWX1NREaFPA4nZch88d\n1p5Te9Ta3qq97Xt1qf+SJ335Tb9urbk1FdrcUX+HFhUtmrZdZyyml3p7daC7O1Vx05HGv0XZsMjn\n06ahtW2uhTe1wWBO+p7Mlpde0oGentHbSkv1Av/eAgAwr/T0vKxQqFF+//TflwAULseJpdba6e19\nRW1tfyDlZP4AzFZyvZ7pK3emnuKN9XoWiqNHP6yOju/krL+5vGZdId3LnVMhzTVdXV16/vnn9cIL\nL+jUqVO6cuWKent7VVJSooqKCjU0NGjbtm3atm2byvK9+DkWjEL6YGfNjh1Sa6v3/dx8s/SHfyj9\n6lfS3r1S1IOKispKaefO4enR1q2TCqwyAcMc19EbnW+o9VRrqtqmvavds/6aFjdpR/0ObW9IBje1\nkfSqQc4ODiYDm+7uVMXN5URuFhddGgiMWt9mU2mpqnJULdoWjarpwIGJ923erLXFxTkZBwAAAICZ\nOXLkv6uz80cZt1u79l8UiWwdquDpHlXNM/m2rqGAqHuoqqfPgyvCdIbX65m6cmeqKd4sq7jgZnnA\naNemKs1VCFtb+zGtWvXtnPSVbYV0L3dOhjRAISqkD3bWPPOMdO+9uennnnuSr+Nx6eDBZGDz618n\nQ5t+D8qyr7suGdpcmx5tzRpCmwLX3tU+KrR57eJr0zeaoeXly1OBzfb67bqh8oa010I7MTAwvL5N\nd7de6u1Vr217NtaRGkOhVGCzubRUt5aWKuLBOmyP/u53+l+nT0+8r65O37j++qz3CQAAACB7urr2\n69Ch2zJqE4ncrptv3jvrm/SOk0gFOsnp2LrHhDldY8KeibZ15XxKJ0jj1+vJpJqH9XpyZaYhbKZM\ns0hbtx5XMFjteV9eKKR7uQUb0ti2reiIp+mLiorkZzFiFLBC+mBn1YMPSk895e35//EfJ98fi0kH\nDgyHNvv2SV6sDbJkSTKsuVZpc8MNhDYFrrOvU3vb96aCm5c7XpbtehOGLC5erDvr70yFNhuqN8hn\nphd+OK6ro319o9a3OdTTo8Ec/fO7uqhImyORVHhzc0mJiqyZl7knHEd1+/fr3CRTvS0NBNS+bZt8\nTC8IAAAAFLRDh3aoqyv92TM2bvy1yst3ejiizCTX6pm+cmeiAGjkNik3D9Vh2PB6PVNX7kw3xRvr\n9UxsJiHsTNTXPzan16wrpHu5BRvSfO9739NDDz2Uev/ss8/q7rvvzuOIgKkV0gc7qy5dSq5Nc/Zs\n9s9dUyP95jfJqcjSNTgovfBCMrD51a+k559Pbsu2pUuHA5tdu6SVKwltClxvrFf739qfCm32v7Vf\n/QlvFscsDZTq9rrbk6FNw3Ztqd2ikC+Udvu446gtGk2tbXOwp0evRqNK5OCfZEtSU3FxstpmKLxp\nKi5WYChUcV1XnfH4pO1/dfWq/vC1qauYfrh2rXaVl0+6v8rv56kpAAAAIM86O3+sI0femdaxFRVv\n14YNP/N4RLnnuq4cpy+typ2ppnOz7Z7pO0PWjVyvZ6rKnamnc5uf6/VkGsJmyrIi2rbtxJxes66Q\n7uUWbEjzta99TZ/5zGckSeXl5bp8+XKeRwRMrZA+2Fn36qvJqcGuXMneOSsqpN27pZtumt15Bgak\n/fuHK232709W32Rbbe3o0GbFCkKbAhezY3rp7Eup6dH2tu/V1YGrnvQVsALaXLM5FdrcUXeHykKZ\nrYnWb9t6pbd3eKq0nh690deXk1lkg4ahDSUl2lxaqsV+v75w6pSn/R3etEkbSko87QMAAADA1FzX\n0YEDN6mvb/qppG+55QVFIltyMKq5yXUd2XbvjKt5rm1jvZ78SK7XM3nlzlTVPIW6Xk8mIexMNDZ+\nSY2Nn/Ps/LlQSPdyCzak+du//Vs9/PDDMgxD69ev16FDh/I9JGBKhfTB9sSrr0r/9b9mp6Kmpkb6\n6U9nH9BMpK9vOLT51a+kF19MrnOTbXV1w4HNXXdJjY3Z7wNZ5biO2i60pUKb1lOtOtNzxpO+DBla\nv2S9djTsSAU31SWZz9HanUjoUG+vDnR3pypujnsx3V+Ofb6hQV9cvjzfwwAAAAAWvI6OFh09+qEp\nj6msvE833fTvORrRwuY4cdl2z4yqeUYGP6zXkw/mBFU66VbzDAdAphnMStiTSQibKb+/Slu3HpfP\nV5r1c+dSId3LLdiQ5umnn9Z73/teQhrMGYX0wfbMpUvSxz8u/dM/zfwcDz4offvbmU1xNhvRqPTc\nc8PTox04ICUS2e+nsXF0pU19ffb7QFa5rquTV0+qtb1Ve07tUWt7q3576bee9bdy0crUmjbbG7br\n+orrZ/TF61I8roPX1rcZCm/OelE95qGm4mK9unlzvocBAAAALHiOE9P+/csVi032QKahTZteUUmJ\nBw9ZwjPJ9Xqmr9yZboo3ycn3pSw449frmbhyZ7op3kzTr3Pnvq833mjO+hivv/4J1dV9MuvnzbVC\nupdbsCFNW1ubbhp6yn7RokXq7OzM84iAqRXSB9tzzzwj/dVfSXv2pN9mxw7pL/9Suuce78aVjt5e\nad++4enRDh6UbA8WCVyxYji0ueuu5HRpKHjne89rb/veVLXN4XOH5bjefCmtLqlOhTY7GnaoaXGT\nLHNm8+CeHRxMTZN2Lby5lGkY6TpSvHtG/WfKlPTqls26zh9QZbhSpmHmpF8AAAAA47W3P6Hjxz81\n4b7Fi9+ntWtn8aAm5qyJ1+tJv5qH9XryyzSLZJqlSiQuS8rew8qBQI22bn1TllWUtXPmSyHdyy3Y\nkEaSbrrpJrW1tckwDD333HPaunVrvocETKqQPtg5c+SI9NRTySnFXnpp9Jo1FRXSrbdKW7ZI73uf\nNOJ/m4LS3S3t3TtcafPyy/8/e/ceF1d954//NTPc5gokBBLuMXdIQsIdQiCk1WpbrX6tdbXbmq6u\nq/USu+t3bbVq4nartrb91rWut1bs/qJdtWtTtbpGJdwDIVcCuV+4hgAhwFy4zpzfHwfmQkgC4ZyZ\nM/B6Ph7nYWaYOe9P0p4EzmvenzfgkOGm/OLFntujLVggfQ2SXN9gH6qaq5yhTXVLNQbtg7LUCg0O\nxbr4dc7gJj06HcEBwVd1LkEQcGZgwGO+zR6zGebLBZJDPUDVLVe5+qvX8WgH5unneb0uERERERGJ\nRkb6UFUVD7u9d9xXNMjMPAydbolP1kUzgzivx3zV3TxjX+O8HmVYsuRlxMTc7+tlSEJJ93IVHdK4\nz6W59tpr8emnn/p6SUSXpKQL2ycEQexSGRwEgoMBgwFQ0MC0SevtBcrKXJ02+/aJvzepLVvmuT1a\nVJT0NUhygyOD2N22G2WNYmhT0VyBvkF5uk9CAkKQGZPpDG1y43JhDL76/V4dgoBjNpsztKk1m7HP\nYsHAWCjJkIaIiIiIaNY6deonaGp6zuO5BQvuwbJlr/toRUSeXPN6Lt+5c/kt3nohCDLMLZ4lQkIW\nIjPzCNTqIF8vRRJKuper6JDGbrdjw4YNqKiogEqlwo9+9CO88MILvl4W0YSUdGGThC5ccIU2xcXA\ngQPy1FmxwhXYbNgAzONNa39gd9hR11HnDG3KmsrQbmmXpZZapcaa+WuQH5+P9QnrkRefh0h95LTO\nOexwoN5qRa3ZjNLORvzXX3IlWu3kbfx6MXIjEpBmMCDNaERssDRDEomIiIiIaPIGB89i165E58B5\nlSoIWVnHERLCeas0s0w0r2ey3TyzfV7P8uVvYf787/t6GZJR0r1cRYc0ANDT04ObbroJ5eXlUKlU\nWLduHZ555hls2LDB10sj8qCkC5tkdP68OItnbHu0ujp56iQnu+bZ5OcDERHy1CFJCYKAkxdOoqyx\nDKVNpShrLMPJCydlq7ds7jKx0yZB7LZJDEu86oCj09qJyBemF/pclZwPgKAw58N5gYFIMxqdoU2a\n0Yg4BjdERERERLI7evQfcfbsGwCAmJiHsWTJb328IiJlcs3ruZpuHtdr/Wlej063AhnnTDnDAAAg\nAElEQVQZdVCprm6WrhIp6V6uokOaZ555BgAwPDyMN954A+fOnXPepImKikJ6ejoWLlwIk8mEwMDA\nKZ37qaeekny9NLsp6cImL+rqAkpKXNuj1dfLU2f1atf2aPn5wJw58tQhyZ01nxW7bEa7bQ6eOwgB\n8vzTG2OMcQY26+PXIzkyGWqVelLvVUpIM5GIwECP0CbNaEQ8gxsiIiIiIknZbEdRU7MCarUW2dmn\nEBTEbbmJ5OQ+r+dKnTuXfq4XDke/7GtNSnoPkZHflr2ONynpXq6iQxq1Wn3RDRj35U7n5oz9csOT\nia6Cki5s8qGODjGsGeu0OXJE+hoqFZCS4toeLT8fCLv8TW5Sjp6BHlQ2VzpDm91tuzFkH5KlVnhI\nOPLi85zdNqkLUhGkmXjvWCWHNBOJCAxEqntwYzAgISSEwQ0RERER0TQcOnQLdLrluOaaZ329FCKa\npEvP63F18/T3n8LZs69d1fkNhrVIS6uFapIfAvUXSrqX63chzXQJggCVSsWQhiSnpAubFKS93TO0\nOXZM+hpqNbB2ravTJi8PCA2Vvg7Jon+4HzWtNc6ZNpXNlbAMWWSppQ3QIjs22xna5MTmQB+kB+B/\nIc1E5gYEINUttEkzGpHI4IaIiIiIaNLM5r0ICUlEYCB3byCaaQ4d+j/o6vpgyu9btepjzJ37dRlW\n5FtKuper+JBGDgxpSA5KurBJwVpbPbdHO3FC+hpqNZCW5uq0ycsDjEbp65AsRhwjONB+wBnalDWW\nodPWKUstjUqD1AWpWB+/HilRKbhr+12y1LksCUOaicwZC27cum4WMrghIiIiIiKiWaa3dxf27cuZ\n0ntMplysXVs+I3+GVtK9XEWHNCUlJbKdu6CgQLZz0+ykpAub/Ehzs2enzenT0tfQaID0dDG0KSwE\n1q0D9Hrp65AsBEHAsfPHnKFNaWMpzvSc8fWyJHPTjaU4NBSIUwMDXqsZHhDguVWa0YhrGNwQERER\nERHRDLdvXz56e8sm/fo1a3YiLGxm3kdX0r1cRYc0RO56enrw8ssv44MPPsCpU6fQ39+P6OhoFBYW\n4r777kNaWppP16ekC5v8WGOjK7ApLgaamqSvERAAZGa6tkfLzQV0OunrkGxa+lqcM23KmspwqOOQ\nr5d01Toe7cA8/TxcGB7GXosFe8xm53HSi8FN2LjgJp3BDREREREREc0wXV0f4dChGyf12vDwa5GS\n8pnMK/IdJd3LZUhDfqGmpga33norWlpaJvy6RqPB008/jSeffNLLK3NR0oVNM8jp056hzSWugWkJ\nDASyslzbo+XkAFqt9HVINt393ahoqnCGNrVttRhxjPh6WZMyFtJMpGd8cGOx4ER/v9fWNj64STMY\nsEirZXBDREREREREfkkQHNi9exVstoYrvjY1tRomU6YXVuUbSrqXq9iQxm63w2q1Oh9rtVoEBgb6\ncEXkK6dOnUJWVha6urqgUqlw77334rbbboPBYEB1dTWee+45nD17FgDw4osv4qGHHvLJOpV0YdMM\nJQjAqVOueTbFxUBbm/R1goLEoGas0yYrCwgJkb4OycY6ZEV1a7Wz26aqpQq2YZuvlzWhy4U0E+kZ\nHsa+seBm9L/HvRjchGo04owbtzk3i7RaqBncEBERERERkR84e7YIR4/+4LKvmTv3W1i16i9eWpFv\nKOlermJDmj/84Q/4x3/8R+fjHTt2YOPGjT5cEfnKzTffjO3btwMAXn31Vdx7770eX29ubkZaWho6\nOzuh0+lw/PhxREdHe32dSrqwaZYQBOD4cVdgs3Mn0N4ufZ2QEM/QJjMTCA6Wvg7JZtg+jH3t+5yh\nTXlTOc73n/f1sgBMPaSZSO/ICPa5hTZ7zGYc82JwYxoLbty6bhYzuCEiIiIiIiIFcjiGsGvXQgwN\nXeqDvyqkpx+AwbDKq+vyNiXdy1VsSPPss8/iiSeeAACEhYWhu7vbxysiX2hoaHBeHHl5eSgrm3iw\n1RtvvOEM9f71X/8Vzz//vNfWOEZJFzbNUoIAHD3qGdp0dEhfR6sV59iMbY+WkSF235DfcAgOHOk6\ngtLGUnGLtMYyNPc1+2QtUoQ0E+kbGXF13JjNqPVBcLPWfas0oxFLGNwQERERERGRAjQ1vYBTp/7v\nhF+LjLwDSUlve3lF3qeke7kBPqk6CQaDAQCgUqmQkJDg49XQVJw8eRI1NTVoaWnB0NAQwsPDsXz5\ncuTm5iJkilsmvf/++85fu3dWjffd734Xmzdvhs1mw/vvv++TkIbI51QqYPly8bjvPjG0OXzYFdjs\n3Al0dU2/Tn8/8MUX4gEAOh2Ql+fqtElLE+fckGKpVWokzUtC0rwk3Jd+HwBgb9tepL2e5vW1/OXI\nX3D94usRFxon6XlNAQEoCAtDQViY87nxwc1Yx40cn1bps9tR0tuLkt5e53PG8cGNwYClOh2DGyIi\nIiIiIvKq6Oh70dj4M9jtveO+okFi4lafrGk2U2xIs2DBAl8vYUZobW1FTU0NqqurUVNTg9raWpjN\nZufXExIScObMGUlq/eUvf8G//du/Ye/evRN+3WAwYNOmTXj66acRERExqXOWlJQ4f3257e60Wi2y\ns7Px5Zdf4tSpU2hubkZcnLQ3/Ij8jkoFJCWJxwMPAA4HUF/v6rQpKQGk6FK02YDPPhMPADAYxNBm\nrNMmNRUIUOw/NzRK6pBksu79SNzCMtYUi9y4XOTE5iA3Lhdr5q9BkEbaDq2Jghvz+ODGYsFRm02W\n4MZst6O0txelbsGNwT24Gf3vUp0OGgY3REREREREJJOAABNiYu5HU9NzHs8vWPAD6HRLfLSq2Uux\n253V19dj1Spx37s5c+agS4pPf88SFRUV+NWvfoXq6mq0XWGouBQhzeDgIO6++25s27ZtUq+fN28e\n3n//feTn51/xtfPnz8e5c+dgMpnQ2zs+2fX00EMP4aWXXgIAfPrpp/ja1742qfVIRUktckST4nAA\ndXWuTpuSEqCnR/o6RiOwfr0Y2hQWAmvWABqN9HVoWjqtnYh8IdLXy3AKCQhBRnQGcuNyneGNHNui\nTcQ8MoL9Y8HN6H+PyBTcTESvVmPtuBk3yxjcEBERERERkYQGB89i165ECMIQAEClCkJW1nGEhMT7\neGXeoaR7uYr9aHNycjKSk5NRX1+PCxcuoLq6GllZWb5ell/YvXs3PvjgA6/UcjgcuP3227F9+3aP\n5zUaDeLj4xEaGorTp097BCydnZ244YYb8PnnnyMnJ+eS5x4cHMS5c+cAYFJdMe6vaWxsnOpvhWj2\nUauBlBTxeOQRwG4HDh4UQ5viYqC0FOjrm34dsxn429/EAwBCQ4H8fNf2aCkp4lqI3AyMDIjzcppc\ns8iWzFni0W2TNC8JGrX0gZ8xIADrw8Kw3q3jxjIW3Lh13Ryx2eCQvDpgdThQ3tuLcrd/O/VqNdaM\nm3GznMENERERERERXaXg4AWYP//7OHv2DQBAdPR9syagURrFhjQAcO+992Lz5s0AgKeffhqffvqp\nj1fk/wwGAywWi2Tn++Uvf3lRQHPffffhySefRHR0NAAxyNm+fTseeeQRNDU1AQBsNhu+853v4NCh\nQwgNDZ3w3O7bso3NKLoco9E44XuJaJI0GmDtWvH4538WQ5t9+1zbo5WViYHLdPX2Ah9+KB4AEB4u\nhjZj26OtWsXQhiZ0vPs4jncfx1sH3gIAmIJNyI7NRm6s2G2TFZsFU7BJltqGgADkhYUhzy24sdrt\nro6b0eOwjMFNRV8fKtyCU9344MZgwHKdDgG8foiIiIiIiGgS4uIexdmzv4darUVCwuO+Xs6speiQ\n5oc//CHeffddVFRUYMeOHXj00Ufxwgsv+HpZfsNoNCItLQ0ZGRnIzMxERkYGTp8+jcLCQknOf/78\nefz7v/+7x3PPPvssfvzjH3s8p1arccsttyAzMxN5eXnO7dVaWlrw61//Glu3TjyMqr+/3/nroKAr\nzyUIDg6e8L1EdJU0GiA9XTwefRQYGQH27nVtj1ZWBlit069z4QKwfbt4AMCcOUBBgWt7tKQkhjY0\nob7BPnx28jN8dlKch6SCCquiVjlDm5y4HCwKXwSVTN0meo0G60JDsc7twwZWux0Hxs24abBaZQlu\nbA4HKvv6UOkW3Gjdg5vR/65gcENEREREREQT0OmWISLiW9DpliMoKMrXy5m1FB3SaDQafPjhh7jp\npptQXl6O3/zmN6ipqcEzzzyDDRs2+Hp5inXjjTfiuuuuw/Lly6Eed1Pm9OnTktX5xS9+4dGxkp+f\nj8cee+ySr4+JicEbb7yBr371q87nfvOb3+Dhhx/G3LlzL3q9Vqt1/npoaOiK6xkcHJzwvUQkkYAA\nIDNTPB57DBgeBmprXZ02FRWAzTb9Ot3dwAcfiAcARES4tkbbsAFYsQLgFk80AQECDp47iIPnDuKV\nPa8AAObp5jnn2uTG5SJtQRq0gfL9G6HXaJAbGopct+DG5h7cjP63wWqFXYb6/Q4Hqvr6UDUuuEkx\nGDxm3CQxuCEiIiIiIiIACQlPIiQk0dfLmNUUHdI888wzAICCggIcP34c586dQ0VFBb7yla8gKioK\n6enpWLhwIUwmEwIDA6d07qeeekqOJSvCokWLZK/hcDjw5ptvejy3ZcuWK35a+Stf+QrWr1+PsjJx\nxoDZbMa7776L+++//6LXum9fNpkt2txf4/5eIpJJYCCQkyMeP/kJMDQE7N7t6rSpqAAGBqZfp6sL\neP998QCAyEhXaFNYCCxdytDGjx1+4DBOdJ9AZXMlKpsrUdNag/4R6bohO22d2H50O7YfFTu1AtWB\nSF2Q6hHcRBujJas3EZ1Gg5zQUOS4BTf9Y8GNW9dNvYzBza6+PuxyC25C1Gqk6PVIMxqRzuCGiIiI\niIho1jIaU329hFlPJQiC4OtFXIparb7opr/7cqezfYndLsdtEOXbuXOnx3ZnCQkJzu3HpqK8vBzr\n1693Pr7mmmtw4sSJSf1v8tZbb2HTpk3Ox9dddx3+93//d8LXRkVFoaOjAyaTCb1uA5Qn8tBDD+Gl\nl14CAHzyySe4/vrrJ/E7kU59fT1WrlzpfHzo0CEkJyd7dQ1EijI4CNTUuEKbykrxOaktWCCGNmPB\nzeLFDG2uQqe1E5EvRHq9bsejHZinn+d8PGwfxsFzB8XQpkUMbpp6m2RdQ0JoAnLicpzbpK2OWo1A\nzdQ+/CGFfrsdB61Wjxk39TYbRrz0rZp7cOPecRPI4IaIiIiIiIhmGCXdy1V0J81EpruvvCAIsu1N\nP5t8/PHHHo+vvfbaSf+5XnvttR6Pd+7cCavVCr1ef9Frk5OT0dHRgb6+PrS0tCA2NvaS521oaPB4\nHxH5WHAwsH69eDz1lNhVs2uXa3u0XbvE7pvpOnsWeOcd8QCAmBjP7dGuuYahjR8J1AQiLToNadFp\neCjrIQBAS18LqpqrnMHNvrP7MOwYlqxmY28jGnsb8adDfwIA6AJ1yIzJdIY22bHZmKu7eFtOqWk1\nGmSZTMgymZzPDYwPbiwWHLJaZQluBhwOVJvNqHbbyjRYpRK3ShsLbgwGJOv1DG6IiIiIiIiIJKL4\nkEbBjT6z2v79+z0e5+bmTvq90dHRSExMdHbwDA0NoaGhARkZGRe9tqCgAMXFxQCA4uJifO9735vw\nnP39/di1axcAYOHChYiLi5v0eojIS0JCXB0vW7YA/f1AVZWr06a6WpxzM12trcC2beIBAHFxrsCm\nsBBITJx+DfKqWFMsbku+Dbcl3wYA6B/ux56ze5xbpFU2V6LT1ilZPduwDTvP7MTOMzudzy2PWI7c\n2Fyx4yYuF8sjlkOtkj+oCNFokGkyIXNccFM3FtyMbpdWJ1NwMygIqDGbUTMuuFk9FtyM/jdZr0cQ\ngxsiIiIiIiKiKVN0SDN2c56U5/Dhwx6Pk5KSpvT+pKQkj23WDh8+PGFI8+1vfxtbtmwBALz++uuX\nDGnefvtt2EYHln/729+e0lqIyEe0WmDjRvEAAKvVFdoUF4vzbUZGpl+nuRn44x/FAwASElzzbDZs\nAOLjp1+DvEobqEVefB7y4vMAiB/oOHnhpEdoc6jjEARIF1oc6TqCI11H8If9fwAAhIWEISc2xznX\nJjMmE4Ygg2T1LidEo0GGyYQMt+Bm0OFA3bgZN3VWK4ZlCm52m83Y7RbcBI0FN25dNysZ3BARERER\nERFdkaJDmoKCAl8vgSbQ39+PpibP+QBT7VwZ//qjR49O+Lrk5GTceOON+PDDD1FWVobXXnsN9957\nr8drmpub8fjjjwMAtFotNm/ePKW1EJFC6PXAV78qHgBgsQAVFa7t0WprASnmiTU2AkVF4gGI26G5\nb492mW0VSZlUKhUWz1mMxXMW4/sp3wcA9A70oqa1xrlF2q6WXegb7JOsZs9ADz458Qk+OfEJAECt\nUiMlKgW5cbnO8CYxLNFrW6wGq9VIN5mQPi64OTTacVMrc3AzJAioHa2Ds2cBiMHNqnEzblbq9Qhm\ncENERERERETkpOiQhpSpq6vLYxu6wMBAREZObdh0TEyMx+OOjo5LvvbXv/41Kioq0N3djfvuuw/7\n9u3DbbfdBoPBgJqaGvz85z93vv/nP//5Ree+Gh0dHejsnNrWOSdOnJh2XSJyYzAAX/uaeABAX58Y\n2ox12uzdCzgc069z6pR4/EHskMDixa7AZsMGIDp6+jXI60JDQnHtomtx7SJxDprdYUdDZ4MztKls\nrsSJbun+3nYIDuxr34d97fvwu92/AwDMN8wXO21GZ9ukLkhFcECwZDWvJFitdoYjYx9vcA9u3Dtu\nhmQKbvaMdveMBTeB44MbgwGrDAYGN0RERERERDRrMaShKbNYLB6PdTrdlD8prNfrL3tOd4sXL8bH\nH3+MW2+9FW1tbXjllVfwyiuveLxGrVbjySefxCOPPDKldVzKyy+/jK1bt0pyLiKSiMkE3HCDeABA\nby9QVubqtNm3D5DiRvOJE+Lx+uvi46VLXdujFRQA8+dPvwZ5nUatwaqoVVgVtQr/lP5PAIAOawd2\ntexybpG2u203BkYGJKvZbmnH/xz+H/zP4f8BAARpgpAene4x22a+wbv/f3IPbsYMjQ9uLBYctFhk\nCW6GBQF7LRbstVjwultws3IsuBndLm2VXo8QjUby+kRERERERERKw5CGpmx8oBISEjLlc2i12sue\nc7zs7GzU19fjd7/7HT744AOcPHkSAwMDWLBgAQoLC3H//fcjPT19yusgIj8WGgp885viAQA9PUBp\nqRjY7NwJHDggTWhz7Jh4vPqq+HjFCs/t0ebNm34NBZirm4uORy/d1ShnXV+J1EfipmU34aZlNwEA\nhuxD2N++32O2Tau5VbJ6Q/Yh53lRJT63MGyhc65NblwuVkauRIDau9+eBanVSDUakWo04h/H1upw\noH4suBmdc3PQYsGgTMHNPosF+ywWvDH6XMBYcOM242Y1gxsiIiIiIiKagRjS0JQNDHh+yjgoKGjK\n5wgO9tzupb+//4rvCQsLwxNPPIEnnnhiyvWIaBYICwNuukk8AKC72xXaFBcDdXXS1Dl8WDz+8z/F\nx8nJrsCmoACIiJCmjpepVWrM08+MwOlqBWmCkBmTicyYTDySLXZmNvc2u0KblkrsO7sPdkGC2Uij\nTvecxume09hWtw0AYAgyICsmyxnaZMdmIywkTLJ6kxWkVmOt0Yi1RiPuGX1ueCy4GQ1t9pjNOCBT\ncDMiCNhvsWC/xYLft7cDEIObZJ3OY8bNar0eWgY3RERERERE5Mf8NqTp7u7G4cOH0d3djd7eXjgc\nDnzta19DVFSUr5c2443vnBkaGpryOQYHBy97Tl/74Q9/iNtuu21K7zlx4gRuvvlmmVZERFM2Zw5w\n883iAQBdXUBJiWt7tPp6aerU14vHSy+Jj1evdnXa5OeL6yC/FRcah9tDb8ftK28HAFiHrKhtq/WY\nbdPd3y1ZPcuQBV+c/gJfnP7C+VzSvCTnXJvcuFwsnbt0ytuMSiFQrcYaoxFrjEbcvWABADG4abDZ\nPGbcHLBaMSDFvKhxRgQBB6xWHLBa8YfR4EYDIHncjJsUg4HBDREREREREfkNvwppOjo68NJLL+HP\nf/4zjhw5ctHXd+zYMWFI8+abb6K5uRkAEB0djXvuueei19DkGQwGj8fjO2smY3znzPhz+lpkZCQi\nIyN9vQwiklJEBHDrreIBAB0dYmgztj3a4cPS1Dl4UDxefBFQqYCUFFenTX6+2PFDfksfpEdBYgEK\nEgsAAIIg4Hj3cY8t0uo7JQoARzV0NqChswFv7BM3A5urnSvOtBkNbjJiMqAL1Elac7IC1WqkjAYj\n/+AW3Bx2D25GO2LkCG7sAA5arThoteJNt+AmadyMmxSDAToGN0RERERERKRAfhPS/PKXv8RTTz2F\noaEhCBNsq3G5T5RaLBZs2bIFKpUKGo0GN954IztupmF8oGKz2SAIwpQ+1Wu1Wi97TiIi2UVGArfd\nJh4A0N4uhjVjnTbHjk2/hiAA+/eLx29+I4Y2a9eKoU1hIZCXJ87WIb+lUqmwdO5SLJ27FJvWbAIA\nXOi/gOrWamdoU91aDcvQ5WevTcX5/vP46NhH+OjYRwAAjUqDNfPXeMy2iTPF+aTbBhCDm9UGA1Yb\nDPjBaHAz4h7cjG6Xtt9iQb9MwU2d1Yo6qxVFo89pAKwYN+NmDYMbIiIiIiIiUgDFhzR2ux233XYb\ntm/fPmEQoFKpJgxt3N1999148skn0dfXB7vdjrfffhs/+tGP5Fz2jBYREeHx5z48PIyOjo4pBV+t\nrZ6DmNm1QkQ+N38+8Hd/Jx4A0NbmCmx27gROnJh+DUEA9u4Vj1/9ClCrgbQ01/ZoeXmA0Tj9OuRT\n4dpwXL/4ely/+HoAgN1hx6GOQx5bpJ26cEqyenbBjj1n92DP2T34j5r/AADEGGM8Qps189cgSDP1\nGXJSCVCrscpgwCqDAZtGnxtxOHDEZvOYcbPfYoFNpuDmkNWKQ1Yr3jp3DgCgBrBCp0O624wbBjdE\nRERERETkbYoPaR544AH85S9/AeAKZNauXYvrrrsO8fHxeOCBB654Dp1OhxtvvBHbtolDef/2t78x\npJkGrVaL+Ph4NDY2Op9ramqaUkjT1NTk8Xj58uWSrY+ISBLR0cCdd4oHADQ3e26PdkqCm+wOB7B7\nt3j88peARgOkp7u2R1u3DlByp6EgAGYzMDQEBAWJAZOPujeUTKPWIGV+ClLmp+D+jPsBAO2WdlQ1\nVzmDm9q2WgzZpz7j7VJaza14r+E9vNfwHgAgJCAE6dHpzi3ScuJyEKn37QckAtRqrDQYsNJgwF3z\n5wMA7IIgBjduM272yRTcOADU22yot9kuCm7SxgU3egY3REREREREJBNFhzTl5eV47bXXnN0zERER\nKCoqwg033OB8zQMPPDCp7TxuvvlmbNu2DYIgoKKiAkNDQwgK8t0nSv3d8uXLPUKahoYGZGRkTPr9\nh8fNfmBIQ0SKFxcH/P3fiwcANDZ6bo/m9nfiVbPbgepq8XjuOSAgAMjIcG2PlpsL6Hwze8Sprg54\n5x2gpkbsCLpwwfW18HAgNRXIzBTDrZUrfbdOhZtvmI9bVtyCW1bcAgAYHBnE3rN7UdUiBjcVzRVo\nt7RLVm9gZADlTeUobyp3Prd4zmKx02Y0uEmalwSN2rdhhEalQrJej2S9Ht93C26Ojptxs89shlXm\n4OaPbsHNcvfgxmDAGoMBhgBFfxtNREREREREfkIlXGmvMB/auHEjdu7cCQAwmUzYtWvXRTfz1Wq1\nM6TZsWMHNm7cOOG5WlpaEB8fD0DsyNm/fz9WrVol3+IVaufOnSgsLHQ+TkhIwJkzZ6Z8nh//+Md4\n/vnnnY/vvfdevPrqq5N679mzZxEdHe18HBgYiO7ubr+fS1NfX4+VbjckDx06hOTkZB+uiIi86swZ\nMawZO1papK8RGAhkZbm2R8vJAbRa6etM5OOPgeefB8rKJv+e9euBH/8Y+PrX5VvXDCUIAhp7G51z\nbSqbK3Hg3AE4BOmDiTGmYBOyYrKcW6RlxWQhNESZM5PsgoBj42bc7LNYYLHbvVJfBbfgZnTOzVoG\nN0RERERERH5DSfdyFfuT5IULF1BWVuYMYH76059Oq9siNjYW4eHhuDD6id8jR47MypBGKt/85jc9\nQprPP/98wplBE/nss888HhcWFvpVQFNUVISioqKLnrdard5fDBEpR2Ii8IMfiIcgiNuhjXXZFBeL\nM26ma3gYKC8Xj5/9TNxiLDvbtT1adjYQEjL9Ou7OnwceekjsnpmqsjLxuPNO4MUXgblzpV3bDKZS\nqZAYlojEsETcuUrccs8yZEFNa424TdrobJuegR7JavYN9mHHqR3YcWqHuAaosDJypcdsm0Xhiyb1\nb73cNCoVVuj1WKHXY7S3DXZBwPFxM272yhTcCAAO22w4bLPh/xvtuFEBWKbTOUObseDGyOCGiIiI\niIiILkOxPzWWl5fDPvpDtUajwT333DPtc0ZGRjpDmo6OjmmfbzbLzc1FREQEurq6AACnTp26qEvn\nUn7/+997PP7Wt74lyxrlcubMGZSUlPh6GUSkZCoVsGiReNx9txjanDjhmmdTXAy0S7CV1dAQUFoq\nHlu3AsHB4pZoY502mZnic1fr4EHghhumHzC9/bb4+/70U4AfkLhqhiADNi7ciI0Lxa5hh+DA0a6j\nrm6blkoc6ToiWT0BAuo66lDXUYdX94jdsvN088SZNrE5yI3LRXp0OrSBXurmugKNSoXlej2W6/X4\n7uicPIcg4Hh/P/aYzah1m3Fjlim4OWKz4YjNhm2j32eqACzVaj1m3Kw1GGBicENERERERESjFPsT\nYtvoDSGVSoVrrrkGYWFh0z5naKhryw6z2Tzt881marUamzZtwgsvvOB8buvWrdiwYcNlP2H7xRdf\noMxtqxyj0YjvfOc7sq5VaomJiSgoKLjoeavVitraWh+siIgUT6UCliwRj3vvFdiJsoAAACAASURB\nVEObo0ddgc3OnYAUHx4YHHR17jz9tLgVWm6uq9MmI0PsvpmMgwfF97jPnJmOtjagoAAoKWFQIxG1\nSo0V81ZgxbwVuDv1bgDAedt57GrZhcrmSlS1VKG6tRq2YZtkNTttndh+dDu2H90OAAhQByB1Qapz\nrk1uXC5iTDGS1ZsutUqFZTodlul0uHOC4Ma940au4OZofz+O9vfjbbdr3CO4MRiQajQqPrjpHh7G\n7Q0NHs/9d1IS5gQG+mhFREREREREM4Nifxrs7u52/nrOnDmSnHNwcND560D+QDltjz32GF555RVY\nLBYAQElJCZ5//nn8+Mc/nvD1ra2tF3VEbd68GREREbKvVUqbNm3Cpk2bLnp+/D6GRESXpFIBy5eL\nx333iaHN4cOuwGbnTmC0U3Fa+vuBL74QDwDQ6YB168TQprAQSEsT59yMd/682EEjVUAz5sIF4Prr\nxQCIW5/JYq5uLr6x9Bv4xtJvAABGHCM4eO6gx2ybxt5GyeqNOEZQ01qDmtYa/L/q/wcAiA+N9+i2\nSYlKQaBGOd93XSq4OeEe3Fgs2Gs2o0+mGTfH+vtxrL8f77gFN0vGgpvR7dJSjUaEKii4+VNHBz4f\n93fCf3d04P4Y5YRyRERERERE/kg5P/mNI0fXi/sWZ/4WDExVRUUF+vv7L3r+wIEDHo8HBgbw+eef\nT3iO6OhoJCUlXbJGREQEHn/8cTz++OPO537yk5+gqakJP/3pTxEdHQ0AcDgc+Otf/4rNmzejqanJ\n4/z/8i//MqXfFxHRjKRSAUlJ4vHAA4DDATQ0uLpiSkoAtw8vXDWbDdixQzwAwGAA8vJc26OlpgIB\nAeIMGilm6EykrQ14+GFg2zZ5zk8exjpdUhek4sHMBwEArX2tqGqpcnbb7Gnbg2HHsGQ1m3qb0NTb\nhD8d+hMAQBugRWZMprPTJic2B3N1ygrp1CoVlup0WKrT4Q634ObkWHAzOudmr9mMXpmCm+P9/Tje\n348/uX2/ulir9Zhxk2owIMxHHzQqmmCLxqL2doY0RERERERE06QSBEHw9SIm8t577+H2228HAOj1\nevT29kKtVl/0OrVa7dxea8eOHdi4ceOE52tubkZCQgIAcQu1Tz/9FNdee61Mq/e9xMRENDZO75Oy\nd911F4qKii77GofDgW9961v46KOPPJ7XaDRISEhAaGgoTp8+jZ4ez8HGWq0WO3bswLp166a1RiUZ\n30lz6NAhJCcn+3BFRDRjOBxAXZ1re7SSEqBHuoHxTkYjsGwZ4I2tGz/6CPjGN+SvQ1c0MDKAPW17\nnHNtKpsr0WGVd3bfsrnLPLptVsxbAbXq4u/zlMYhCDjV3+8Mbca2SusZGfHaGhaFhHjMuEk1GBAu\nc3BTb7Vi5e7dE38tIwNJer2s9YmIiIiIiKSmpHu5iu2kSUlJcf7aZrOhoqIC69evv+rzvffee85f\nazQaZGdnT2t9JFKr1Xjvvffwgx/8AH/605+cz9vtdpw6dWrC98ydOxfvv//+jApoiIhkpVYDKSni\nsXkzYLeLW4aNbY9WWgr09k6/jtnsnYAGAH7xC4Y0ChESEIJ18euwLl78d1kQBJy6cMq1RVpLJerO\n1UGAdJ/rOXr+KI6eP4o3978JAAgLCUN2bLZztk1mTCaMwUbJ6klFrVJhsU6HxTodbo+MBDD65zUw\n4DHjZo+Mwc3JgQGcHBjAu52dzueucQ9uRjtvpAxu3pqgi8b9a88vWiRZLSIiIiIiotlGsZ00ALBo\n0SKcOXMGAHDTTTfhgw8+uOg1k+mk6evrQ3JyMtpGt27Jzs5GRUWFfAtXAG910rj785//jJ/97GfY\nv3//hF/X6/W466678PTTTyNy9MbGTKKk9JWIZhm7Hdi/37U9WlmZGLgoXV0dwFlefqFvsA81rTXO\n4KaqpQp9g32y1VOr1FgdtdoZ2uTE5WBh2ELn93xKJwgCTrsHN6OdNxe82HGzcCy4cdsubc5VBDcj\nDgfidu1C+9DQhF9fEBSEpuxsBEzQ8U5ERERERKRUSrqXq+iQZuvWrdi6dSsAcYuyP/zhD7jrrrs8\nXnOlkMZut+PWW2/FX//6V+d5/vjHP+K73/2uF34Hs9OJEydQXV2N1tZWDA0NISwsDCtWrMC6desQ\nEhLi6+XJRkkXNhHNciMjwN69ru3RysoAq9XXq7rY448D//7vvl4FXQWH4EBDZ4Or26a5Ese7j8ta\nM0of5ZxrkxuXi9QFqQgJ8J/vKwRBwJmx4MZtu7RuLwY3iSEhHqFNmtGIOQEB6Bq+9Eyi4p4e3N7Q\ncNnzvpuUhA1hYZf8ekRgoN8EbERERERENDso6V6uokMaq9WKRYsWobOzE4IgQKPR4Oc//zn++Z//\nGRqNBsDlQ5ojR47gn/7pn1BeXu58bunSpWhoaOAPiiQ5JV3YREQehoeBPXtc26OVlwM2m69XBXz1\nq8COHb5eBUmk09qJXS27nFuk1bTWYGBkQLZ6QZogpC1Ic4Y2ObE5WGBcIFs9OQiCgMaBAY/QZo/Z\njPNeDG4WBAXh7CW6ZKSyPz0dKQaDrDWIiIiIiIimQkn3chUd0gDARx99hFtuuQUOhwOCIEClUiE+\nPh533HEH0tLScNtttwEQO2See+45LFy4ECdOnMCXX36JL7/8EoIgYOy3qNVqUV5ejrVr1/ryt0Qz\nlJIubCKiyxoaAnbvdnXaVFQAA/LdTL+k8HDg/HmAH5yYkYbsQzjQfsAZ2lQ2V6Klr0XWmolhiWJo\nM7pN2qqoVQhQK3YE44QEQUDT4OBFM24u1+2idE8lJGDrwoW+XgYREREREZGTku7lKj6kAYDXXnsN\nP/zhDz0Cl7FOGPflj++OGQt1BEFAYGAg/uu//gvf+c53vLdwmlWUdGETEU3J4KAY1txwg/dr9/UB\nRuUNiCd5NPc2o6qlyrlF2r72fRhxyNc1og/UIys2yxnaZMdmI1wbLls9uQiCgGb34Ga086bTT4Kb\nlXo96jIyfL0MIiIiIiIiJyXdy/WLkAYAvvjiC3zve99De3u7R0DjHsxMFNgIgoCoqCi89957yMvL\n8+6iaUYqKipCUVHRRc9brVbU1tY6HzOkISK/0tUFzJvn/brf/z7wjW8AGzYAkZHer08+ZRu2obat\n1hnaVLVUocvWJWvNFRErPGbbLJu7zC+3wRUEAS1jwY3bdmkdCgxuNADacnMRGRTk66UQEREREREB\nYEhz1Xp7e/Gf//mfeOmll9DW1nbF14eHh+ORRx7B5s2bYTKZvLBCmg22bNmCrVu3XvF1DGmIyK/0\n9QGhob5dw8qVQGGheBQUAHPm+HY95HWCIOBE9wlnaFPZUon6jnoIkO/b1TnaOciJzXGGNhnRGdAH\n6WWrJydBENA6OHjRjJtzPgxuFoaE4O0VK5Dt679fiIiIiIiI3DCkmSaHw4EDBw6grKwMhw8fxvnz\n59HT0wOdToeIiAgsXLgQhYWFyMzMRECAf+1DTsrHThoimpEEAZg7F7hwwdcrEalUwJo1wMaNYmiz\nfj3AD1zMSj0DPahuqXaGNtUt1TAPmWWrp1FpkDI/xblFWm5cLuJD4/2y2wYQg5u2oSHUus+48WJw\no1OpkGEyIdNkQqbRiEyTCXHBwX7750lERERERDMDQxqiGUhJFzYR0VX56leBL77w9SomptEA6eli\nYLNxI7BuHaDT+XpV5AN2hx31nfWubpvmSpy8cFLWmtHGaDGwGQ1u1i5YiyCN/27dNRbcuIc2eywW\ntA8NeaV+VGCgR2iTYTQiPDDQK7WJiIiIiIgAZd3LZUhDJBElXdhERFfl8ceBZ5/19SomJzAQyM52\nhTbZ2UBwsK9XRT5yznIOVS1VztCmtq0Wg/ZB2eoFa4KREZPh3CYtJzYHUYYo2ep5S9vgIP52/jy2\nNjaiZVC+P7+JLNFqnaFNptGINQYDQjQar66BiIiIiIhmDyXdy2VIQyQRJV3YRERXpa4OWL3a16u4\nOiEhQG6ua3u0jAwxyKFZaXBkEPvb9zu3SKtoqsBZy1lZay4KX+TcHi03LhfJ85KhUftnyPBcYyN+\ncvq0T9cQqFJhtV6PTJMJWaPBzTKdDmpuk0ZERERERBJQ0r1chjREElHShU1EdNXy84GyMvnrLF8O\n5OUBX34JnDol/fn1enGOzVinzdq14pZpNCsJgoCm3ibXFmktlTjQfgB2wS5bTWOQEdmx2c5um+zY\nbISGhMpWT0rfqqvDX8+f9/UyLmLUaJDh1m2TaTIhhh10RERERER0FZR0L5chDZFElHRhExFdtY8/\nBr75Te/U+frXxV83NQHFxWJg8+WXQEuL9PVCQ4GCAjG0KSwEVq0C1Grp65DfsA5Zsbttt8dsmwsD\nF2Srp4IKyZHJzrk2uXG5WDxnMVQ+6gxxCA6ct10cxAiCgKTdNTg/PHJV5w1Rq7FUq0WDzYaRiX7M\nCDQBKumuveigII/QJt1oRGhAgGTnJyIiIiKimUlJ93IZ0hBJREkXNhHRtNx5J/DOO/Kef9u2ib8m\nCMDJk67QprgYOHdO+jXMnQts2ODaHm35coDbKM1qDsGBY+ePeYQ2h7sOy1ozQhfhnGmTG5eL9Oh0\n6AJ1stYc02ntROQLkV6p5SHnAyAoTNYSy3U6j/k2qw0GBDOUJSIiIiIiN0q6l8uQhkgiSrqwiYim\n5fx5cTZNW5v0546OBg4eFEOSyRAE4MgRV2BTXAx0d0u/rvnzXVujFRYC11zD0IbQ3d+NXS27UNVc\nhcqWSlS3VMM6bJWtXoA6AGvnr/WYbRNripWllq9CmnfvOYpjI8Go6etDdV8fzg0Py14zSKXCGoPB\no+NmiVbL+TZERERERLOYku7lMqQhkoiSLmwiommrqxO3B7sg4fZP4eFASYm41djVcjjEtY112pSU\nAH190q1xTFycK7ApLATi46WvQX5nxDGCunN1zrk2lc2VONNzRtaacaY4Z2CTE5uDNfPXIFATOO3z\n+iqk6Xi0A/P08wCIW6u1DA6ixmxGTV8fasxm1JrNsNjlmxU0JlSjQYZbaJNpNGIB59sQEREREc0a\nSrqXy5CGSCJKurCJiCRRVwdcf700HTXR0cCnn04voJnIyAiwb5+ry6asDLDK0OmwaJFnp838+dLX\nIL/UZm5DVXMVqlqqUNlciT1n92DIPiRbPW2AFhkxGc7ZNjlxOYjQRUz5PEoIaSZiFwQcsdmcoU1N\nXx8OWq0Tz7eRWGxwMLLcQps0oxFGzrchIiIiIpqRlHQvlyEN0RQVFRWhqKjoouetVitqa2udjxnS\nENGMcP488PDDwNtvX/057rwTePHFyW9xNh3Dw8Du3a7t0SoqgMFB6eusWOHqstmwAYiY+k1ympkG\nRgaw9+xej9k256wyzFVys3TuUrHbJlYMbZLmJUGtuvwMFqWGNBPpt9uxz2LxCG5ODgzItEIXFYAk\nnc5jm7RVej0COd+GiIiIiMjvMaQh8mNbtmzB1q1br/g6hjRENKN8/DHwi18ApaWTf09+PvDYY8DX\nvy7fuq5kYADYtcsV2lRXi0GO1FavdnXZ5OcDYfIORif/IQgCTvecRmVzpXO2zcFzB+EQHLLVDA0O\nRXZstnObtKyYLBiDjR6v8aeQZiLnh4ex2y20qTGb0emF+TYhajXWGgwe26Qt0mqh4nwbIiIiIiK/\nwpCGyI+xk4aIZrVDh4B33gFqaoA9ezxn1oSHA2lpQGYmcMcdgNs3O4phtYrdNWMzbWprxTk3UlKr\ngdRUV2iTlwcYDNLWIL9mHjSjprXGOdumqrkKvYO9stVTq9RYFbnKGdrkxuXCEGhA1K+iZKt5KVKF\nNOMJgoDGgQGP0GaP2Qyb1Nf3BMIDAjxCm0yTCZFBQbLXJSIiIiKiq8eQhmgGUtKFTUTkFYIAWCzi\ndmLBwWIQ4W+fJu/rE+fYjHXa7N8v/r6kFBAgBldjM21ycgCtVtoa5NccggOHOw97hDZHzx+VtWaE\nLgJdti5Za0xErpBmIiMOBxrGzbeps1ohf2wDJAQHe4Q2qQYDDJxvQ0RERESkGEq6l8uQhkgiSrqw\niYjoKnV3AyUlrk6b+nrpawQFiUHNWKdNVpb4HJGbLlsXdrXscs61qWmtQf9Iv6+XNW3eDGkmYrXb\nsc9sRo3ZjOrR8OaMF+bbqAGs1Os9gptknQ4BnG9DREREROQTSrqXy5CGSCJKurCJiEgi584BO3eK\noU1xMXDsmPQ1dDpg3TpXaJOWJnbfELkZtg/jwLkDztCmqqUKTb1Nvl7WlPk6pJlIx9AQdrttk1bT\n14fukRHZ62rVaqQZjR5bpSWGhHC+DRERERGRFyjpXi5DGiKJKOnCJiIimbS2urpsvvwSaGyUvobR\nCOTnu7ZHS0kR59wQjdPS14Kq5irnNml7z+7FiEP+cGE6lBjSjCcIAk4NDHiENnstFgx4Yb5NRGCg\nR2iTYTQigp12RERERESSU9K9XIY0RBJR0oVNRERecvq0K7QpLgba2qSvER4OFBS4Om2Sk/1v9g95\nRf9wP2rbap2hTWVzpU/mzlyOP4Q0Exl2OHDIanWGNjVmM+qtVnjjB6lrQkI8tklbazBAp9F4oTIR\nERER0cylpHu5DGmIJKKkC5uIiHxAEIDjx12BTXEx0NkpfZ3ISGDDBlenzZIlDG1oQoIg4OSFk84t\n0iqbK3Go4xAEr0QLE/PXkGYi5pER7LVYPDpumgYHZa+rAbDKYPDouEnS66Hh3wNERERERJOmpHu5\nDGmIJKKkC5uIiBRAEID6elenTUkJcOGC9HWio11dNhs3AomJ0tegGaN3oBfVrdXYcXIHXqh6wev1\nZ1JIM5H2wUFxvo1bx02PF+bb6Mfm24yGNlkmE+KCgznfhoiIiIjoEpR0L3fGhDQDAwP4/PPPcezY\nMWg0GiQnJ6OwsBCaSWwF0NbWhp/+9KdQqVT4/e9/74XV0kykpAubiIgUyG4HDhxwddmUlgJms/R1\nEhNdgU1hIRATI30N8nud1k5EvhDp9boLwxZi48KNyE/IR0FCARLCEry+Bm9yCAJO9Pd7dNvss1gw\n5IUfwaICAz22ScswGhEeGCh7XSIiIiIif6Cke7kzIqR577338OCDD6Kry3PP7ZiYGDz33HO48847\nL/v++vp6rFq1CiqVCna7Xc6l0gympAubiIj8wMgIsGePa3u08nKgv1/6OkuXukKbDRvE7dJo1vNV\nSDNefGg8ChIKUJBQgPyEfCyes3jGd38MORw4aLF4dNscsdm8sgndEq3WY5u0NQYDQjjfhoiIiIhm\nISXdy/X7kGbbtm246667IAgCJvqtqFQq3HHHHXj99deh1WonPAdDGpKCki5sIiLyQ4ODQE2NK7Sp\nqgKGhqSvk5zs6rIpKADmzJG+BimeUkKa8RYYFji7bAoSC7AiYsWMD20AoHdkBHvcQpuavj60ynH9\njxOgUiFFr/fouFmu00E9C/7MiYiIiGh2U9K9XL8OaTo6OrB06VL09fUBAG6++WZ85StfwdDQEIqL\ni/HJJ5/AbrdDpVIhOzsbn3zyCUwm00XnYUhDUlDShU1ERDNAfz9QWemaabN7t9h9IyWVClizxtVp\ns349MMH3SjTzKDWkGS9CF+EMbfIT8rE6ajXUKrWvl+UVrYOD2O0W2uw2m9HnhZ9VjBoN0o1Gj46b\n2JAQ2esSEREREXmTku7l+nVI8+yzz+KJJ56AWq3Gtm3bcPvtt3t8vba2Fps2bUJDQwNUKhVSU1Px\n2WefITw83ON1DGlICkq6sImIaAayWMQt0cY6bfbuBRwOaWtoNEB6uhjaFBYC69YBer20NUgR/CWk\nGS8sJAx58XnOLdLWLliLAHWAr5flFQ5BwDGbDTVmM6pHw5sDFguGvfDjXHRQkEe3TbrRiNCA2fHn\nTkREREQzk5Lu5fp1SFNYWIjS0lL8/d//Pd56660JX2O1WnHnnXfiww8/hEqlQkpKCj7//HPMcdva\ngyENTUVRURGKioouet5qtaK2ttb5mCENERHJqqcHKC11ddocPCh9jcBAICvLtT1adjbAT9TPCP4a\n0oxnCDJgXdw6Z7dNRkwGgjRBvl6W1wzY7ThgtXpsk3ZMjtlWE1iu03l026w2GBCsnh1dTkRERETk\n/xjSSCQqKgpdXV3Yvn07vvnNb17ydYIg4J577sGbb74JlUqF1atX4/PPP8fcuXMBMKShqdmyZQu2\nbt16xdcxpCEiIq/q6gJ27nSFNkeOSF8jJATIzXWFNhkZYpBDfsdXIc0/rPkH1LTV4FDHIVnOHxIQ\ngpzYHOf2aNmx2dAGTjyXcqa6MDyMWrPZGdpU9/Xh3PCw7HWDVCqsMRg8Om6WaLWcb0NEREREisSQ\nRiLBwcEYGRnB3r17kZKScsXX33///Xj11VehUqmwcuVKfPHFF4iIiGBIQ1PCThoiIvILZ8+Koc3Y\n9mgnT0pfQ68X59iMzbRZu1bcMo0Uz1chTcejHZinn4cuWxfKm8pRcqYEpU2l2N++Hw5B4u37AARp\ngpAZk4n8+HwUJBYgNy4XhiCD5HWUTBAEtAwOOkObGrMZtWYzLF74uSdUo0GGW2iTaTRiQXCw7HWJ\niIiIiK6EIY1ETCYTrFYrSkpKkJeXN6n3PPjgg3j55ZedQc2XX36J9vZ2hjQ0bUq6sImIiC7S1OTq\nsikuBpqbpa8RGgrk57s6bVatArj9kSL5OqQZr3egF+VN5ShtLEVJYwlq22phF6T/vlyj0iAtOs3Z\naZMXn4ewkDDJ6yidXRBwxGbz2CbtoNWKES/8aBgbHOwR2qQZjTBxvg0REREReZmS7uX6dUiTlJSE\no0eP4rXXXsPdd9896fc99NBD+N3vfgeVSoXk5GS8+OKL2LhxI0MamhYlXdhERESXJQjAqVOuwObL\nL4Fz56SvM3cusGGDK7RZvhzg1keKoLSQZjzLkAVVzVUoaSxBaWMpqlurMWQfknw9KqiwZv4a50yb\n9QnrEaGLkLyOP+i327HPYvEIbk4ODMheVwVghU7nsU3aKr0eQQx4iYiIiEhGSrqX69chzd/93d/h\n3XffxR133IFt27ZN6b0PP/wwXnrpJahUKsybNw8dHR0MaWhalHRhExERTYkgiDNsxgKbnTuB8+el\nrzN/vhjWjG2Pds01DG18ROkhzXj9w/2oaa1BSWMJShpLUNVchf6RfhlWCCTPS3Z22uQn5GOBcYEs\ndfzB+eFh7HYLbWrMZnR6Yb5NsEqFVKPRo+NmkVYLFf++ICIiIiKJKOlerl+HNC+//DIefPBBGAwG\ntLe3Q6fTTen9mzdvxn/8x39ApVJBEASGNDQtSrqwiYiIpsXhAOrqxNCmuBgoKQF6e6WvExfn6rIp\nLATi46WvQRPyt5BmvCH7EGrbap0zbcqbymEZskiwwostmbMEBQkFKEgUg5v40Nn7/1NBENA4MOAR\n2uwxm2FzSD9PaLzwgACP0CbDZEJUUJDsdYmIiIhoZlLSvVy/DmlOnTqFxYsXQ6VS4be//S0efPDB\nKZ/jRz/6EX77298CAEMamhYlXdhERESSstuBfftc26OVlQFWq/R1Fi1yddkUFoqdNyQLfw9pxhtx\njGB/+36UnBE7bcqaytAz0CN5HQBIDEt0dtoUJBTgmvBrZnWHx4jDgYZx823qrFbIH9sACcHBHtuk\npRoMMHC+DRERERFNgpLu5fp1SAMAmzZtQmtrK2JiYlBUVHRV53jsscfw7rvvAgBOnz4t4epoNlHS\nhU1ERCSr4WFg927X9miVlYAcsyuWL3cFNhs2ABGzc1aIHGZaSDOeQ3Cg7lwdShtLnXNtOm2dstSK\nMcY4A5v8hHwsj1g+q0MbALDa7dhnNnt03Jz2wnwbNYBkvd6j42alXo8AzrchIiIionGUdC/X70Ma\nIqVQ0oVNRETkVQMDwK5dru3Rdu0SgxyprV7tCm3y84GwMOlrzBIzPaQZTxAEHOk64gxsShpL0GZu\nk6XWPN08Z2hTkFiAlZEroVYxJOgcGvIIbWr6+tA9MiJ7Xa1ajVSDwRnaZJlMSAwJmfVBGhEREdFs\np6R7uQxpiCSipAubiIjIp6xWsbtmbHu02lpxyzQpqdVAaqpre7S8PMBgkLbGDOYQHDhvO+/1unN1\ncxURWAiCgJMXTjoDm5IzJWjsbZSlVnhIONYnrHd22qyZvwYBam7JJQgCTg0MeIQ2ey0WDHhhvk1E\nYKDnfBujERGcb0NEREQ0qyjpXu6sCWm++OILXHfddQDE2TMjXvjUFs0uSrqwiYiIFKWvT5xjM7Y9\n2v79gNTfggYEABkZrk6b3FxAq5W2Bs1ojT2NKG0sdQY3x7uPy1LHGGTEuvh1ztAmPTodQRoGBAAw\n7HDgkNXq0XFTb7XCGz+wXhMS4jHfZq3BAJ1G44XKREREROQLSrqXO6tCmmuvvRaAGNLYpf40J816\nSrqwiYiIFK27GygpcYU29fXS1wgKAnJyXKFNVpb4HNEknTWf9ZhpU98pw/9PAWgDtMiNy3VukZYV\nm4WQgBBZavkj88gI9losHh03TYODstfVAFhlMHh03CTp9dBwmzQiIiKiGUFJ93IZ0hBJREkXNhER\nkV/p6AB27nRtj3bsmPQ1tFpxS7Sx7dHS0sTuG6JJ6rR2oqypzBncHGg/AEGGHo8gTRCyYrKcnTa5\ncbnQB+klr+PP2gcHsdts9ui46fHCTgl6tRppbqFNpsmE+OBgzrchIiIi8kNKupfLkIZIIkq6sImI\niPxaa6sY1ox12pw5I30NoxHIzxdDm8JCICUF4NZGNAU9Az0obypHyZkSlDaVYk/bHtgF6X/GCFAH\nID06Hfnx+ShILMC6uHUIDQmVvI4/cwgCTvT3e3Tb7LNYMOSFH3UjAwM9QpsMoxFzAgNlr0tERERE\n06Oke7kMaYgkoqQLm4iIaEY5fdoztGlrk75GeDhQUODaHi05GeCn42kKzINmVLVUoeRMCUoaS1DT\nWoNhx7DkddQqNdbMX+PstFkfvx5zdXMlr+PvhhwOHLRYPLptjthsXplvFTAr1gAAIABJREFUs1ir\nRZZbx80agwEhDIGJiIiIFEVJ93IZ0hBJREkXNhER0YwlCMDx467AprgY6OyUvs68ea4um40bgSVL\nGNrQlPQP92NXyy7n9mhVLVUYGBmQpdbKyJUoSChwBjdRhihZ6vi73pER7HELbWr6+tA6NCR73QCV\nCil6vUfHzTKdTvHzbbqHh3F7Q4PHc/+dlMROISIiIpoRlHQvlyEN0RQVFRWhqKjoouetVitqa2ud\njxnSEBEReYEgAA0NrsBm507gwgXp60RHuwKbwkJg4ULpa9CMNjgyiNq2WpQ0ip02FU0VsA5bZam1\nbO4y5Cfki8FNYgFiTbGy1JkJWgcHsdsttNltNqPPCz8rGjUapBuNztAm02hEjMLm27zc2ooHjh/3\nfG7JEtwfE+OjFRERERFJhyGNDzCkIals2bIFW7duveLrGNIQERH5gN0OHDzoCm1KSwGzWfo6iYme\noQ1vWtIUjThGsPfsXmenTVljGXoHe2WptTBsIQoSXZ02C8MWKioMUBKHIOCYzeYMbarNZhywWDDs\nhR+bFwQFeYQ26UYjwnzYtZK5Zw92j/v7M9NoRHVamo9WRERERCQdhjQ+wJCGpMJOGiIiIj8yMgLs\n2ePaHq28HOjvl77O0qWu7dEKC4HISOlr0Ixmd9hR11GHkjMlKG0qRWljKbpsXbLUijXFujptEgqw\ndO5ShjaXMWC344DV6rFN2jE5/h6ZwDKt1mObtBSDAcFqtex1661WrNy9e+KvZWQgSa+XfQ1ERERE\ncmJI4wMMaUhuSrqwiYiI6BIGB4GaGjG0KS4GKisBOWZSJCe7umwKCoA5c6SvQTOaQ3DgcOdhZ6dN\nSWMJ2i3tstSK0kchPyHfGdwkRyZDrZI/CPBnF4aHUWs2uzpu+vpwbnhY9rqBKhXWGAzINBqRZTIh\n02TCEq0WaolDtn89eRK/bG6e+GtxcXh+0SJJ6xERERF5m5Lu5TKkIZKIki5sIiIimqT+fqCqyrU9\nWk2N2H0jJZUKWLPGtT3a+vWAySRtDZrxBEHAie4TKGkscQY3Tb1NstSao52D9fHrndujrZm/Bhq1\nRpZaM4UgCGgZHHSGNjVmM2rNZli88HNnqEaDDLdum0yjEQuCg6/6fCMOB+J27UL7JQLsBUFBaMrO\nRoAXOnqIiIiI5KKke7kMaYgkoqQLm4iIiK6SxSJuiTa2PdrevYDDIW0NjQZIS3N12qxbB/jD1kGC\nIM73GRoCgoIAo1EMoMhnzvScEQOb0S3STnSfkKWOKdiEvPg85MfnoyCxAGkL0hCo8d2sFH9hFwQc\nsdk8tkk7aLVixAs/gscGB3uENmlGI0wBAQDEQKnrMl0/xT09uL2h4bLnfzcpCRvCwi759YjAQG6h\nR0RERIqmpHu5DGmIJKKkC5uIiIgk0tMDlJa6tkc7cED6GoGBQFaWK7TJzgZCQqSvczXq6oB33hE7\njPbuBS5ccH0tPBxITQUyM/9/9u48Kq7rTBf+UwNQQA2UEBrQgBBoQLZkSUgIgQxC1gBO2rGDV/tG\nt9M3fXvdL7k36Y5z406cpDux0nE7iZ3EcXe6O6vXyvVabcdf8llO4mVfSkZCKiQ0IDTPYhKgCSEo\nqqgCihrO98eBUyBrKIl9qFPw/NbSHwFqvzsr2oq9H+33BbZvB0b9cxDFxjXPNdS21SovbS7cvqBK\nnZSEFBTNK1Je2hTMKYDJqJHfsxo3EArhpNc75sVN0wTMt9EByEtJQYHVitmJiXitXZ1XWCNOrlmD\nJ8xmVWsQERERjYeW7nIZ0hAJoqWDTURERCq5fRtwOiPt0S6ocAluMgFFRZH2aGvXykHORPr4Y+An\nPwH274/+M08+Cbz8MvD00+rtix7KLd8t7G/br7RIO915GhLE/+tfkiEJhXMLlZk2hXMLkZoYB6/D\nNKI7EMDRUa9t6vv60DUB823U9P2sLOzIzo71NoiIiIjuSUt3uTENaWprayesVkNDA1566SUADGlI\nHVo62ERERDRBbtwA9u2LtEdrbhZfIzVVDkDKyuRfq1fLLdPU0N0N/M3fyK9nHtX27cBbbwHp6eL2\nRUK4Blw40H4AzjYnnG1OHL9xHGFJcDs/AEa9EWsz1yovbYrnF8OaxDlM0ZIkCW2Dg2NCm2N9fegX\n3XpRRY+npuLM2rWx3gYRERHRPWnpLjemIY1er5/wPrWSJDGkIVVo6WATERFRjLS3R1qj1dQAHR3i\na9hsQElJpD3a8uWAiAHep08DFRXA9evjXyszE3A45L2RZnn8HhzsOKjMtDl67SgCYfEvOPQ6PVbP\nXq3MtNkwfwOmJU8TXmcyC4bDOH/HfJszPh+0GtsYAFwvKsKMxMRYb4WIiIjorrR0l6uJkGaitjBS\niyENqUFLB5uIiIg0QJKAlpZIYLN3L3Dzpvg66enAxo2R9mhLlwIP+xehTp+W1xg9c2a87Ha5NRyD\nmrjRH+jH4auHldDmUMch+EN+4XV00GH5zOVKaFOSVYIZqTOE15nsfKEQTvT1jXlx0zo4GOttIdtk\nwm/z8lBos8V6K0RERET3pKW7XE2ENBOJIQ2pRUsHm4iIiDRIkoBLlyKBzd69cnsx0WbNirRGKysD\ncnLuH9p0dwMrVoh5QXOnzEw5AGLrs7jkD/pRf60etW21cLY5cbDjIHwBnyq1lk5fqrRHK80qxRzr\nHFXqTHZdQ0M4Oiq0qfd40B0MTlj9IqsV/7JoEVaazRP+7/pERERED0NLd7kxDWkWLFgQs39wa21t\njUldmry0dLCJiIgoDoTDwNmzkdDG6QTcbvF15s2LvLIpKwPmzx/7/e3bxzeD5kG2bwfefVe99WnC\nBEIBHL9xHM42J2rbarG/fT88fo8qtXLsOUpgU7qgFFm2LF76PwJJktAyODgmtDnu9WJQ5fk2MxIS\nUGSzochqRbHNhnyLBUki2jISERERCaKlu9yYhjREk4mWDjYRERHFoVAIOHEi0h5t/37Ap8KrhYUL\nI4FNKAT85V+Kr3Gnjz4CPvMZ9evQhAqFQzjVeUp5aVPbVouegR5Vas2zzpNbow23SFs0bRFDm0cU\nCIdx1udTQpsDbjcuDwyoWjNRp8MaiwXFNhuKh8ObDM6rISIiohjS0l0uQxoiQbR0sImIiGgSCASA\no0cjrdHq6gANzJt4JCUl8kshmtTCUhjnu84rM22cV5zo9HWqUmuWeZby0qYkqwTLMpZBr+NLjUe1\no7UVr7S1TWjNRcnJSmBTbLNhaUoK9AzeiIiIaIJo6S6XIQ2RIFo62ERERDQJDQ4CR45E2qMdPiwH\nOfHizBlg1D8r0eQnSRIaexrhvOKEs03+ddVzVZVa6cnpKMkqUYKbFTNXwKA3qFJrMvrcmTP4UI0Z\nWQ/BbjSiyGpF0fBrm7UWC1IM/N+QiIiI1KGlu1yGNESCaOlgExER0RTQ3y+/rhlpj9bQILcv06rv\nfhd49dVY74JiSJIkXOm9orRGc7Y50eJqUaWWLcmGDfM3KC9tVs9ejQRDgiq14p0kSZh58CC6NBb6\nGnU6rDKb5RZpw+FNZlJSrLdFREREk4SW7nIZ0hAJoqWDTURERFOQxyPPsRlpj3biBKClf9TfvBmo\nro71LkhjrnquyoHNcIu0i7cvqlInNSEVxfOLlZk2azPXIsnIC38AaB4YQO6RI+Na47/OmIGzPh9O\n+3xQ80+dBSYTiofboxXZbHg8NRUGtkgjIiKiR6Clu1yGNESCaOlgExEREaGnB6itjbRHO3s2tvux\n24HuboAXqnQfnd5O7G/fr7RIO3PrjCp1TEYTCucWKi9tCucWIiUhRZVaWvfOzZv44sXxhWPv5OXh\nv86cCU8wiMMeDw663ajzeHDY44FXxRd+VoMBhSMt0qxWrLNaYTEaVatHREREk4eW7nIZ0hAJoqWD\nTURERPQpt24B+/bJgU11NdDcPPF78HgAi2Xi61Lc6hnowf62/Up7tBM3TyAshYXXSdAnYO2ctSjN\nKkVpVimK5hXBkjQ1fq9+9fJl/Ov16/f8/kKTCRKA1sHBe6+RmYl/Wbz4U18PhsM46/OhzuNBnduN\ng2432vx+Edu+Kz2AFWaz8tqm2GbDvKQk6BgOExER0R20dJfLkIZIEC0dbCIiIqL7un0byMiY+Lqd\nncCMGRNflyYN96AbBzsOwtkmv7RpuN6AYDgovI5BZ8Dq2auVlzYb5m+APdkuvI4WrG5owAmv967f\n+8uZM/HPixYBAL7W2Ij/7Oy8+xpmM46tWRNVvauDgzg4Etp4PDjR1wc1p2nNSUxUApsiqxVPmM1I\n0OtVrEhERETxQEt3uQxpiB7S22+/jbfffvtTX/f5fGhoaFD+M0MaIiIi0iyPB7DZJr6u3Q5s3Ahs\n2iT/ystj+zMaF9+QD4euHlJe2hy5egT+kPiXGjrosGLmCvmlzYJSPDn/SWSkxiDoFEySJOTV1+PS\nwMCYr9sMBvz74sX4LzNnjvn6e52d+Mrly/Dc0cJsSXIyLhQUPNKLFV8ohHqPRwluDnk86A2KD95G\npOj1WGe1omj4tc16qxVpCQmq1SMiIiJtYkhDFMdeeeUV7Nix44E/x5CGiIiINEuSgPR0wOWK7T5m\nzgTKyuRfmzYBOTkMbWhcBoODqL9WD+cVJ2rba3Gw4yD6A/2q1FqWsQwl80tQukB+bZNpyVSljtp8\noRC+19KCt65dgwRgg82Gd/LykGUy3fXnrwwM4C8uXECdxwMdgK/PnYsfZWcj1WAQsp+wJOFCfz/q\n3G7lV/N9Wq2Nlw7AY6mpSmhTbLNhocnEFmlERESTHEMaojjGlzREREQ0KWzeDOzZE+tdjDV3rhzW\njIQ28+fHekcU54ZCQzh2/Zjy0uZA+wH0DfWpUit3Wq7SHq00qxRZaVmq1FHLgd5eHPJ48I25c2F8\nQDuwYDiMn1+9iiKrFRvS0lTfW+fQEA4OBzYHPR409PUhoOJVxsyEBBSNapG22mJBElukERERTSoM\naYgmIS0dbCIiIqIH+u53gddei/Uu7m/hwkhoU1YGzJ4d6x1RnAuGgzh18xScbU7UttWitq0WrkF1\nXpRl2bKUwKZ0QSly7Dl8nSHIYCiEhr4+pUVanduNbhVbpCXpdFhrtaLYakXRcHAzPTFRtXpERESk\nPi3d5TKkIRJESwebiIiI6IHOnAFWrIj1Lh7O0qWR0GbjRmD69FjviOJcWArj7K2zykub2rZa3PLd\nUqXWbPNsuTXacIu0vOl5ExrahKUwuvu7J6zeiPSUdOh16r5CkSQJlwcGlNc2dR4PLvar0+ZuxJLk\nZOW1TbHViiUpKQzhiIiI4oiW7nIZ0hAJoqWDTURERBSVkhJg/3716+h08hwc0VasiIQ2JSXABLRd\noslNkiRc6r6khDbOK05c67umSq3pKdOVlzYlWSVYMXOFqmFGl68LM96Yodr693LrpVvISM2Y8Lrd\ngQAODQc2B91u1Pf1YTAcVq3eNKNRDm2GZ9ussViQLGhODxEREYmnpbtchjREgmjpYBMRERFF5eOP\ngc9+Vv06O3cCNhtQUwPs3QvU1wOhkNgaej2Qnx+ZZ7NhA5CaKrYGTTmSJKG1txXOK07UttfCecWJ\n1t5WVWqlmdKwYf4GuT1aVilWzV4Fo94obP2pFtLcaSgcxgmvV55rMxze3BwaUq1egk6H1WazPNdm\nOLyZlZSkWj0iIiJ6OFq6y2VIQySIlg42ERERUdS2bwfee0/d9d99d+zX+vqAAwfk0KamBjhxQvxL\nG6MRWLcuEtqsXw+YTGJr0JTU4e4Y0x7tUvclVeqYE80onlesvLRZO2ctEg2PPgdlqoc0d5IkCa2D\ng0pgU+d246zPBzUvSBaaTGNe2zyWmgo9W6QRERHFhJbuchnSEAmipYNNREREFLXubrlt2PXr4tfO\nzAROnwbS0+//cy4X4HTKr2xqaoCzZ8XvJSkJKCqKhDZr1wIc/E0C3PTeRG1brRLcnL2lwu9fAMnG\nZBTOLZRf2iwoxbo565CckBz15xnSPJg7GMTh4cDmoNuNwx4PfCq2SLMZDCgcDmyKbTYUWCwwG8W9\nniIiIqJ709JdLkMaIkG0dLCJiIiIHsqZM0BpqRyWiGK3y8HL8uUP/9lbt4B9+yKhzeXL4vY1IiUF\nePLJSGizapX8+oZonG7338aB9gNKi7STN08iLIm/6E80JKJgTgFK5pegdEEpiuYVwZxovufPM6R5\neMFwGKd9Pjm0GQ5vOvx+1eoZADwx0iJtOLyZxxeAREREqtDSXS5DGiJBtHSwiYiIiB7amTNAebmY\nFzWZmYDD8WgBzd1cuyYHNiOhzZUrYtYdzWqVg6qR0Gb5cnnODdE4uQfdONB+QHlp03C9ASFJ8Ewm\nAAadAfmZ+cpMm+L5xUgzpSnfZ0gjRsfgoBLY1LndOOX1Qvz/mhHzkpLGhDYrUlNh5J9NRERE46al\nu1yGNESCaOlgExERET2S7m7gb/8W+O1vH32N7duBt956cIuz8WhtHRvaqNGqLT0d2LhRDmzKyoCl\nSwHOjiABvENeHOo4pMy0OXLtCIZC4gfY66DDylkrUZJVgtKsUuRl5CHvV3nC6zzIZAtp7uQNBlHf\n16eENoc8HnhC6sU2qXo91o1qkVZotcLGV4BEREQPTUt3uQxpiATR0sEmIiIiGpePPwZ++lOgtjb6\nz5SUAN/+NvD00+rt624kCWhslMOakeCmq0t8nVmzIq9sysqAhQsZ2pAQA4EB1F+rh7PNCWebE4c6\nDmEgOBDrbQkz2UOaO4UkCefvaJHWMjioWj0dgMdTU8e8tsk2maDjn09ERET3paW7XIY0RIJo6WAT\nERERCXH2LPDee0B9PXDs2NiZNXY7kJ8PFBQAX/gCMOqfg2IqHAbOnYu8snE6gd5e8XXmzx8b2syb\nJ74GTUlDoSE0XG9QZtocaD8A75A31tt6ZFMtpLmbG34/Do1qkXbc60VAxauYWYmJKLZaUTT82maV\n2YxEtkgjIiIaQ0t3uQxpiATR0sEmIiIiEk6SAK8X8PuBpCTAbI6PlyShEHDyZCS02b9f/u8hWm7u\n2NBm5kzxNWhKCoaDOHnzJJxX5Jc2+9v3o3dQheBRJQxpPm0gFELDSIs0jwcH3W70BIOq1TPp9Vhr\nscgt0qxWrLfZkJ6QoFo9IiKieKClu1yGNESCaOlgExEREdE9BAJAQ0MktKmrA9RoRbRsWSS0KS1V\nd0YPTSlhKYwznWdQ21arzLXp6lehxZ8gDGkeTJIkXOrvVwKbOrcblwbUbXm3NCUFxcPt0YpsNixO\nTmaLNCIimlK0dJfLkIZIEC0dbCIiIiKKkt8PHD4cCW0OH5aDHJF0OuCJJyKhTUkJYLWKrUFTliRJ\nuHj7ohLYONucuN53PdbbUjCkeTS3h4ZwcCS08Xhw1OOBX8Xrm+kJCSgaaZFmtWKNxQKTwaBaPSIi\noljT0l0uQxoiQbR0sImIiIjoEfX3y69rRkKbhga5ZZpIBoM8z2ekNVpxMZCaKrYGTVmSJKHZ1awE\nNs4rTrS522K2H4Y0YvjDYZzo60PdqNk2t0QHyqMk6HTIt1jGvLaZmZioWj0iIqKJpqW7XIY0RIJo\n6WATERERkSAejzzHpqZGDm5OnpTn84iUkACsWxcJbQoLAZNJbA2a0tp62/DR5Y/wtaqvTXhthjTq\nkCQJLYODqHO7ldc253w+qHnBk2MyyXNtbDYUWa1YlpoKPVukERFRnNLSXS5DGiJBtHSwiYiIiEgl\nPT2A0xkJbc6dE1/DZAKKiiKhzdq1cpBDNA5dvi7MeGPGhNfd99/2oSSrhPNOJkBvIIBDHg8ODr+2\nOeLxoD8cVq1emtGI9VYrioZf2xRYrUhlizQiIooTWrrLZUhDJIiWDjYRERERTZDOTmDfvkho09go\nvkZqKvDkk5HQZtUquWUa0UOIVUgDAJmWTJTnlKM8txxbcrYgzZQWk31MNYFwGKd9PqU9Wp3bjWtD\nQ6rVMwBYZbEooU2xzYY5SUmq1SMiIhoPLd3lMqQhEkRLB5uIiIiIYuTq1cg8m5oaoL1dfA2bDSgt\njYQ2jz8O6PXi69CkEsuQZjSDzoDCuYUoz5VDm9WzV0Ov4+/fidI+3CKtzu3GQY8Hp7xeqPfWBpif\nlDSmRdoKsxkGvqoiIiIN0NJdLkMaIkG0dLCJiIiISAMkCWhtjYQ2e/cCN26IrzN9OrBxYyS0WbIE\n4CUo3UErIc2dMlIysC13G8pzyrE1Zyvn10ywvmAQR0a1SDvk8aAvFFKtntlgQKHVimKrFUU2Gwqt\nVliNRtXqERER3YuW7nIZ0hAJoqWDTUREREQaJEnApUtjQ5vubvF1Zs+Ww5pNm+Rf2dnia1Dc0WpI\nM5oOOuRn5qM8pxwViypQMKcARj0v8CdSSJJwbnSLNI8HVwYHVaunB7A8NRVFw69tiq1WZJlMnGFE\nRESq09JdLkMaIkG0dLCJiIiIKA6Ew8DZs5HAxukE3G7xdbKyIq9sysqAuXPF1yDNi4eQ5k5ppjRs\nWbhFaY2WacmM9ZampOt+Pw4OBzYH3W4c93oRVPEqKTMxUQ5thmfbrDSbkcCWjkREJJiW7nIZ0hAJ\noqWDTURERERxKBQCTpyIhDb79wM+n/g6ixaNDW1mxNfFPT2aeAxp7rRi5gqU58iBTfH8YiQaEmO9\npSmpPxTC0b4+ObgZnm3jCgZVq5es16PAYpHn2thsWG+1YlpCgmr1iIhoatDSXS5DGiJBtHSwiYiI\niGgSCASAo0cjoU1dHeD3i6/z2GOR0Ka0FJg2TXwNirlYhTT/55n/g0NXD6GqqQodng5h65oTzdiU\nvQkVuRUozy3HgrQFwtamhxOWJFzs71de29S53WgcGFC15rKUlDGvbXKTk9kijYiIHoqW7nIZ0hAJ\noqWDTUREREST0OAgcPhwJLQ5fBgQ/bfXdTpg5cpIaPPkk4DVKrYGxUSsQppbL91CRmoGJEnChdsX\n4GhywNHkgLPNiaHQkLA6S9KXKG3RSrNKkZyQLGxtenhdQ0M4OBzYHHS7cbSvD0MqXj9lJCSgaDiw\nKbbZkG+xIIkt0oiI6D60dJfLkIboIb399tt4++23P/V1n8+HhoYG5T8zpCEiIiIiVfl88uuakdCm\noUGecyOSwQCsWRMJbYqLgZQUsTVoQsQ6pLmTb8gHZ5sTjiYHqpqq0NTTJKymyWjCxgUbldZoi9MX\n85VFjPnDYRwbaZE2HN50BQKq1UvU6bBmpEXacHiTkcj2eEREFMGQhiiOvfLKK9ixY8cDf44hDRER\nERFNKLcbqK2VA5uaGuDUKfE1EhKA9evlwGbTJmDdOiApSXwdEk5rIc2dmnqasKtpFxzNDtS01qA/\n0C9sD9lp2corm7IFZbAkWYStTY9GkiQ0DQwor23q3G6c7xf3v/ndLEpOHhPaLE1JgZ7hHRHRlMWQ\nhiiO8SUNEREREcWF27cBpzMS2ly4IL5GcrL8umYktFmzBjAaxdehcdN6SDOaP+jH/vb9Smu0c13n\nhO0nQZ+ADfM3oDy3HBW5FXh8xuN8ZaMRrkAAh0ZapHk8OOLxYED068BR7EYj1o9qkbbWYkGKwaBa\nPSIi0haGNESTkJYONhERERHRp9y8KQc2I6FNc7P4GmYzUFISCW2eeEJumUYxF08hzZ063B1yYNPs\nwO6W3fD4PYJ2B2RaMpW2aFtytiDNlCZsbRqfQDiMk16vEtrUud24PiRujtGdjDodVpnNcmhjtaLI\nZkMmXwoSEU1aWrrLZUhDJIiWDjYRERER0QO1t48NbTo6xNdISwM2boyENo89BvDVQkzEc0gzWiAU\nwOGrh5XQ5viN48LWNugMKJxbqLRGWz17NfQ6Dp/XCkmS0DY4OKZF2hmfD+q9tQEWmExKYFNss+Hx\n1FQY+GcYEdGkoKW7XIY0RIJo6WATERERET0USQJaWuSwZiS06ewUXycjQw5sRkKbRYsY2kyQyRLS\n3Omm9yY+af4EjiYHPmn+BN0D3cLWzkjJwNacrajIrcDWnK2q/vegR+MJBnFkJLTxeHDY44E3FFKt\nnsVgQOFIizSrFeusVljY4pGIKC5p6S6XIQ2RIFo62ERERERE4yJJwMWLclhTUwPs2wf09Iivk5kp\nhzWbNsnBzYIF4msQACAshdHdLy7AiFZ6SvqEvUYJhUM4duMYqhqr4Gh24MjVI5Ag5spDBx3yM/OV\n1mjr5q6DUc/Lea0JSRLOeL2o83hwcPi1TZvfr1o9PYAVZjOKh4ObIpsN85OSOOeIiCgOaOkulyEN\nkSBaOthEREREREKFw8Dp05FXNrW1gEfcXBBFdnbklU1ZmRziED2i7v5u7G7ZDUezA44mB256bwpb\nO82Uhi0Lt6A8txzbcrZhjnWOsLVJrGt+vxLY1Hk8ONHXB/Xe2gBzEhOVwKbYasUTZjMS9GybR0Sk\nNVq6y2VIQySIlg42EREREZGqgkHgxInIS5sDB4D+fvF1liyJhDYbN8rt0ogeQVgK43TnaXmWTZMD\ndR11CIaDwtZfPmM5KnIrUJ5bjuL5xUg0JApbm8TyhUI46vGgbrhN2iGPB71Bcb8X7pSi16PAalVe\n26y3WpGWkKBaPSIiio6W7nIZ0hAJoqWDTUREREQ0oYaGgPr6yEubQ4cANVoMLV8eCW1KSgC7XXwN\nmhI8fg/2tOyBo8mBqqYqdHg6hK1tTjRjU/YmpTVatj1b2NokXliScKG/H3Vut/zixuNB08CAavV0\nAJalpMhzbWw2FFmtyElOjnmLtJ5AAC+cPz/ma79btgzTGCgR0SSlpbtchjREgmjpYBMRERERxdTA\ngBzUjIQ29fXy6xuRdDpg9epIaLNhA2CxiK1BU4IkSbh4+6IS2DjbnBgKDQlbf0n6EpTnyoFNaVYp\nkhOSha1N6ugcGsJBtxsHh1/bHOvrw5CK12czExJQNBzYFNtsWG2xIGmCW6T967Vr+Gpj49ivLVqE\n/zmHrfyIaHLS0l0uQxoiQbR0sImIiIiINMXrlVuijYQ2x4/Lc24/b/lNAAAgAElEQVREMhiAgoJI\naFNUBCTzMpwenm/IB2ebU2mN1tjT+OAPRclkNKE0q1RpjbY4fXHMX1DQgw2GQjjm9cpzbYbDm9uB\ngGr1knQ6rLValdCmyGrF9ER1W+gVHDuGo319Y79mseBIfr6qdYmIYkVLd7kMaYgE0dLBJiIiIiLS\ntN5eoLY2EtqcPi2+RmIisH69HNiUlQHr1slfI3pIzT3NcmDT7EBNaw36A+LmLy1IW4DynHJULKpA\n2YIyWJL4GiweSJKExoEBJbCpc7txQY25XKMsTk4e0yJtaUqKsIDvnM+Hx48evfv31q7FstRUIXWI\niLRES3e5DGmIBNHSwSYiIiIiiitdXYDTGQltLl4UXyMlBSgujoQ2+fmA0Si+Dk1q/qAfB9oPoKqp\nCo4mB851nRO2doI+ARvmb1Baoy2fsZyvbOJIdyCAQ6NCm/q+PgyKfjE4yjSjEUU2G4qtVhTZbFhr\nsSDZYHiktb7V3IzXO+4+l+lb8+bhJzk549kqEZEmaekulyENkSBaOthERERERHHt+nVg3z45sNm7\nF2hpEV/DYgFKSiKhzRNPABM8A4LiX4e7A7uad8HR5EB1SzU8fo+wtTMtmSjPkQObzQs3w55sF7Y2\nqW8oHMbJUS3S6jwe3BwSN+voTgk6HVabzXJwMxzezEpKeuDnguEw5h0+fM+9zU5MRHthIYz885GI\nJhkt3eUypCESREsHm4iIiIhoUmlri7yyqakBrl0TX2PaNKC0NBLaLFsG8BUDPYRAKIDDVw8rrdGO\n3zgubG29To/CuYXKLJvVs1dDr+OleTyRJAlXBgeVwOag240zPh/UvJTLNplQZLXiidRUrB1ukWa4\n48+1vb29eOH8+fuu8/tly7AxLe2e35+ekMBXX0QUd7R0l8uQhkgQLR1sIiIiIqJJS5KApqZIaLN3\nL3Drlvg6M2cCGzdGQpvcXIY29FA6vZ3KK5tPmj9B90C3sLWnp0zHtpxtKM8tx7acbchIzRC2Nk0c\ndzCIw8OBTZ3bjcMeD3wqtkhTy8k1a/CE2RzrbRARPRQt3eUypCESREsHm4iIiIhoypAk4Pz5SGCz\nbx/gcomvM3duJLApKwOyssTXoEkrFA7h2I1jcDQ5UNVUhfpr9QhLYi7jddAhPzNfaY22bu46GPWc\ntxSPguEwzvh8ymubOrcbHX5/rLf1QN/PysKO7OxYb4OI6KFo6S6XIQ2RIFo62EREREREU1YoBJw+\nHQltamuBvj7xdRYuHBvazJ4tvgZNWt393djdshuOZgccTQ7c9N4UtrYtyYYtOVtQkVuBbTnbMMc6\nR9jaNPE6BgdxcDiwOeh246TXi1CsN3WHx1NTcWbt2lhvg4jooWjpLpchDZEgWjrYREREREQ0LBgE\njh2LhDYHDgADA+LrLF0aCW02bgSmTxdfgyYlSZJwqvOUPMumyYG6jjoEw0Fh6y+fsRzlueWoyK1A\n8fxiJBoSha1NE88bDKK+r09ukebx4JDbDXcotrGNAcD1oiLMSOTvLSKKH1q6y2VIQySIlg42ERER\nERHdg98P1NdHQptDh4ChIfF1VqyIhDYlJcB9hm4Tjebxe1DTWoOqxio4mh1od7cLWzs1IRVPLXxK\naY2WbWeLqngXliSc8/mU1zZ1bjdaBgcnrH62yYTf5uWh0GabsJpERCJo6S6XIQ2RIFo62ERERERE\nFKX+fjmoGQlt6uvllmki6fXA6tWR0GbDBiAehmxLktwqbmgISEwELBZAp4v1rqYUSZJw8fZF+ZVN\nswPOK074Q+JmlCxOX4yK3AqU55ajNKsUyQnJwtam2Lnp90dapHk8aPB4IO5tVoQewAabDRXTpmGz\n3Y5VFgsM/DOCiOKElu5yGdIQCaKlg01ERERERI+or09uiVZTI/86cUIOK0QyGoGCgkhos349kKyR\ny/EzZ4D33pPDquPHAZcr8j27XQ6bCgqA7duBUf/+QxPDN+SDs82ptEZr7GkUtrbJaEJpVqnSGm1x\n+mLoeOE+KQyEQmjo68O/Xr+O97u6EFTpKnCa0YhNdju22O3YbLdjoVb+XCMiugst3eUypCESREsH\nm4iIiIiIBHG5AKdTfmWzd68cYoiWlCQHNSOhTUGB/HJlIn38MfCTnwD790f/mSefBF5+GXj6afX2\nRffV3NOsvLKpaa1Bf6Bf2NoL0hYobdE2ZW+CJckibG2KnSsDA9h+4QIOeTyq11poMmHzcGizyW7H\ntIQE1WsSEUVLS3e5DGmIBNHSwSYiIiIiIpXcuiWHNiMvbS5fFl8jJUVuibZpk/xr1Sr59Y0auruB\nv/kb+fXMo9q+HXjrLSA9Xdy+6KH5g34caD8AR5MDVU1VONd1TtjaCfoEbJi/AeW5cmizfMZyvrKJ\nYz9ua8N3WlsntKYOQL7FooQ2RVYrTAbDhO6BiGg0Ld3lMqQhEkRLB5uIiIiIiCbItWuRVzY1NcCV\nK+JrWK1Aaan8ymbTJmD5cnnOzXidPg1UVADXr49/rcxMwOGQ90aa0OHuwK7mXXA0OVDdUg2PX9zL\niUxLJrblbENFbgU2L9wMe7Jd2Nqkvs+dOYMPu7tjuodkvR5P2mxKaLPCbIaewR8RTSAt3eUypCES\nREsHm4iIiIiIYqS1dWxoIyIAuVN6OrBxYyS0WboUeNjLzdOn5TVGz5wZL7tdfmXEoEZzAqEAjlw7\ngqrGKjiaHTh+47iwtfU6PQrnFqI8pxwViyqwevZq6HUCQkRShSRJmHnwILoCgUf6fJJOhxS9Hq5Q\nSOi+MhIS8NTwLJstdjvmm0xC1yciupOW7nIZ0hAJoqWDTUREREREGiBJQGOjHNaMBDddXeLrzJol\nBzYjoc3ChfcPbbq7gRUr1AmQMjPlAIitzzSt09uJT5o/QVVTFT5p/gTdA+JeVUxPmY5tOdtQnluO\nrTlbMSN1hrC1afyaBwaQe+TIuNZoLCiAOxTCbpcL1T09OOB2wy/4enFxcrIS2GxMS0Ma59kQkWBa\nustlSEMkiJYONhERERERaVA4DJw7Fwls9u0DenvF15k3Tw5rRkKbefPGfn/79vHNoHmQ7duBd99V\nb30SKhQO4diNY3A0OeBocuDItSMIS2Fh6+fPzkdFbgXKc8uxbu46GPUqzVeiqLxz8ya+ePHi+NbI\ny8N/nTlT+c8DoRAOuN2odrmw2+XCCa93vNscQw+gwGpVQptCqxWJIlo+EtGUpqW7XIY0RIJo6WAT\nEREREVEcCIWAU6ciL21qawHBl5sAgJycSGgTCgFf/KL4Gnf66CPgM59Rvw4J193fjd0tu+FolkOb\nm96bwta2JdmwJWcLynPKUZ5bjjnWOcLWpuh89fJl/Ot9XtEtNJkgAWgdHLz3GpmZ+JfFi+/5/a6h\nIdT09qK6pwfVLhfa/f7xbPlTUvV6lKalKaHNY6mp0HGeDRE9JC3d5TKkIRJESwebiIiIiIjiUCAA\nNDRE5tnU1QH3uSjVtJISeT4NxTVJknC68zQcTQ5UNVWhrqMOwXBQ2PrLZyxHea4c2BTPK0aSMUnY\n2nR3qxsa7vnS5S9nzsQ/L1oEAPhaYyP+s7Pz7muYzTi2Zk1U9SRJQtPAgNwazeVCjcsFt+B5NrMS\nE7F5eJ7NZrsdc5L4+4iIHkxLd7kMaYgE0dLBJiIiIiKiScDvBw4fjoQ2hw/LQU68OHMGGPXvSBT/\nPH4PalprlNCm3d0ubO3UhFQ8tfAp5ZVNtj1b2NokkyQJefX1uDQwMObrNoMB/754Mf7LqBZmAPBe\nZye+cvkyPHeEKkuSk3GhoOCRXq8Ew2Ec83qVeTYHPR4EBF9N5qWkYMtwYLMxLQ0WI1vsEdGnaeku\nlyENkSBaOthERERERDQJ9ffLr2tGQpuGBrl9mVZ997vAq6/GehekEkmScPH2RXmWTbMDzitO+EPi\n2lotTl+M8pxyVCyqQGlWKZITkoWtPZX5QiF8r6UFb127BgnABpsN7+TlIctkuuvPXxkYwF9cuIA6\njwc6AF+fOxc/ys5GqsEgbD+1vb3KS5szPp+QdUcYdTqss1iwZdo0bLbbUWCxIIHzbIgI2rrLZUhD\nJIiWDjYREREREU0BHg+wf38ktDl5EtDSv+Jv3gxUV8d6FzRB+gP9cF5xoqqpCo4mBxp7GoWtbTKa\nUJpVivLcclTkVmBx+mLOIBmnA729OOTx4Btz58L4gNAiGA7j51evoshqxYa0NFX3ddPvx56R0Kan\nB9eGhoSubzEYsDEtTXlpszQlhb+XiKYoLd3lMqQhEkRLB5uIiIiIiKagnh55DsxIaHPuXGz3Y7cD\n3d0AL0CnpOaeZuxq3oWqpirUtNagP9AvbO0FaQuUtmibsjfBkmQRtjZphyRJuNTfj2qXC7tdLuzt\n7UWf4NeDc4bn2Yy8tJmZmCh0fSLSLi3d5TKkIRJESwebiIiIiIgInZ3Avn1yYLN7N9DSMvF78HgA\nCy/Qpzp/0I8D7QeU1mhnb50VtnaCPgHF84tRkVuB8txyLJ+xnC8jJqlAOIyjfX1KaHPY40FQ8LXm\n8tRU5ZVNSVqasLZuRKQ9WrrLZUhDJIiWDjYREREREdEYt28DGRkTX7erC5g+feLrkqZ1uDuwq3kX\nHE0OVLdUw+P3CFt7tnk2ynPlVzZbFm6BPdkubG3Slr5gEM7eXiW0Od8v7rUWACTodCiyWpVXNmss\nFhgYABJNGlq6y2VIQySIlg42ERERERHRGB4PYLPFpi5f0tB9BEIBHLl2BI4mB6qaqnD8xnFha+t1\nehTOLVRao+Vn5kOv49D4yeqa3489LpcS2twUPM8mzWhE2ah5NrnJyXy1RRTHtHSXy5CG6CG9/fbb\nePvttz/1dZ/Ph4aGBuU/M6QhIiIiIiLNkCQgPR1wuSauJmfS0CPo9Hbik+ZP4Gh2YFfTLnQPdAtb\ne3rKdGzL2Yby3HJszdmKGakzhK1N2iJJEs75fEpg4+zthS8cFlojKylJmWezKS0NGZxnQxRXGNIQ\nxbFXXnkFO3bseODPMaQhIiIiIiJN2bwZ2LNnYutVV09cPZp0QuEQjt04Js+yaXLgyLUjCEviLtrz\nZ+crrdEK5xbCqDcKW5u0ZSgcxmGPRwlt6j0eiI1sgFVmsxza2O3YYLMhmfNsiDSNIQ1RHONLGiIi\nIiIiikvf/S7w2msTV+/b3wZ+/OOJq0eTXs9AD3a37EZVUxUcTQ7c9N4UtrYtyYYtOVtQnlOObbnb\nMNc6V9japD29gQD2jZpnc3lgQOj6STodNthsykubVWYz9HxVSKQpDGmIJiEtHWwiIiIiIqJPOXMG\nWLFi4uplZQG/+hXw9NNseUbCSZKE052nlVk2dR11CIaDwtZfPmO58sqmeF4xkoxJwtYm7WkfHMTu\n4Xk2e1wudAUCQtefZjTiqeFZNlvsdmQnJwtdn4genpbuchnSEAmipYNNRERERER0VyUlwP79E1tz\n2zbgF78A8vImti5NKR6/BzWtNUpo0+5uF7Z2akIqNmVvQkVuBcpzy5Ftzxa2NmlPWJJw2utVQpta\ntxuDgufZLDSZsGU4tNlkt2NaQoLQ9YnowbR0l8uQhkgQLR1sIiIiIiKiu/r4Y+Czn534ugYD8L/+\nF/DKK8C0aRNfn6YUSZJwqfsSqhqr4Gh2wHnFCX/IL2z9xemLUZ4jv7LZuGAjkhP4KmIyGwyFcNDj\nUUKbY319EHmZqgOwxmJRXtkU2WxI0usFViCiu9HSXS5DGiJBtHSwiYiIiIiI7mn7duC992JTe9o0\n4Ic/BL78ZcDIIe00MfoD/XBecSqvbBp7GoWtbTKaUJpVqrRGW5K+BDq295vUegIB1AzPsql2udAy\nOCh0/WS9HiWj5tksT03lPBsiFWjpLpchDZEgWjrYRERERERE99TdLc+muX49dnt47DG5BdqWLbHb\nA01ZzT3N2NW8C44mB/a07kF/oF/Y2gvSFiivbDZlb4IlySJsbdKmloEB7B4Obfa4XOgJipuNBAAZ\nCQnYPGqezTyTSej6RFOVlu5yGdIQCaKlg01ERERERHRfZ84ApaWAyyVuzbQ0ec0PPwSivWp45hng\njTeARYvE7YPoIfiDfhxoPwBHkwOOZgfO3jorbG2j3ogN8zcooc2KmSv4ymaSC0kSTnq9qO7pQbXL\nhQNuN4YEX70uTk5W5tmU2e2w8VUi0SPR0l0uQxoiQbR0sImIiIiIiB7ozBmgvFzMi5rMTMDhAJYv\nB44dA77+daCuLrrPJiTIP//3fw/YbOPfC9E4XPVcxa6mXahqqkJ1SzU8fo+wtWebZytt0bYs3AJ7\nsl3Y2ncKS2F093ertv69pKekQ6/jPJUR/aEQDrjdSmu0k16v0PX1AAqsViW0KbRakch5NkRR0dJd\nLkMaIkG0dLCJiIiIiIii0t0N/O3fAr/97aOvsX078NZbQHp65GuSBPz+98Df/R3Q0RHdOjNmAD/6\nEfDf/ztgMDz6fogECYQCOHLtiPzKpsmBYzeOCVtbr9OjcG6h8somPzNfaLjR5evCjDdmCFsvWrde\nuoWM1IwJrxsvuoaGsGfUPJt2v1/o+ql6PUrT0pTQ5rHUVL7eIroHLd3lMqQhEkRLB5uIiIiIiOih\nfPwx8NOfArW10X+mpAT49reBp5++98/098vtzH78Y2BgILp1V64E3nxTbp1GpCGd3k580vwJHM0O\n7Grahe4BcS9VpqdMx9acrajIrcDWnK2YkTq+gIUhjfZJkoSmgQFUD4c2NS4X3KGQ0BqzEhOVWTab\n7XZkJiUJXZ8onmnpLpchDZEgWjrYREREREREj+TsWeC994D6erlt2eiZNXY7kJ8PFBQAX/gCMOrf\nfx7o6lXg5ZeBd9+N/jPPPw+8/jqwYEH0nyGaIKFwCMdvHEdVUxUcTQ4cuXYEYSksbP382flKa7TC\nuYUw6h9u7ghDmvgTDIdxbHiezW6XCwc9HgQEX9suS0lRQpvStDRYOM+GpjAt3eUypCESREsHm4iI\niIiIaNwkCfB6Ab8fSEoCzGZgvG1zDh2S588cPRrdzyclAd/8JvCd78j1iTSqZ6AHu1t2K63Rbnhv\nCFvblmTDlpwtKM8px7bcbZhrnfvAzzCkiX/eYBD73W7lpc0Zn0/o+kadDoWj5tmstViQwHk2NIVo\n6S6XIQ2RIFo62ERERERERJoVDgPvvCO/rLkR5UX27NnAa68BX/wiwEtE0jhJknC687Qc2DQ7cKD9\nAILhoLD1H5/xOCpyK1CeW47iecVIMn66hRVDmsnnpt+PPb29qO7pQbXLhetDQ0LXtxgMKBs1z2ZJ\nSgrn2dCkpqW7XIY0RIJo6WATERERERFpntcrz6p54w35tU401q4FfvlLYP16dfdGJJDH70FNaw0c\nTQ5UNVWh3d0ubO3UhFRsyt6ktEZbaF8IgCHNZCdJEi7292O3y4Vqlwv7envRJ3iezdykJKU12lN2\nO2YmJgpdnyjWtHSXy5CGSBAtHWwiIiIiIqK40doKfOtbwPvvR/+Z7duBn/wEmPvgtk9EWiJJEi51\nX1ICG+cVJ/yhKEPKKCxOX4zynHKsn7seX/jgC8LWjRZDmtgIhMOo7+tT5tkc9nggNrIBVqSmKqHN\nk2lpSDUYBFcgmlhaustlSEMkiJYONhERERERUdxxOoEXXwROnozu55OT5ZZpL70EpKSouzcilfQH\n+uG84lRao13uvhzrLY0LQxpt8ASDcPb2KvNsLvT3C10/UadDkc2mhDb5FgsMbI1GcUZLd7kMaYgE\n0dLBJiIiIiIiikuhEPCb3wDf+x7Q1RXdZ+bNA376U+CFFwBeElKca3G1yIFNkwM1rTXwBcQOi1cb\nQxptuub3Y/dwYLPb5cJNwfNs0oxGbEpLU0KbnORkzrMhzdPSXS5DGiJBtHSwiYiIiIiI4prbDfzo\nR/L8mUAgus8UFwNvvgmsWaPu3ogmiD/oR11HHaoaq+BoduDsrbOx3tIDMaTRPkmScM7nQ/XwPBtn\nby/6w2GhNbKSkrBl2jRsttvxVFoapnOeDWmQlu5yGdIQCaKlg01ERERERDQpNDbK7cw+/DC6n9fp\ngC99CfinfwJmzVJ1a0QT7arnKnY17YKj2YHq5mq4/e5Yb+lTGNLEn6FwGIc8Hux2uVDd04OjfX0Q\nG9kAq8xmbLHbsdluxwabDcmcZ0MaoKW7XIY0RIJo6WATERERERFNKtXVwDe+AZw7F93Pm81yy7QX\nXwRMJnX3RhQDwXAQh68eVlqjHbtxLNZbAsCQZjLoDQSwt7dXDm1cLjQODAhdP0mnw5OjWqOtNJuh\nZ2s0igEt3eUypCESREsHm4iIiIiIaNIJBoFf/xr4/veBnp7oPpOdDfzsZ8Czz3JeDU1qnd5OfNL8\nCRzNDuxq2oXuge6Y7IMhzeTTNjg4Zp7N7WhbUEYp3WjEU8OvbLbY7ViQnCx0faJ70dJdLkMaIkG0\ndLCJiIiIiIgmrZ4eYMcO4Fe/AkKh6D5TVibPq1mxQt29EWlAKBzCnpY92PbutgmvzZBmcgtLEk57\nvageDmxq3W4MCp5nk2MyKfNsNqWlwZ6QIHR9ohFausvVx6QqEREREREREdGjmDYN+OUvgTNngG1R\nXkLv3QusWgV85StAV5e6+yOKMYPegFWzV8V6GzQJ6XU6rLRY8Hfz52PXE0/AVVyMPU88gZfnz8ca\niwUi3is2Dw7i369fx/PnzmF6XR0Kjh3D91pasNflgl9wIESkFQxpiIiIiIiIiCj+5OUBVVXARx8B\nixc/+OfDYbld2qJFwM9/DgwNqb9Hoinm6LWjCEu8SJ8qTAYDNtnteG3hQhzNz0dXcTH+v2XL8P/M\nno1sAfPAwgCO9vXhn9rbsenUKdgPHED5qVP4WUcHTnm9CLNBFE0SbHdGJIiWnsgRERERERFNKUND\ncvuzHTsAtzu6zyxeLIc1Tz/NeTU06XT5ujDjjRkxqT3HMgfPLX0Olcsq8eT8J2HQG2KyD4q9loEB\n7Ha5UO1yYY/LBVcwKHT9GQkJeGp4ls1mux3zBARDNHVo6S6XIQ2RIFo62ERERERERFNSVxfwD/8A\n/Md/yC9norFtG/CLX8gvc4gmiViGNKNlpGTg2aXP4vllz6NsQRkSDJwvMlWFJAkn+vqUeTYH3G4M\nCb6WXpKcjM12O7ZMm4aNaWmwGY1C16fJRUt3uQxpiATR0sEmIiIiIiKa0k6fBl58UZ5FEw2DAfjq\nV4Ef/ECeeUMU57QS0oxmN9nxzJJnUJlXiS05W2Ay8tXDVNYfCuGA262ENie9XqHrGwAUWK1yaGO3\no9BqRYKekz8oQkt3uQxpiATR0sEmIiIiIiKa8iQJ+OMfgZdeAlpaovvMtGnAD38IfPnLAP8GNsUx\nLYY0o5kTzfjs4s+iMq8SFbkVSE1MjfWWKMZuDQ2hZrg1WrXLhQ6/X+j6ZoMBpTYbtkybhs12O5al\npEDHVpdTmpbuchnSEAmipYNNREREREREwwYHgTffBF59FYj2b2o/9pjcAm3LFnX3RqQSrYc0oyUb\nk1GeW47KvEp8dvFnYTPZYr0lijFJktA4ap7NXpcL7lBIaI3ZiYnKK5un7HZkJiUJXZ+0T0t3uQxp\niATR0sEmIiIiIiKiO9y4AXzve8Dbb8uvbKLxzDPAG28AixapujUi0eIppBkt0ZCILQu3oDKvEs8s\neQbpKemx3hJpQDAcRkNfnxLaHPJ4EBB8pf1YSooyz6bEZoOFryknPS3d5TKkIRJESwebiIiIiIiI\n7qGhQZ5XU1cX3c8nJABf/zrw938P2Pg3/Ck+xCqk+bfP/BuqW6pR1ViFgeDAuNYy6Awoyy5DZV4l\nnl36LGaZZwnaJcU7bzCIWrdbCW3O+nxC1zfqdFg/ap7NWosFRs6zmXS0dJfLkIZIEC0dbCIiIiIi\nIroPSQJ+9zvgW98COjqi+8yMGXLLtL/6K8BgUHd/ROMUq5Dm1ku3kJGaAd+QD44mB3Ze2ImPLn+E\nvqG+ca2rgw4b5m9AZV4lPp/3ecyzzRO0Y5oMbvj92ONyKaHN9aEhoetbDQaUpaUpL20WJydzns0k\noKW7XIY0RIJo6WATERERERFRFPr75XZmP/4xMBDl3/pfuRL45S+BkhJ190Y0DrEOaUYbDA5id8tu\n7LywE3+6+Ce4Bl3jrlMwpwDP5z2PymWVWGhfOO71aPKQJAkX+/tRPRza7O3thVfwPJt5SUnYbLcr\nv2YkJgpdnyaGlu5yGdIQCaKlg01EREREREQP4epV4OWXgXffjf4zzz8PvP46sGCBatsielRaCmlG\nC4QC2HdlH3Ze2Ik/XPwDbvlujbvmylkrUZlXicq8SuRl5I17PZpcAuEw6vv6UN3Tg2qXC0c8HoiN\nbIAVqanYMhzYlKSlIYWvLeOClu5yGdIQCaKlg01ERERERESP4NAhef7M0aPR/XxSEvDNbwLf+Q5g\nNqu7N6KHoNWQZrRQOIQD7Qew88JOfHDhA1zruzbu+nnT8+TAZlklnpj5BFtS0ad4gkHs6+1VWqNd\n7O8Xun6iTocim00JbfItFhj4+1CTtHSXy5CGSBAtHWwiIiIiIiJ6ROEw8M478suaGzei+8zs2XLL\ntL/4C4DDpUkD4iGkGS0shVF/rR47z+/Ezgs70drbOu69LLQvVF7YFMwpYGBDd3V1cBC7h1uj7Xa5\n0BkICF3fbjRi0/A8m812O3I4z0YztHSXy5CGSBAtHWwiIiIiIiIaJ69XDl7eeAPw+6P7TEEB8Oab\nwPr16u6N6AHiLaQZTZIknLx5EjsvyIHNxdsXx72vedZ5+Hze51GZV4mieUUw6NmOij5NkiSc9fmU\neTbO3l70h8NCaywwmZRXNk/Z7UhPSBC6/sPoCQTwwvnzY772u2XLMC2Ge5pIWrrLZUhDJIiWDjYR\nEREREREJ0toKfOtbwPvvR/+Z7duBn/wEmDtXvX0R3UdYCqO7v3vC66anpEOvE/ua7HzXebx//n3s\nvLATpztPj3u9makz8dzS51C5rBKlWaVIMEyNC2l6eP5wGMAGR8cAACAASURBVIc9HlT39GC3y4Wj\nfX0QGdnoAKwym5XQZoPNBtMEzrP512vX8NXGxrFfW7QI/3POnAnbQyxp6S6XIQ2RIFo62ERERERE\nRCSY0wm8+CJw8mR0P5+cLLdMe+klICVF3b0RTRFNPU1KS7Sj16OcHXUf05Kn4XNLPofKvEpsXrgZ\nScYkAbukycoVCGBfb6/y0qZxYEDo+ia9HhtGzbNZaTZDr2JrtIJjx3C0r2/s1ywWHMnPV62mlmjp\nLpchDZEgWjrYREREREREpIJQCPjNb4DvfQ/o6oruM/PmAT/9KfDCCwDnEBAJ0+5uxwcXPsDOCztR\n114HCeO74rQmWfFni/8MlXmV2Ja7DSkJDFfp/tqG59lU9/RgT28vbgueZ5NuNOIpu10JbRYkJwtb\n+5zPh8eP3j3oPLd2LZalpgqrpVVaustlSEMkiJYONhEREREREanI7QZ+9CPgl78Eor2UKy6Wf36K\n/A1lool0o+8G/nDxD9h5YSecV5wISaFxrZeSkIKnFz2NyrxKfGbRZ2BJsgjaKU1WYUnCKa9XDm1c\nLux3uzEoeJ5NbnIyNg+HNmVpabCPY3bMt5qb8XpHx92/N28efpKT88hrxwst3eUypCESREsHm4iI\niIiIiCZAY6PczuzDD6P7eZ0O+NKXgH/6J2DWLFW3RjRV3e6/jT9d/BN2XtiJ3S27EQiP73VDkiEJ\nW3O2ojKvEs8seQb2ZLugndJkNhgKoc7jUV7aHPd6x/nWayw9gDUWixLarLfZkKSPbh5UMBzGvMOH\ncXNo6K7fn52YiPbCQhijXC9eaekulyENkSBaOthEREREREQ0gaqrgW98Azh3LrqfN5vllmkvvgiY\nTOrujWgK6x3sxUeXP8LOCzvhaHJgMDg4rvWMeiM2ZW9CZV4lnl36LGakzhC0U5rsugMB1AzPsql2\nudA6OL7fi3dK0etRkpaGzXY7NqelYXZiInT3aLG5t7cXL5w/f9/1fr9sGTampd3z+9MTEu65frzQ\n0l0uQxoiQbR0sImIiIiIiGiCBYPAr38NfP/7QE9PdJ/JzgZ+9jPg2Wc5r4ZIZd4hL6oaq/D+hffx\n8eWP4Qv4xrWeXqdHSVYJKvMq8dzS5zDHOkfQTmkqaB4YwO7h0GaPywVXMBjrLT2Uk2vW4AmzOdbb\nGBct3eUypKG44PF4cOLECTQ0NKChoQHHjh1DU1MTRn77tra2YsGCBTHdo5YONhEREREREcVITw+w\nYwfwq18BoSjnYpSVAW++CaxYoe7eiAgAMBAYwCfNn2DnhZ348NKHcPvd415z/dz1qMyrROWySixI\nWzD+TdKUEZIkHO/rU17Z1LndGNL4lf33s7KwIzs71tsYFy3d5TKkobiwatUqnDx58p7fZ0hDRERE\nREREmnL+PPC//zewa1d0P6/XA//jfwD/+I9ARoa6eyMixVBoCDWtNdh5fif+eOmPuN1/e9xrrp69\nWg5s8iqxZPoSAbukqaQ/FMJ+t1uZZ3PKN75XX2p4PDUVZ9aujfU2xkVLd7kMaSgurFy5EqdOnQIA\n2Gw2rFq1ChcvXsTNmzcBMKQhIiIiIiIiDZIk4P/+XzmsuXw5us/YbMAPfgB89atAYqK6+yOiMYLh\nIPa37cfOCzvxwYUPcMN7Y9xrPpbxGJ5f9jwq8yrx+IzH436OB028W0ND2DNqnk2H3x/rLcEA4HpR\nEWbE8f9PaekulyENxYW33noLGRkZWLNmDXJzc6HT6bBx40Y4nU4ADGmIiIiIiIhIw4aG5PZnO3YA\n7ijbKi1eDPz858DTT3NeDVEMhKUwDnUcws4LO7Hzwk60u9vHveaiaYuUlmj5s/MZ2NBDkyQJjQMD\nqB4ObWpcLniiba0pSLbJhN/m5aHQZpvQuqJp6S6XIQ3FLYY0REREREREFFe6uoB/+AfgP/4DCIej\n+8y2bcAvfgHk5am7NyK6J0mScOzGMew8Lwc2jT2N414zy5aFz+d9HpV5lVg/bz30Or2AndJUEwyH\n0dDXp4Q2Bz0eBFW87v/izJn4l0WLYDUaVasxUbR0l8uQZopqbm5GfX09rl69iqGhIdjtdixduhRF\nRUUwmUyx3l5UGNIQERERERFRXDp9GnjxRWDv3uh+3mCQ25/94AfAtGnq7o2I7kuSJJy9dVZ5YXP2\n1tlxrznbPBvPLX0OlcsqUZJVAqM+/i/AKTa8wSBq3W4ltDkraJ6NzWDAvy1ejC/MnClkPS3Q0l0u\nQxoNuHbtGurr63HkyBHU19ejoaEBfX19yvezsrJw5coVIbX++Mc/4h//8R9x/Pjxu37fbDbjS1/6\nEn7wgx9g+vTpQmqqhSENERERERERxS1JAv74R+Cll4CWlug+M20a8MMfAl/+MjAJ/hYz0WRw6fYl\nJbA5fuPu920PY3rKdDy75FlULqvEpuxNSDTE78wPir0bfj/+385OvNbRga5A4JHWWG+14r1ly5AV\nJ3+xP1paustlSBMjdXV1+NnPfoYjR47g+vXr9/1ZESGN3+/HX//1X+Pdd9+N6uczMjLw/vvvo6Sk\nZFx11cSQhoiIiIiIiOLe4CDw5pvAq68CXm90n3nsMbkF2pYt6u6NiB5Kq6sVH1z4ADsv7MShq4fG\nvV6aKQ1/tvjPUJlXia05W5GckCxglzQV/bitDd9pbX20zy5ciG/Pny94R7GnpbtcNjuMkaNHj+IP\nf/jDAwMaEcLhMF544YVPBTQGgwHZ2dlYuXIlbHcMeurq6kJFRQUOHRr//6EQERERERER0T2YTMDL\nLwOXLwN/9VdANIPEz50Dtm4FPvc5oHH8szGISIxseza+WfRNHPzrg7j6jav454p/xsYFGx953kzv\nYC/+8/R/4tnfPYuM1zPwwvsv4Pfnfg/vUJSBLtGwQx7Po3/W7Ra4E7obhjQaZDabha73+uuv409/\n+tOYr33lK19Be3s7WlpacOLECfT09OCDDz7A/FGpaH9/P/78z/8cbh5EIiIiIiIiInXNng385jdA\nfT1QXBzdZz78UH5V83d/B/Df3Yk0ZY51Dr5W8DXs/W97ceObN/Drz/4aW3O2PvK8GV/Ah9+f+z1e\neP8FZLyeged+9xzeOf0O3IM8+3R/kiSNL6TxeMBmXOpiA9MYs1gsyM/Px9q1a1FQUIC1a9eitbUV\nZWVlQtbv7u7Gq6++OuZrr732Gl5++eUxX9Pr9XjuuedQUFCADRs2KO3Vrl69ip///OfYsWPHfevU\n1NSgv79/3PtdtWoV5syZM+51iIiIiIiIiOLSmjXA/v3A734HfOv/Z+++o6sq87aPf5PQizQBKSqi\nMEmICAIWFJARHAsjYiwoqDiKXbGAqDA6jIMo9oLt8cGoWEYJIooFRQkKIkZBIQQRBBEQAWGQhE7O\n+8d+h8eI5qSdnOTk+1mLJfvwu/e+ssbgrH1l7/tm+OGHgud37YL77oPnnw9emXbxxZCQUDZZJRVK\nk9pNuKzTZVzW6TI2bdvElG+mkJ6dzrRl09ixZ0eRz7d993YmL57M5MWTqRpflV6te5GalErfxL7s\nX6t87zGtsvfd9u3F3o8GYN2uXSzfvp3WNX3dXqS4J02ULFu2jB07dpCYmEh8fP4HmmbMmJGvpCnJ\nnjTDhw9n7Nixe4+7d+/OjBkziCvg8enp06fTq1evvcd169Zl+fLlNGrU6A/XtGrViu+//75YGX/t\nhRdeYODAgYWadU8aSZIkSVJM27o1KGDuvhu2bSvcmg4d4OGHoRzvMSspsGXHFqZ+O5X07HTe/vZt\ntu4q2Q9AJ8Ql0KNVD1KTUumX2I9mdZuVUlJVZBPWruWCxYtLdo6kJAY0bVpKicqH8nQv19edRcmh\nhx5KcnLyPgVNacrLy+PZZ5/N99k//vGPAgsagBNPPJFu3brtPd6yZQuvvvpqRDJKkiRJkqQ/UKsW\n3H57sF/NgAGFWzN/PvToAWefDcX8gU9JZaNu9br0T+nPa2e/xvph65l0ziQGHD6A/arvV6zz7Qnt\n4cPlH3L121fT4oEWHD/+eB789EFWbl5ZyslVkYR71VnrGjU4pEaNgs/hKzUjytedxbDZs2ezfv36\nvcetW7fmhBNOKNTaSy65hI8//njv8eTJk7nyyiv/cH7OnDns3r272Fn/q2HDhiU+hyRJkiRJMaVl\nS5gwAa6+GoYMgc8/D79m4kR480246Sa49VYo5f1vJZWuWlVr0S+pH/2S+rFj9w4++O4D0rPTeeOb\nN9i4bWORzxcixKwfZjHrh1ncOO1GujTvQmpSKqnJqRzW8LAIfAUqrwoqaS5s2pRH27QB4Jpvv+WF\nn34q8jlUcpY0MWzq1Kn5jnv37h32KZpfz/7ajBkzyM3NpXbt2r87f8ABBxQvpCRJkiRJKpxjj4U5\nc4LC5pZb4McfC57fsQPuuguefTZ4ZdrAgRDBN3pIKh3Vq1TntLancVrb09i1ZxcZ32eQviid1xe/\nzk+5v38TPZzP13zO52s+55bpt9C+afugsElKJblxcqHvF6riCYVCbN2zZ5/P6yUk8GTbtvT/1SvM\nnk9K4pSGDbliyRJ++c2a3D17CIVC/rsSIf6XOYbNnz8/33HXrl0LvbZ58+b59njZuXMnixYtKq1o\nkiRJkiSpOOLj4cILg1eg3XYbVK8efs2PP8JFFwUlz6efRj6jpFJTNaEqvVr34ok+T7D6xtXMHDST\nIUcPoeV+LYt9zq9/+po7ZtxByhMpJI1LYsT0EXz545e4dXnsiYuL44vOnRnSogX/rVeOr1ePr7p0\nyVfQ/Nd5TZvyVefOHLdf8Mq9OOD6li35onNnC5oIsqSJYdnZ2fmOk5OTi7T+t/O/PZ8kSZIkSYqS\nOnVg9GjIzoazzircmrlzoWvXYH+bVasim09SqUuIT6Dbwd146OSHWHn9Sj679DOGdR1G6wati33O\nb37+hrs+uYtOT3fi0EcOZdi0YcxZNYe8UF4pJlc01U5I4KE2bZjZoQNjW7fmoyOO4OAC9qBpV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88mPyQnklOl+danU4rc1ppCalcmqbU6ldrXYpJVVRlad7uZY0UikpT9/YkiRJkiRVSBs3wqhR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\nMHIkPPYYFOZ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"text/plain": [ "" ] }, - "execution_count": 7, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.plot_compression_experiments(res_t, comp_ratios,\n", - " \"../figs/compression_traffic.png\", 10)\n", + " \"../figs/compression_traffic.png\")\n", "Image(filename=\"../figs/compression_traffic.png\")" ] }, @@ -247,28 +269,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### FSWT x GWT" + "### Reconstruction Error: FSWT vs GWT" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 1.23442666, 1.27973632, 1.30163643, 1.28388523, 2.60036447,\n", - " 5.70455735])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " GWT error| SWT error| Reduction\n", + "-----------------------------------------------\n", + " 2.2200923| 1.5877000| -0.2848496\n", + " 1.1735329| 0.8091524| -0.3104988\n", + " 0.6413987| 0.4088434| -0.3625753\n", + " 0.3337205| 0.2063473| -0.3816763\n", + " 0.1571017| 0.0444101| -0.7173164\n", + " 0.0660063| 0.0084063| -0.8726447\n", + "\n" + ] } ], "source": [ - "np.divide(res_t['GWT'], res_t['FSWT'])" + "reduction = np.divide(res_t['FSWT'], res_t['GWT']) - 1\n", + "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n", + "text += \"-\"*47 + \"\\n\"\n", + "for i in range(len(comp_ratios)):\n", + " text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_t['GWT'][i], res_t['FSWT'][i], reduction[i])\n", + "print(text)" ] }, { @@ -280,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -293,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -312,68 +343,101 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ - "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_h, time_h = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_h, time_h = exp.compression_experiment(G, F, algs, comp_ratios, 10)" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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atGku5w0c50Nuu+02ff75503aJrqxUlJSNGvWLH3yySe69NJLG92O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\nvbM5yc1lF1OARUleTWu6v7tpSRuZcwq7pqmpKX/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hmuvRZatoxfvhp0SBnm5yRXGeau2B9rcxmmtOVfLiea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\nFbq/8f3y9nD83mOeLU/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OQOALORO2Uza9Ys7d+/X1LhbXAci7n11lvVokUL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\nMpbRunfvrtq1a0tyveNw/vx5vfjii34b74UXXlBeXl6hcZznMTExU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"text/plain": [ "" ] }, - "execution_count": 64, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.plot_compression_experiments(res_h, comp_ratios,\n", - " \"../figs/compression_human.png\", 100.)\n", + " \"../figs/compression_human.png\")\n", "Image(filename=\"../figs/compression_human.png\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### FSWT x GWT" - ] - }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { + "image/png": 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ABgAAAAAAdAmENACAOzN4sPT1r0vv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\nb9O+tKrWilAoFEBCBcGSRiqAEukbW5IkSZIk7cfkydC1K6xYE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"text/plain": [ - "array([ 1.30960783, 1.46880193, 1.40233368, 1.83547972, 2.97625452,\n", - " 5.96806709])" + "" ] }, - "execution_count": 65, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.divide(res_h['GWT'], res_h['FSWT'])" + "res_h_copy = dict(res_h)\n", + "res_h_copy.pop('HWT')\n", + "\n", + "exp.plot_compression_experiments(res_h_copy, comp_ratios,\n", + " \"../figs/compression_human2.png\")\n", + "Image(filename=\"../figs/compression_human2.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reconstruction Error: FSWT vs GWT" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " GWT error| FSWT error| Reduction\n", + "-----------------------------------------------\n", + " 6.8382095| 5.0477291| -0.2618347\n", + " 3.8316544| 2.7098935| -0.2927615\n", + " 2.1117686| 1.7143273| -0.1882031\n", + " 1.1529724| 0.6686821| -0.4200363\n", + " 0.5595893| 0.2221975| -0.6029275\n", + " 0.2478246| 0.0556169| -0.7755798\n", + "\n" + ] + } + ], + "source": [ + "reduction = np.divide(res_h['FSWT'], res_h['GWT']) - 1\n", + "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n", + "text += \"-\"*47 + \"\\n\"\n", + "for i in range(len(comp_ratios)):\n", + " text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_h['GWT'][i], res_h['FSWT'][i], reduction[i])\n", + "print(text)" ] }, { @@ -385,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 60, "metadata": { "collapsed": true }, @@ -398,61 +462,120 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#vertices = 4881\n", + "#edges = 11937\n" + ] + } + ], + "source": [ + "print(\"#vertices = \", G.number_of_nodes())\n", + "print(\"#edges = \", len(G.edges()))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ - "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "sys.setrecursionlimit(G.number_of_nodes())\n", - "res_w, time_w = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_w, time_w = exp.compression_experiment(G, F, algs, comp_ratios, 1)" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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pSZMmafXq1Tp37pxsNpvLFxAuli5dqpycHMVJeszsYALgMUlxkrZt2+bXLS5D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\nMacAAADmu+mmm1xuc84NlJaW6pNPPgl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T9x80c3U+fpOjwy5RH+3PpnwEklSZIkSZIkSVJ2xaIMEwqFiu1NypJTGSYU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\nJElSpWEYRpIkS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/70pz9Rq1atMu3/6KOP5s9//jPHHHPMQfsODphybc+ePdx///1l2n9U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\nVKmAaeg9oalWrVpOH7NarUzbjaBG3/Efi8Xi9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"text/plain": [ "" ] }, - "execution_count": 76, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.plot_compression_experiments(res_w, comp_ratios,\n", - " \"../figs/compression_wiki.png\", 20.)\n", + " \"../figs/compression_wiki.png\")\n", "Image(filename=\"../figs/compression_wiki.png\")" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { + "image/png": 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c9Yu1Z66ikpLS1wfY0hF/ovqOpMle/Mm/eXUTvtOm11ex6R9kjNTJ05ZICT\nmZDJ8mkAAAAAgJARSu9yCWmAAAmlLzaAEGEY5syazZu9M21Oj8BL9YwMqajIG9rk5kqBeCG+f7/0\noQ9J9fXDf1Z2tvTnP0s33DD8Z8HjbPdZHW09OijAOdp6VJ0XOq1uzyM5JtkMbC4KcOakzdHstNmK\nj4q3uj0AAAAAwBgTSu9yCWmAAAmlLzaAEGUY0qFD3pk2W7dKbW2Br5OdbYY1A6HN9On+P2P/fjP4\nOXMmcH2lpkqlpQQ1QeA23Ko/X68jLRfNvHk/vKlur5bbcFvdoseUcVN89755P8CZmjxVEfYgL+sH\nAAAAABgTQuldLiENECCh9MUGECbcbjMMGQhttm2Tzp0LfJ2cHN/QZtKkK1/f2irNnx+YGTSXys42\n/8wsfWaZnr4eHT9zfMgAp6Wzxer2PGIiYjRr/CxPgHPx8mlp8fz9AQAAAABcv1B6l0tIAwRIKH2x\nAYSpvj7J6fSGNjt2SJ0jsGRVbq43tCkqkjIzfT9fu3Z4e9Bczdq10gsvjNzzcd3autrM4KbliM/y\nacdaj6mnv8fq9jzGx433zry5KMCZNX6WYiNjrW4PAAAAABDiQuldLiENECCh9MUGMEr09kplZd7Q\nZudOqWcEXpQXFHhDm54e6e67A1/jUn/4g/SRj4x8HQSE23Cr5mzNkAFOzdkaq9vzsMmmaSnThgxw\nJo+bLLvNbnWLAAAAAIAQEErvcglpgAAJpS82gFGqq8sMagZCm7Iyc/ZNOFq1ytyfBmGv80KnKtsq\nhwxw2rvbrW7PIy4yTrPTZpvBzUX73+Sm5SolNsXq9gAAAAAAQRRK73IJaYAACaUvNoAxoqPDXBJt\nILRxOs19bsLFe++Zs3gwKhmGoZbOFnPPm4v2vznaelSVbZW64L5gdYsemQmZ3pk3FwU4M1JnKDoi\n2ur2RoTbcKu1szXoddPi05jRBAAAAMByofQuN9KSqgAAYPgSE6XbbjMPSWpvl7Zt84Y2+/db29/V\nvPSS9KMfWd0FRojNZlNGQoYyEjJ089SbfT7rc/fpZPvJIQOcuvN1Qe+1ydWkJleTdtTs8BmPsEVo\neup0n+XTBgKcrMQs2Wy2oPcaKK2drcp8MvPqFwZY04NNykjICHpdAAAAAAhVhDQAAIwWKSnSxz9u\nHpLU0iJt3eoNbQ4ftrS9QcrKrO4AFom0R2rm+JmaOX6mPjz7wz6fdfR26GjrUc/yaUfbvMuodfR2\nBLXPfqNflW2Vqmyr1OvHXvf5LDE60Te4ef9nblqukmKSgtonAAAAACB8EdIAADBapadLd95pHpLU\n0GAGNgPH8ePW9rd7t2QYUhjPRkDgJUYnanHWYi3OWuwzbhiGGjsavXveXBTgVJ2pUr/RH9Q+O3o7\n5GxwytngHPRZVmKW5qTPGRTgTE+drkg7//MbAAAAAODF/0sEAGCsyMqS1q41D0mqqfHOstm0Saqv\nD24/Z85IGzZICxdKc+ZIU6dKERHB7QFhw2azKSspS1lJWSrKKfL5rLe/VyfOnPAJcAZ+P+06HfRe\nGzoa1NDRoK3VW33Go+xRmjl+5qAZOHPS5ygjPiOsl08DAAAAAFwfQhoAAMaqqVOldevMo7lZygz+\n/hT6zne8v8fGSrm5ZmAzd673yM01998BLiM6ItqcuZI+Z9Bn7d3tOtZ6zBvgXLQPTldfV1D7vOC+\noMMth3W4ZfDSg8kxyZqTPmdQgDM7bbbio+KD2icAAAAAIHgIaQAAgBQTY3UHUne3tH+/eVxq8mRv\naHNxiDNpEsul4YpSYlO0bNIyLZu0zGfcbbhVd67ON7h5f/m06vZqGTKC2ufZnrMqqytTWd3gvZqm\njJtiBjjjc32WUZuaPFURdmafAQAAAEA4I6QB/ORwOORwOAaNu1yu4DcDAIGSlCSlpppLkIWi2lrz\n2LTJdzwhwTe0Gfh99mwpLs6aXhEW7Da7piRP0ZTkKVozY43PZ9193Tredtwb4Fy0jFprV2vQez11\n7pROnTulTVW+f/9jImI0a/ysIQOctPi0oPcJAAAAAPAfIQ3gp+rqapWWllrdBgAEls0mLV4svfmm\n1Z34x+WSnE7zuJjNJuXkDF46bc4cacIEZt/gimIjY5Wfma/8zPxBn7V2tnrCm4t/Hms9pp7+nqD2\n2dPfo4rmClU0Vwz6LC0uzVwy7ZIAZ+b4mYqNjA1qnwAAAACAyyOkAfyUk5OjwsLCQeMul0vl5eUW\ndAQAAXLjjeEX0lyOYUgnTpjHn//s+1ly8tBLp82cKUVHW9MvwkZafJr+Jv5v9DdT/sZnvN/dr1Pn\nTulIy5FBAU7N2Zqg99na1aqdtTu1s/b/s3fvwVGf973HP7u635DE6rqAEAgJgSRjkMxxbHNLbCeA\nc2maNmOm58RJzh9NZ+L2D2fs6aSTZFofT9y0c8bt9I9zplPmtDZN46ROYhqS2MUY27GxxFVC9ws3\nCXRH9+vu+eOpVvpp14DQ7m9Xq/dr5hlpn5/0e76QLIbfR8/3+Z1l3iGHCjMKVZhRaHtNAAAAAAB/\nDq/Xa2/DbSBK1dXVqby83Pe6trZWZWX+P4ELABHr0iXpgQfsW+/hh+fbmEWCmBhp8+bAAY6L1lG4\nf2PTY2rua/bbgdPY26jbk7fDXZ6tup/rVnZKdrjLAAAAALDKRdKzXHbSAAAAo6JC2rNHOn069Gvt\n3SvNtY4cHpaamqTGRqmhYX40NUmTNraPmp2VmpvN+OUvrdeysgK3Ttu0SYrlr1O4s+S4ZO3I26Ed\neTss816vVz1jPWrsbfQLcFr7WzXtmQ5TxQAAAAAAu/BUAQAAzHv+eXtCmuefn/88LU2qrDRjodlZ\n6epVE9gsDnBu3Qp9jQv19prx/vvW+bg4qbjYf+fN1q2mrRpwBw6HQzkpOcpJydGejXss12Y8M+oY\n7LAEOHMhTudwZ5gqBgAAAAAEGyENAACYd/iw9PTT0rFjoVvjyBHp0KG7f11MjNmpsmmTdPCg9drg\noDW4mfu8pUWatnH3wfS0dPmyGYvl5QVunVZQIDmd9tWIFSnWGasta7doy9otOqzDlmvDk8Nq7m8O\nGOCMTI2EqeJ78+GND/Xf1v03ZSdny+FwhLscAAAAAAg7zqQBgiSS+hgCwLL09ZmzaTpD8NP6brd0\n8WLozniZnpba2/0DnPp6qb8/NGsuVWKiVFLi3zqtpERKTQ13dVjBvF6vuka6THCzKMBpH2jXrHc2\n3CX6pCekq8RV4jeK1xYrLSEt3OUBAAAAiHKR9CyXkAYIkkh6YwPAsl26JO3bJw0MBO+emZnmHJqK\niuDdcyl6ewO3Tmtrkzye8NS02IYN/jtvSktNuMWuAyzD1OyU2gbafAHO+Zvn9Vrta+EuK6D81HxL\naDP3+ebMzUqITQh3eQAAAACiQCQ9yyWkAYIkkt7YABAUly5Jn/tccHbUuN3SiRPhC2juZHJSam31\nb53W0CANDYW7OiM1dT64WRjgFBebnTnAEvWM9ijnRznhLmNJnA6nCjMKTWiz1roDZ0P6BjkdtBEE\nAAAAcG8i6VkuZ9IAAIDAKipMa7Jnn5VeW8ZP3B85Ir3ySuhanC1XQoK0fbsZC3m90q1b1l03cwHO\nlSvmul1GRqSaGjMWcjikwkL/1mmlpVJODrtvEFU8Xo/aBtrUNtCmEzphuZYQk6BiV7EvwPF97irh\n/BsAAAAAEY2QBgAAfDKXS3r1VRO0vPyy9O679/69e/dKzz8vHToUuvpCyeGQ8vLM2L/fem1sTGpu\n9m+d1thortnF6zVn8LS3S7/6lfVaRob/zputW6WiIik+3r4aARtMzk6qtrtWtd21ftc4/wYAAABA\nJCOkAQAAd3f4sBm1tdKxY9KZM2ZXx8IzazIzpcpKafdu6emnpQXbhqNOcrK0Y4cZC3k80o0bgVun\n3bhhb42Dg9KHH5qxUEyMCWoCBTiRutsJWIbbk7f1cefH+rjzY79rnH8DAAAAINwIaQAAwL0rL5de\nfNF87vWaNlyTk6ZlWGoq7bWcTmnDBjOeeMJ6bXhYamry33nT1GR+D+0yO2vWbGryv5aV5d82rbTU\ntFSL5a+NWL7ab9VqYGJATX1NltHS36LJWRvfB/+la6RLXSNdOnXllGWe828AAAAA2IV/bQMAgPvj\ncEhpaWbg7tLSzE6jykrr/OysOeMmUOu0W7fsrbG3V3rvPTMWio+XtmzxP/tm61YpPd3eGrGi5aTk\nqCynTI8VPGaZn/XM6trQNb/wpqmvSR2DHfLKxjOgxPk3AAAAAOxDSAMAABBOMTHS5s1mHDxovTYw\nMB/eLAxxWlqkmRn7apyaki5fNmOx/PzArdMKCszOIuAexDhjVJhRqMKMQj1Z9KTl2uTMpNoG2qzh\nTb/5eHPkpu21cv4NAAAAgGAipAEAAIhUmZnSww+bsdD0tNTe7r/zpr7eek6QHbq6zDh50jqflCSV\nlPgHOCUlUkqKvTViRUuITdC27G3alr3N79rQ5JCa+5r9wpumviYNTQ7ZXivn3wAAAABYKkIaAACA\nlSYuzoQdJSXSF74wP+/1mpZlgVqntbVJHo99NY6PSxcumLHYhg3+rdNKSyW3m3ONsCRrEtao0l2p\nSre1jaDX61X3aLea+5s5/wYAAABARCOkAQAAiBYOh5SdbcZj1jM/NDlp2qQtbp3W0CAND9tb57Vr\nZvz2t9b51NTArdOKi6XERHtrxIrmcDiUm5qr3NRczr8BAAAAENEIaQAAAFaDhASprMyMhbxe6eZN\n666buc+vXLG3xpERqbrajIUcDmnTJv8Ap7TUBFI8qMYScP4NAAAAgEhCSAMAALCaORxSfr4ZBw5Y\nr42NSc3N/q3TGhvNNbt4vaZdW1ub9B//Yb2WkRG4dVpRkWkLBywB598AAAAAsBshDQAAAAJLTpZ2\n7DBjIY9Hun49cOu0zk57axwclD780IyFYmNNULO4dVppqbR2rb01Iipw/g0AAACAUCCkAZbo6NGj\nOnr0qN/86Oio/cUAABAOTqdUUGDGk9Z2URoakpqa/NunNTVJU1P21TgzM7/r5xe/sF7Lzg7cOq2w\nUIqJsa9GRAXOvwEAAACwHIQ0wBJ1dHTo1KlTd/9CAABWozVrpKoqMxaanTVn3CxundbQIHV321tj\nT48Zp09b5+PjpeJi/503W7eaX1cUcSW71P3cEn/fvV5zbtD0lBQXL6WmLvk8IFeya2lrrnCcfwMA\nAADgbghpgCUqLCzUvn37/OZHR0dVvfigYwAAYMTESJs3m3HokPVaf//8rpeFIU5rq9kRY5epKamu\nzozF3O7ArdM2bDA7i1YYp8Op7JTsu3/hpUvSsWPSmTPS2bPSwMD8tcxMadcuafdu6cgRqbw8dAVH\nIc6/AQAAACBJDq/Xa+8eeyBK1dXVqXzBw4na2lqVlZWFsSIAAFa46Wmprc1/501DgzUsCKekJBPY\nLG6dVlJizvRZqY4fl374Q//dRneyZ4/0wgv+IRyCJhLPv/kknH8DAACASBZJz3IJaYAgiaQ3NgAA\nUc3rNe3KFoY2c5+3t0seT7grNAoK/HfelJZK+flLbhNmm74+6dvfNrtn7teRI9Irr0iu1dXaLNwi\n7fybO+H8GwAAAIRbJD3LJaQBgiSS3tgAAKxaExNSS4t/67TGRml4ONzVGWlpgVunbdkiJSaGr66L\nF6WDB6XOzuXfy+2WTpyQKiqWfy8sW6Sdf3MnnH8DAAAAO0TSs1xCGiBIIumNDQAAFvF6pa6uwK3T\nrl4Nd3WG0ylt2uTfOm3rVik7O7S7by5elPbvD24bucxM6dQpgpoIF2nn39wJ598AAAAgWCLpWS4h\nDRAkkfTGBgAASzA6KjU3+wc4jY3S+Hi4qzMyM/3bppWWSps3S3Fxy7t3X5/0wAPB2UGzmNttAiBa\nn604nH8DAACAaBZJz3IJaYAgiaQ3NgAACAKPR7p2LXDrtFAEGvcjNlYqKgp89k1m5r3d48iR5Z1B\ncy/3f/XV0N0ftuP8m5XJ4/Wob6zP9nVdyS6CMgAAEHEi6VkuIQ0QJJH0xgYAACE2NGTdcTMX4DQ3\nS1NT4a7OyMkJfPZNYaEUE2O+5vhx6amnQl/Lm29Khw+Hfh2EHeffRK6e0R7l/CjH9nW7n+tWdkq2\n7esCAADcSSQ9y40Ny6oAAADASrZmjfTQQ2YsNDsrdXQEPvump8feGru7zTh92jqfkCAVF5vQ5swZ\ne2p5+WVCmlUiITZB27K3aVv2Nr9rkXb+ze3J2/q482N93Pmx3zXOvwEAAIBdCGkAAACAYImJMe3H\nior8Q4n+/sCt01paTLhjl8lJqbbWDLu8+65Zb8FPqmH1WZOwRpXuSlW6Ky3zkXj+TddIl7pGunTq\nyinLPOffAAAAINgIaQAAAAA7rF0rfepTZiw0NSW1tfnvvGlokAYHw1NrKBw7Jr34YrirQARyOBzK\nTc1VbmquHit4zHIt0s6/8Xg9ahtoU9tAm07ohOUa598AAADgfhDSAAAAAOEUHz9/bsxCXq9pkRao\ndVp7u7m+ktjVWg1RJcYZo8KMQhVmFOrJoict1yLt/JvJ2UnVdteqttt/l9pqO/8GAAAA946QBgAA\nAIhEDoeUk2PG3r3WaxMTpk3a4tZpDQ3SyEh46r2bDz4wbc9KS6XsbPPrA5aB828AAAAQDQhpAAAA\ngJUmMdGc77L4jBevV+rsDNw67dq18NQ6Z2xM2rfPfJ6RIZWUSFu3Wj8WF0vJyeGtE1GB828AAACw\nUhDSAAAAANHC4ZDWrTPjM5+xXhsdlZqaTGBz7pz0138dnholc9bOmTOBW6AVFPgHOFu3Shs2SDEx\n9teKqML5NwAAAIg0hDQAAADAapCSIu3cacbhw+ENae7k6lUz3nrLOp+QYHbaBApw1q4NT62IKtF+\n/k1mYqbtdQIAAODuCGkAAACA1SYtTcrMlAYGwl3JvZuclGprzVjM5fJvnbZ1q7Rliwl3gGWKlvNv\nAAAAEHkIaQAAAIDVxuGQdu2S3n473JUER1+f9MEHZizkdEobN/rvvCkpMS3hnJzpgeW72/k3c4HN\nwnNwwnX+DQAAACIPIQ0AAACwGu3ebW9Ik5BgdsPYyeOR2tvNOGE900PJyaZ9WqAAJz3d3joRlRae\nf7Nn4x7LtUg7/wYAAADhQ0gDAAAArEZPPy299JJ96338sZSbKzU2mtHUNP+xtVWanravFkkaG5Mu\nXDBjsdzcwGffbN4sxcXZWyei0p3Ov5mYmfCdf+NroxbG828AAAAQWoQ0EsBD7QAAIABJREFUAAAA\nwGpUUSHt2SOdPh36tfbuNetJUk6OWXehmRmpoyNwgNPZGfr6Frt1y4zFvzcxMSaoWbzzZutWKS/P\ntJEDlikxNlHbs7dre/Z2v2uRdv7Nvfi/Z/+vHnI/pG3Z27QubZ0cvE8AAAAsHF6vl33UQBDU1dWp\nvLzc97q2tlZlZWVhrAgAAOAujh+XnnrKnnUOHbq/7x0elpqbAwc4IyPBrXM50tL8d9+UlJiRmhru\n6hDlVsr5N2nxaSrNKtW27G0qdZmP27K2qWhtkWKd/AwpAACwTyQ9yyWkAYIkkt7YAAAA9+zIEenY\nsdDe/9VXg39fr1fq6vIPbhobzRk0s7PBX/N+ud3+O29KSqTCQimWB9MIrbnzb87cOKOvvv7VcJcT\nUJwzTsWuYm3LMqHNXHizNWurkuOSw10eAACIQpH0LJd/EQAAAACr2d/9nXTqVGjairnd0iuvBP++\nkmkt5nabceCA9drUlNTWFjjA6e4OTT130tlpxsmT1vm4OKmoKHCAk51N+zQExdz5NylxKeEu5RNN\ne6Z1ueeyLvdc9ru2MX2jL7RZGOC4kl1hqBQAACD4CGkAAACA1czlkk6ckPbtkwYGgnffzExzX1cY\nHqTGx0ulpWYsNjjoH9w0NZkxPm5vndPTUkODGYtlZAQ++2bLFimZnQVYPa7cvqIrt6/oRMsJy3x2\ncrZpnbYguNmWvU0b1mzg3BsAALCiENIAAAAAq11FhdlN87nPBWdHjdttApqKiuXfK9gyMqTdu81Y\nyOORbtwIfPZNR4dpr2anwUHpzBkzFiso8N95s3WrtGGDFBNjb51AmPSM9ajnao9OXz1tmU+JS1Fp\nVqlfgLNl7RbFxcSFqVoAAIBPRkgDAAAAwAQqFy9Kzz4rvfba/d/nyBHT4iwcO2iWw+k0IceGDdLj\nj1uvTUxIra2BA5y+PvtrvXrVjN/+1jqfkCAVFwcOcNautb9OQGbHS89Yj23rjU6PqqarRjVdNZb5\nWGestqzdYmmbNhfmpMan2lYfAADAYoQ0AAAAAAyXS3r1VRO0vPyy9O679/69e/dKzz8vHToUuvrC\nJTFRKiszY7G+vvnQZmGA09IiTU7aW+fkpFRba8ZiLlfgs2+2bDHhDhAidX9Sp1hnrOp761XfU6+G\n3gbzeW+92gfa5ZU9u9RmPDNq6G1QQ2+D/l3/brm2Yc2GgOfeZKdk21IbAABY3Rxer9379oHoVFdX\np/Lyct/r2tpalQX6hzwAAMBKUVsrHTtmWm7V1FjPrMnMlCorTduwp5+WFvw9CJJmZ81ul8U7bxob\npWvXwl3dPKdTKiz033lTUiKtXy9xtkfU6BntUc6Pcmxft/u57k8MO8anx9XU1+QLcOp7TYjT2Neo\nqdkpmyv150pyBTz3piC9QE6HM9zlAQCAZYikZ7mENECQRNIbGwAAIOi8XmlkxOzWSEiQUlN5gH+/\nRkfNTptAAc7t2+Gubl5ysglrAgU46enhrg5LFIkhzSeZ9cyqfbDdF9wsDHGGJodCVOm9S45L1lbX\nVtMyzVXqC3CKXcWKj4kPd3kAAOAeRNKzXNqdAUt09OhRHT161G9+dHTU/mIAAADs4nBIaWlmYHlS\nUqQdO8xYyOuVurv9g5umJnMmzvS0vXWOjUnnz5uxWG5u4LNvNm+W4jicHcsT44zRlrVbtGXtFn1+\n6+d9816vVzdHblpCm7nPu0a6bKtvbHpM526e07mb56x1O2JUtLbIr21aaVap0hL4sxMAAARGSAMs\nUUdHh06dOhXuMgAAABBtHA4TfuTmSnv2WK/NzEjt7YEDnM5O+2u9dcuMxecWxcSYoGZhcDP3eV4e\nu6+wLA6HQ/lp+cpPy9enN33acm1wYtCcd7Po3Ju2gTZ5vB5b6pv1zqqpr0lNfU36eePPLdfWpa0L\neO5NTkqOHLwvAABY1QhpgCUqLCzUvn37/OZHR0dVXV0dhooAAAAQ9WJjpeJiMw4ftl4bHjZhTaAA\nZ2TE3jpnZ6XmZjMWS0vz33mzdav5NaWm2lsnok5GYoYeXv+wHl7/sGV+YmZCzX3NAc+9mZiZsK2+\nG8M3dGP4ht5qe8syn5mYGfDcm43pGxXjjLGtPgAAED6cSQMESST1MQQAAADk9UpdXYHPvmlvN4FK\npFi3LnCAs3GjCaiwbCvpTBo7zHpmdeX2lfm2aQvapw1ODIa7PCXGJgY896bEVaKE2IRwlwcAwIoX\nSc9y+dsuAAAAAEQjh0Nyu804cMB6bWpKamsLHOB0d9tf640bZpw8aZ2Pi5O2bPFvnbZ1q5SVRfs0\n3LcYZ4w2Z27W5szNOlwyvzvN6/Wqe7Q74Lk3N4Zv2FbfxMyELty6oAu3LljmnQ6nNmdutrRNm9uJ\nk56Yblt9AAAgeAhpAAAAAGC1iY+XSkvNWGxwMHDrtKYmaXzc3jqnp6X6ejMWy8gIfPZNcbGUlGRv\nnYgaDodDuam5yk3N1f7C/ZZrQ5NDauht8J19MxfgtPa3atZrz840j9ejlv4WtfS36JdNv7Rcy0/N\nD3juTV5qHufeAAAQwWh3BgRJJG2RAwAAAILO45GuXw8c4HR0mPZqkaKgIHCAU1AgOZ3hri4sPF6P\n+sb6bF/XleyS0xHdv+eTM5Nq6W/xO/emobdB4zM2B5sBpCekm902iwKcTRmbOPcGALBqRdKzXEIa\nIEgi6Y0NAAAA2GpiQmpp8Q9wGhul/v5wVzcvMdG0T1vcOq2kRFq7NtzVRR6vVxoeNu3x4uOltDRa\nzC2Bx+vR1dtXA5570z8e/vdFQkyCSlwlAc+9SYpjNxoAILpF0rNc2p0BAAAAAJYnMVEqLzdjsb6+\nwGffNDebh/92mpiQamvNWCwrK/DZN0VFUsIqOqj90iXp2DHpzBnp7FlpYGD+WmamtGuXtHu3dORI\n4P+94eN0OFWYUajCjEIdLD5oudYz2hPw3JtrQ9dsq29ydlKXui/pUvcly7xDDm3K3OTXNq00q1SZ\nSZm21QcAwGrBThogSCIpfQUAAAAi3uysdPVq4ADnmn0Pqu/K6ZQKCwMHOOvWRc/OkuPHpR/+UDp9\n+t6/Z88e6YUXpEOHQlfXKjMyNRLw3JuW/hbNeGbCXZ5yU3IDnnvjTnNz7g0AYEWJpGe5hDRAkETS\nGxsAAABY0UZHTfu0QAHO7dvhrm5ecrK1ZdrCj2vWhLu6e9PXJ33722b3zP06ckR65RXJ5QpeXbCY\nnp22nHvT0GdCnIbeBo1Oj4a7PKXFpwU892Zz5mbFOmniAgCIPJH0LJeQBgiSSHpjAwAAAFHJ65W6\nu/2Dm6YmqbVVmp4Od4XzcnMDn32zebMUFxfu6oyLF6WDB6XOzuXfy+2WTpyQKiqWfy/cM4/Xo+tD\n1wOee9M71hvu8hQfE6/itcWWlmnbsrZpa9ZWJcclh7s8AMAqFknPcglpgCCJpDc2AAAAsOrMzEjt\n7f4BTmOj1NUV7urmxcaaoCbQDpy8PPvap128KO3fbz1zZrkyM6VTpwhqIkTvWK9f27T6nnpduX0l\n3KXJIYc2Zmy07LqZC3BcyezIAgCEXiQ9yyWkAYIkkt7YAAAAABYYHjahTaAdOCMj4a5u3po1JqxZ\nHOAUF0upqcFbp69PeuCB4OygWcztNgEQrc8i1ujUqJr6mvx23jT3NWvaE/7daNnJ2QHPvVm/Zj3n\n3gAAgiaSnuXSGBQAAAAAEN3S0qTKSjMW8nrNLptAZ9+0t0uzs/bWOTQkVVebsdi6dYHPvikslGJi\nlrbOt78dmoBGMvd99lnp1VdDc38sW0p8inbm79TO/J2W+enZabUNtPmde1PfW6+RKfvCzJ6xHvVc\n6dG7V9611h2XEvDcm6LMIsXFREgLQQAA7gM7aYAgiaT0FQAAAMAyTU1JbW2BA5zu7nBXNy8+Xioq\nChzgZGX5t087flx66qnQ1/Xmm9Lhw6FfByHn9Xp1Y/iG37k3Db0NujV6K9zlKdYZ6zv3ptRVajn/\nJiU+JdzlAQAiVCQ9y2UnDQAAAAAAi8XHS6WlZiw2OBi4dVpTkzQ+bm+dU1NSfb0Zi2Vm+gc3L71k\nT10vv0xIEyUcDofWr1mv9WvW64miJyzX+sf7A5570zHYIa/s+ZngGc+Mb+3FCtIL/NqmlWaVKjsl\n25baAAC4F+ykAYIkktJXAAAAAGHg8UjXrwcOcDo6THu11eTSJWnBv5GweoxPjwc896apr0lTs1Ph\nLk+uJFfAc282pG+Q0+EMd3lB4fF61DfWZ/u6rmRX1PweAohukfQsl500AAAAAAAEg9MpFRSY8fjj\n1msTE1JLi3+A09go9feHp95QO3ZMevHFcFeBMEiKS9KOvB3akbfDMj/jmVH7QLuvXdrCEGdocsi2\n+vrG+/Te1ff03tX3LPPJccna6trqF+BsWbtF8THxttUXDH1jfcr5UY7t63Y/181OJQBYIkIaAAAA\nAABCLTHR7CoJtLOkry/w2TfNzaad2Up15ky4K0CEiXXGqthVrGJXsb6w9Qu+ea/Xq66RroDn3nSN\ndNlW39j0mM7dPKdzN89Z5mMcMdqydoulZdrcx7SENNvqAwBEJ0IaAAAAAADCyeWSHnnEjIVmZ6Wr\nV/133jQ1SdeuhafWpaipMS3eHI5wV4II53A45E5zy53m1mc2f8ZybXBiMOC5N+2D7fJ4PbbUN+ud\nVWNfoxr7GvWG3rBcW79mfcBzb3JScuTg//sAgHtASAMAAAAAQCSKiZE2bTLjc5+zXhsdNe3TFgc4\njY3SkH1to+5oYEAaGZHS2GmA+5eRmKGH1z+sh9c/bJmfmJlQc1+z37k3jb2NmpydtK2+60PXdX3o\nun7b9lvLfGZiZsBzbzZmbOTMFgCABSENAAAAAAArTUqKtGOHGQt5vVJ3t3/rtLo6qbXV/jr/9m+l\nw4elBx+UYnkEgeBJjE1URW6FKnIrLPOznll1DHbMn3uzIMAZnBi0rb6BiQF9cO0DfXDtA7+6A517\nU7y2WAmxCbbVBwCIHPwNCQAAAACAaOFwSLm5ZuzZMz8/NCSlp9tfz/e/b8aaNaae/fvNILRBiMQ4\nY1S0tkhFa4v0VMlTvnmv16tbo7cs59409JkQ58bwDdvqm5iZ0IVbF3Th1gVr3Y4Ybc7crG3Z21Tq\nKp0PcbK3aU3CGtvqAwDYj78RAQAAAAAQ7dLSpMxM04IsHIaGpOPHzZCsoc2BAya0iYkJT21YFRwO\nh/JS85SXmqcDmw5Yrg1NDgU896Z1oNXWc2+a+5vV3N+sX+gXlmvuNHfAc2/yUvM49wYAogAhDQAA\nAAAA0c7hkHbtkt5+O9yVGIFCm717rTttCG1gkzUJa7R73W7tXrfbMj85M6mW/paA596Mz4zbVl/n\ncKc6hzv1drv1/ZuRmKHSrFK/AKcwo9C22gAAy0dIAwAAAADAarB7d+SENIsNDUlvvmmGRGiDiJAQ\nm6CynDKV5ZRZ5j1ej64MXgl47k3/eL9t9Q1ODOrD6x/qw+sfWuuOSdDmzM221QEAWB5CGgAAAAAA\nVoOnn5ZeeincVdybxaFNero1tNmxg9AGYeN0OLUpc5M2ZW7SoeJDvnmv16uesZ6A595cG7pmW32T\ns5Oq7623bT0AwPIQ0gAAAAAAsBpUVJhzYE6fDv1aGzdKRUXSBx9IExPLv9/t29Ivf2mGRGiDiORw\nOJSTkqOclBztK9xnuTYyNRLw3JuW/hbNemfDVDEAIBIQ0gAAAAAAsFo8/7w9Ic0//IN06JA0OSmd\nOSO9844ZdoQ2Bw5IDzxAaIOIkhqfqip3larcVZb5qdkptfa3+p1709DboLHpsTBVCwCwEyENAAAA\nAACrxeHDpu3ZsWOhW+PIERPQSFJCgtm9s2eP9Bd/YV9ok5Fh3WlDaIMIFR8Tr23Z27Qte5u0bX7e\n4/Xo2u1rZvfNogCnd6w3fAUDAILO4fV6veEuAogGdXV1Ki8v972ura1VWVnZHb4DAAAAAMKgr8+E\nFp2dwb+32y1dvCi5XPf29RMT/qHN5GTw6yK0QRTpHesNeO7NldtXwl2a/ten/5f2F+7XjrwdSo5L\nDnc5APCJIulZLiENECSR9MYGAAAAgDu6dEnat08aGAjePTMzpVOnzNk398uu0CYz0z+0cTqDvw5g\no9GpUTX2Naq+p17VndX63x/977DVEuOI0fbs7b4Wb5X5ldqRt0OJsYlhqwkAFoqkZ7mENECQRNIb\nGwAAAADu6tIl6XOfC86OGrdbOnFieQFNIHOhzcmTJrT53e8IbYB70DPao5wf5YS7DItYZ6zKsst8\noU2Vu0oVuRUENwDCIpKe5RLSYEUYGhrSuXPnVF1drerqatXU1KilpUVz//dtb29XYWFhWGuMpDc2\nAAAAANyTvj7p2Wel1167/3scOSK98sq9tzhbjokJ6aOP5nfaENoAAUViSBNIrDNWFTkVvtCmyl2l\n8pxyJcQmhLs0AFEukp7lEtJgRdi5c6fOnz//idcJaQAAAABgGY4fl15+WXr33Xv/nr17peeflw4d\nCl1dd2NnaLNv33xoU1FBaIOItlJCmkDinHF6IPcBX3BT6a5UeU654mPiw10agCgSSc9yY8OyKrBE\nC7PE9PR07dy5Uw0NDbp582YYqwIAAACAKHH4sBm1tdKxY6bFWE2N9cyazEypslLavVt6+mlpwYON\nsElMNOHJvn3S975nQpsPP7SGNlNTy19nYEB64w0zJEIbIISmPdOq6apRTVeN/s/Z/yNJio+J1wO5\nD6gq34Q2Ve4qlWWXKS4mLszVAsDyEdJgRfjGN76h7OxsVVVVacuWLXI4HNq/fz8hDQAAAAAEU3m5\n9OKL5nOvVxoZMTtTEhKk1FTJ4QhvfXeTmDgfnEjS+Lj/TptQhDZr1863RztwwPw+EtpgFUqJS9Ho\n9GjQ7zs1O6XqzmpVd1ZLNWYuISZBO/J2WIKb7dnbFevkcSeAlYU/tbAiPPvss+EuAQAAAABWF4dD\nSkszY6VKSgoc2pw8aUKbDz8MTmjT3+8f2izcaUNog1Wi9dlWDUwMqLqzWjWdNaruqta5rnMhCW4m\nZyd15sYZnblxxjeXGJuoB/MenG+Vll+pbdnbCG4ARDT+hFqlWltbdebMGV2/fl1TU1PKzMxUaWmp\nHnnkESUmJoa7PAAAAAAAgi9QaLOwPVowQ5t//3czJEIbrBpOh1OlWaUqzSrVHz3wR5KkWc+sGvsa\nTWjTWa2arhqdu3lOY9NjQV9/YmZCH17/UB9e/9A3lxSbpAfzHlSVu8oX3JRmlSrGGRP09QHgfhDS\nRIAbN27ozJkz+uijj3TmzBlVV1dreHjYd33jxo3q6OgIylpvvPGG/vIv/1Jnz54NeD01NVXPPPOM\nvve97ykrKysoawIAAAAAEJGSkkx7sgMHzGu7QhuXy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"text/plain": [ - "array([ 1.24759097, 1.47684102, 1.64567719, 1.62750754, 1.91693145,\n", - " 1.75885185])" + "" ] }, - "execution_count": 45, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.divide(res_w['GWT'], res_w['FSWT'])" + "res_w_copy = dict(res_w)\n", + "res_w_copy.pop('HWT')\n", + "\n", + "exp.plot_compression_experiments(res_w_copy, comp_ratios,\n", + " \"../figs/compression_wiki2.png\")\n", + "Image(filename=\"../figs/compression_wiki2.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reconstruction Error: FSWT vs GWT" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " GWT error| FSWT error| Reduction\n", + "-----------------------------------------------\n", + " 1.1236578| 0.9424534| -0.1612630\n", + " 0.5947244| 0.4116692| -0.3077982\n", + " 0.3650694| 0.2490409| -0.3178259\n", + " 0.2259673| 0.1528849| -0.3234200\n", + " 0.1347382| 0.0679602| -0.4956130\n", + " 0.0702006| 0.0388420| -0.4466996\n", + "\n" + ] + } + ], + "source": [ + "reduction = np.divide(res_w['FSWT'], res_w['GWT']) - 1\n", + "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n", + "text += \"-\"*47 + \"\\n\"\n", + "for i in range(len(comp_ratios)):\n", + " text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_w['GWT'][i], res_w['FSWT'][i], reduction[i])\n", + "print(text)" ] }, { @@ -464,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -477,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -496,214 +619,145 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier()]\n", + "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n", "\n", "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n", "\n", - "res_b, time_b = exp.compression_experiment_static(G, F, algs, comp_ratios, 10)" + "res_b, time_b = exp.compression_experiment(G, F, algs, comp_ratios, 10)" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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yZYviJD1odTAmeFBSnKTNmzf79fjHYMaaIhR5ailjJHXy8/MDHJF/+Doid9++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\ny5bpwgsvdLlq9+7dtWzZMj300EOOC2b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IFjF1iOtOnjTUxZOYVIcYR5G+YR5cQ/VmuR3oIRPUYQLggzutdocrNyT0NwSZIkSZJOryDsh1pg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\n7XWIiIiI5wT4+XFNgwbMio3F2r8/Szp2xBwVRVAF177WvDxeOXKEvlu30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Hk2X7lCQX6D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RgcRE0ovxktjP1ZU2maOHbowfusvDo8AtWEVEROSWJUuWMGjQINyxjA5q7uiCSkjWUUKLFy9m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\nF0vdYY54PBwJcneYkw3FtEhIoGXF7aRm2B2mVatWLF++nEsuuYQpU6ZQsnM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rcsXhwf3LQpHj0wM4tjzmcd+Xx05POxs4IPP2qnWNUzXsjTnMlEQzptegFKcuvLL4+5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\ndc3zzz8f733ve2doR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"text/plain": [ "" ] }, - "execution_count": 77, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.plot_compression_experiments(res_b, comp_ratios,\n", - " \"../figs/compression_blog.png\", 4000.)\n", + " \"../figs/compression_blog.png\")\n", "Image(filename=\"../figs/compression_blog.png\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### FSWT x FT" - ] - }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { + "image/png": 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Vmk9Ew6GGaDjUEOt2rUvsUzShqM/ZLfPK5nV9P6d0ThRO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\nnMYNHFetW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"text/plain": [ - "array([ 1.18970511, 1.20880759, 1.52070644, 2.9339968 ,\n", - " 2.56411917, 87.36908629])" + "" ] }, - "execution_count": 78, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.divide(res_b['GWT'], res_b['FSWT'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Average Computation times" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Small Traffic" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FT 0.0120865980784\n", - "FSWT 0.330213073889\n", - "SWT 0.693568921089\n", - "GWT 0.22568975687\n", - "HWT 8.12668774128\n" - ] - } - ], - "source": [ - "for alg in time_smt:\n", - " print(alg, \" \" ,np.mean(time_smt[alg]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Traffic" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FT 35.4420957287\n", - "FSWT 18.375430127\n", - "GWT 5.96227147182\n" - ] - } - ], - "source": [ - "for alg in time_t:\n", - " print(alg, \" \", np.mean(time_t[alg]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Human" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FT 2.81663463513\n", - "FSWT 14.0157053073\n", - "GWT 11.5385679166\n" - ] - } - ], - "source": [ - "for alg in time_h:\n", - " print(alg, \" \", np.mean(time_h[alg]))" + "res_b.pop('HWT')\n", + "exp.plot_compression_experiments(res_b, comp_ratios,\n", + " \"../figs/compression_blog2.png\")\n", + "Image(filename=\"../figs/compression_blog2.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Wikipedia" + "### Reconstruction Error: FSWT vs GWT" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "FT 381.24113421\n", - "FSWT 424.825737035\n", - "GWT 386.143908437\n" + " GWT error| FSWT error| Reduction\n", + "-----------------------------------------------\n", + " 760.4954199| 667.9291131| -0.1217184\n", + " 552.5279946| 483.1301899| -0.1256005\n", + " 408.4589303| 310.3847051| -0.2401079\n", + " 230.1754795| 130.1136140| -0.4347199\n", + " 132.3424893| 27.8478488| -0.7895774\n", + " 55.7708549| 0.1880823| -0.9966276\n", + "\n" ] } ], "source": [ - "for alg in time_w:\n", - " print(alg, \" \",np.mean(time_w[alg]))" + "reduction = np.divide(res_b['FSWT'], res_b['GWT']) - 1\n", + "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n", + "text += \"-\"*47 + \"\\n\"\n", + "for i in range(len(comp_ratios)):\n", + " text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_b['GWT'][i], res_b['FSWT'][i], reduction[i])\n", + "print(text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Blogs" + "## Average Computation Time (in seconds)" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "FT 7.84694833755\n", - "FSWT 38.6995140592\n", - "GWT 47.0106969635\n" + "Transform | Small Traffic Traffic Wikipedia Human Blogs\n", + "HWT | 0.1105079 53.0783952 379.8365982 5.1249488 10.7540500\n", + "FT | 0.0714019 33.3342006 168.3058656 3.8720605 8.3236080\n", + "GWT | 0.5620325 32.1109771 513.3351845 19.0809389 51.8282435\n", + "SWT | 1.5199850 Unavailable Unavailable Unavailable Unavailable\n", + "FSWT | 0.5427060 23.3150091 517.8988918 20.3815869 52.4716242\n", + "\n" ] } ], "source": [ - "for alg in time_b:\n", - " print(alg, \" \", np.mean(time_b[alg]))" + "times = [time_smt, time_t, time_w, time_h, time_b]\n", + "algs = ['HWT', 'FT', 'GWT', 'SWT', 'FSWT']\n", + "transforms = ('Transform', 'Small Traffic', 'Traffic', 'Wikipedia', 'Human', 'Blogs')\n", + "text = \"{:10s}|{:>15s}{:>15s}{:>15s}{:>15s}{:>15s}\".format(*transforms) + \"\\n\"\n", + "for alg in algs:\n", + " text += \"{:10s}|\".format(alg)\n", + " for time in times:\n", + " msg = np.mean(time.get(alg, 0.))\n", + " if msg != 0.:\n", + " text += \"{:>15.7f}\".format(msg)\n", + " else:\n", + " text += \"{:>15s}\".format('Unavailable')\n", + " text += \"\\n\"\n", + "\n", + "print(text)" ] } ], From 7633db43b41b20a8bc070ee952bfa7076491f634 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Tue, 26 Sep 2017 10:04:33 +0200 Subject: [PATCH 54/62] Add lib/__init__.py --- lib/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 lib/__init__.py diff --git a/lib/__init__.py b/lib/__init__.py new file mode 100644 index 0000000..e69de29 From 0198beefe7b213d6f6d195dece3b7f66729cac21 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Tue, 26 Sep 2017 10:34:42 +0200 Subject: [PATCH 55/62] Add comments --- lib/graph_signal_proc.py | 5 + lib/optimal_cut.py | 294 +++++++++++++++++++++------------------ lib/static.py | 2 + 3 files changed, 167 insertions(+), 134 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 6d1346b..d376035 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -299,6 +299,7 @@ def sqrtmi(mat): def set_fiedler_method(method): + # Set method for Fiedler vector computation global _method _method = method @@ -456,6 +457,10 @@ def ratio_cut_hierarchy(G, method='lobpcg'): inserted node. Input: * G: graph + * method: method for Fiedler vector computation. + The default value is 'lobpcg' however 'tracemin_lu' + seems faster and gives determinisic output when used with + PYTHONHASHSEED set to a constant value. Output: * root: tree root * ind: index with unique integers as values diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index e859395..da34cd1 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -66,38 +66,13 @@ def sweep_opt(x, F, G, k, ind): return vec, best_energy, best_cut_size -def fast_cac(G, F, ind): - """ - Computes product C*A*C, where C is the Laplacian of a complete graph - and A is a pairwise squared difference matrix. - Input: - * G: graph - * F: graph signal - * ind: vertex index v: unique integer - Output: - * CAC: matrix product - """ - sorted_F = np.array([F[ind[v]] for v in G.nodes()]) - return -2 * math.pow(len(sorted_F), 2) * np.outer(sorted_F, sorted_F) - - -def power_method(mat, start, maxit): - """ - Power method implementation. - Input: - * mat: matrix - * start: initialization - * maxit: number of iterations - Output: - * vec: largest eigenvector of mat - """ - vec = np.copy(start) - vec = vec / np.linalg.norm(vec) - - for i in range(maxit): - vec = np.dot(vec, mat) - - return vec +############################################################################### +# List of functions used only by the SWT: # +# - spectral_cut() # +# - complete_graph_laplacian # +# - weighted_adjacency_complete # +# - one_d_search # +############################################################################### def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): @@ -131,6 +106,112 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): return res +def complete_graph_laplacian(n): + """ + Laplacian of a complete graph with n vertices. + Input: + * n: size + Output: + * C: Laplacian + """ + C = np.ones((n, n)) + C = -1 * C + D = np.diag(np.ones(n)) + C = (n) * D + C + + return C + + +def weighted_adjacency_complete(G, F, ind): + """ + Computes weighted adjacency complete matrix (w(v)-w(u))^2 + Input: + * G: graph + * F: graph signal + * ind: vertex index vertex: unique integer + Output: + * A: nxn matrix + """ + A = [] + for v in G.nodes(): + A.append([]) + for u in G.nodes(): + A[-1].append(pow(F[ind[v]] - F[ind[u]], 2)) + + return np.array(A) + + +def one_d_search(G, F, k, ind): + """ + Cut computation. Perform 1-D search for beta using golden search. + Input: + * G: graph + * F: graph signal + * k: max edges to be cut + * n: number of chebyshev polynomials + * ind: vertex index vertex: unique integer + Output: + * cut + """ + C = complete_graph_laplacian(nx.number_of_nodes(G)) + A = weighted_adjacency_complete(G, F, ind) + CAC = np.dot(np.dot(C, A), C) + start = np.ones(nx.number_of_nodes(G)) + L = nx.laplacian_matrix(G).todense() + + # Upper and lower bounds for beta + gr = (math.sqrt(5) - 1) / 2 + a = 0. + b = 1000. + c = b - gr * (b - a) + d = a + gr * (b - a) + + # Tolerance + tol = 1. + + resab = {} + resab["size"] = k + 1 + + # golden search + while abs(c - d) > tol or resab["size"] > k: + resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind) + resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) + + if resc["size"] <= k and (resc["energy"] > resd["energy"]): + start = resc["x"] + b = d + d = c + c = b - gr * (b - a) + elif resd["size"] <= k and (resc["energy"] < resd["energy"]): + start = resd["x"] + a = c + c = d + d = a + gr * (b - a) + else: + start = resc["x"] + a = c + c = d + d = a + gr * (b - a) + + resab = spectral_cut(CAC, L, C, A, start, F, G, (b + a) / 2, k, ind) + + return resab + + +############################################################################### +# List of functions used only by the FSWT: # +# - trans() # +# - isqrt() # +# - coef() # +# - chebyshev_approx_2d() # +# - chebyshev_approx_1d() # +# - fast_cac() # +# - power_method() # +# - cheb_spectral_cut() # +# - fast_search() # +############################################################################### + + def trans(L, min_v, max_v): """ Chebyshev polynomial translation. @@ -260,6 +341,40 @@ def chebyshev_approx_1d(n, beta, x, L): return P +def fast_cac(G, F, ind): + """ + Computes product C*A*C, where C is the Laplacian of a complete graph + and A is a pairwise squared difference matrix. + Input: + * G: graph + * F: graph signal + * ind: vertex index v: unique integer + Output: + * CAC: matrix product + """ + sorted_F = np.array([F[ind[v]] for v in G.nodes()]) + return -2 * math.pow(len(sorted_F), 2) * np.outer(sorted_F, sorted_F) + + +def power_method(mat, start, maxit): + """ + Power method implementation. + Input: + * mat: matrix + * start: initialization + * maxit: number of iterations + Output: + * vec: largest eigenvector of mat + """ + vec = np.copy(start) + vec = vec / np.linalg.norm(vec) + + for i in range(maxit): + vec = np.dot(vec, mat) + + return vec + + def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind): """ Fast spectral cut implementation using chebyshev polynomials. @@ -309,118 +424,29 @@ def fast_search(G, F, k, n, ind): return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind) -gr = (math.sqrt(5) - 1) / 2 +# optimal_wavelet_basis() builds the wavelet basis tree. To do so, +# it calls either the SWT (one_d_search()) or the FSWT (fast_search()) -def laplacian_complete(n): - """ - Laplacian of a complete graph with n vertices. - Input: - * n: size - Output: - * C: Laplacian - """ - C = np.ones((n, n)) - C = -1 * C - D = np.diag(np.ones(n)) - C = (n) * D + C - - return C - - -def weighted_adjacency_complete(G, F, ind): - """ - Computes weighted adjacency complete matrix (w(v)-w(u))^2 - Input: - * G: graph - * F: graph signal - * ind: vertex index vertex: unique integer - Output: - * A: nxn matrix - """ - A = [] - for v in G.nodes(): - A.append([]) - for u in G.nodes(): - A[-1].append(pow(F[ind[v]] - F[ind[u]], 2)) - - return np.array(A) - - -def one_d_search(G, F, k, ind): +def optimal_wavelet_basis(G, F, k, npol, method='lobpcg'): """ - Cut computation. Perform 1-D search for beta using golden search. + Computation of optimal graph wavelet basis. Input: * G: graph * F: graph signal * k: max edges to be cut - * n: number of chebyshev polynomials - * ind: vertex index vertex: unique integer + * npol: number of chebyshev polynomials, if 0 run exact version + * method: method used for Fiedler vector computation. + The default value is 'lobpcg' however 'tracemin_lu' + seems faster and gives determinisic output when used with + PYTHONHASHSEED set to a constant value. Output: - * cut - """ - C = laplacian_complete(nx.number_of_nodes(G)) - A = weighted_adjacency_complete(G, F, ind) - CAC = np.dot(np.dot(C, A), C) - start = np.ones(nx.number_of_nodes(G)) - L = nx.laplacian_matrix(G).todense() - - # Upper and lower bounds for beta - gr = (math.sqrt(5) - 1) / 2 - a = 0. - b = 1000. - c = b - gr * (b - a) - d = a + gr * (b - a) - - # Tolerance - tol = 1. - - resab = {} - resab["size"] = k + 1 - - # golden search - while abs(c - d) > tol or resab["size"] > k: - resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind) - resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind) - - if resc["size"] <= k and (resc["energy"] > resd["energy"]): - start = resc["x"] - b = d - d = c - c = b - gr * (b - a) - elif resd["size"] <= k and (resc["energy"] < resd["energy"]): - start = resd["x"] - a = c - c = d - d = a + gr * (b - a) - else: - start = resc["x"] - a = c - c = d - d = a + gr * (b - a) - - resab = spectral_cut(CAC, L, C, A, start, F, G, (b + a) / 2, k, ind) - - return resab - - -def optimal_wavelet_basis(G, F, k, npol, method='lobpcg'): - """ - Computation of optimal graph wavelet basis. - Input: - * G: graph - * F: graph signal - * k: max edges to be cut - * npol: number of chebyshev polynomials, if 0 run exact version - Output: - * root: tree root - * ind: vertex index vertex: unique integer - * size: number of edges cut + * root: tree root + * ind: vertex index vertex: unique integer + * size: number of edges cut """ - global _method - _method = method - gsp.set_fiedler_method(_method) + gsp.set_fiedler_method(method) # Creating index ind = {v: i for i, v in enumerate(G.nodes())} diff --git a/lib/static.py b/lib/static.py index 196a43e..98b3146 100644 --- a/lib/static.py +++ b/lib/static.py @@ -115,6 +115,7 @@ class GRCWavelets(object): """ def __init__(self, method='lobpcg'): + # Method for Fiedler vector computation self.method = method def name(self): @@ -160,6 +161,7 @@ class OptWavelets(object): def __init__(self, n=0, method='lobpcg'): self.n = n + # Method for Fiedler vector computation self.method = method def name(self): From 8fa4d91161a7dfb620d21765d23465622f37191c Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 29 Sep 2017 12:45:01 +0200 Subject: [PATCH 56/62] Rename lib/io to lib/io_utils and make minor changes --- lib/experiments.py | 9 +-- lib/{io.py => io_utils.py} | 3 + notebooks/compression-experiments.ipynb | 74 ++++++++++++------------- notebooks/test.py | 2 - 4 files changed, 45 insertions(+), 43 deletions(-) rename lib/{io.py => io_utils.py} (96%) delete mode 100644 notebooks/test.py diff --git a/lib/experiments.py b/lib/experiments.py index 3b26656..e4ac6cf 100644 --- a/lib/experiments.py +++ b/lib/experiments.py @@ -365,12 +365,13 @@ def compression_experiment(G, F, algs, comp_ratios, num): To run the following DOCTEST set PYTHONHASHSEED=0 - >>> from lib import io, static + >>> from lib import io_utils, static >>> import lib.datasets as data >>> - >>> G = io.read_graph(data.small_traffic["path"] + "traffic.graph", - ... data.small_traffic["path"] + "traffic.data") - >>> F = io.read_values(data.small_traffic["path"] + "traffic.data", G) + >>> G = io_utils.read_graph(data.small_traffic["path"] + "traffic.graph", + ... data.small_traffic["path"] + "traffic.data") + >>> F = io_utils.read_values(data.small_traffic["path"] + + ... "traffic.data", G) >>> algs = [static.OptWavelets(n=5, method='lobpcg'),\ ... static.OptWavelets(method='tracemin_lu'), static.Fourier(),\ ... static.GRCWavelets(method='tracemin_lu'), static.HWavelets()] diff --git a/lib/io.py b/lib/io_utils.py similarity index 96% rename from lib/io.py rename to lib/io_utils.py index 3685efe..3a25d76 100644 --- a/lib/io.py +++ b/lib/io_utils.py @@ -64,9 +64,12 @@ def read_graph(input_graph_name, input_data_name): if v in G: graph_signal[v] = values[v] +<<<<<<< Updated upstream:lib/io.py input_graph.close() nx.set_node_attributes(G, "value", graph_signal) +======= +>>>>>>> Stashed changes:lib/io_utils.py return G diff --git a/notebooks/compression-experiments.ipynb b/notebooks/compression-experiments.ipynb index 6c04be2..68824fa 100644 --- a/notebooks/compression-experiments.ipynb +++ b/notebooks/compression-experiments.ipynb @@ -29,7 +29,7 @@ "\n", "sys.path.append('../')\n", "\n", - "import lib.io as io\n", + "import lib.io_utils as io\n", "import lib.experiments as exp\n", "import lib.static as static\n", "import lib.datasets as data" @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -254,7 +254,7 @@ "" ] }, - "execution_count": 28, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -311,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -324,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -343,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -356,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -366,7 +366,7 @@ "" ] }, - "execution_count": 35, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -389,7 +389,7 @@ "" ] }, - "execution_count": 59, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -412,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -449,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -481,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -494,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -504,7 +504,7 @@ "" ] }, - "execution_count": 39, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -517,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -527,7 +527,7 @@ "" ] }, - "execution_count": 58, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -550,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -600,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -619,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 27, "metadata": { "collapsed": true }, @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -644,7 +644,7 @@ "" ] }, - "execution_count": 44, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -657,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -667,7 +667,7 @@ "" ] }, - "execution_count": 57, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -688,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -725,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 31, "metadata": {}, "outputs": [ { diff --git a/notebooks/test.py b/notebooks/test.py deleted file mode 100644 index e7cc498..0000000 --- a/notebooks/test.py +++ /dev/null @@ -1,2 +0,0 @@ -from lib import io - From 4104303af6ed6ef1f74172694c0cb3834bda094c Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 29 Sep 2017 12:50:17 +0200 Subject: [PATCH 57/62] Add compatibility for NetworkX 2.0 --- lib/graph_signal_proc.py | 13 +++++++------ lib/optimal_cut.py | 13 ++++++++----- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index d376035..6f2f3eb 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -319,11 +319,12 @@ def sweep(x, G): N = nx.number_of_nodes(G) best_val = N - 1 edges_cut = 0 + nodes_list = list(G.nodes()) for i in range(N - 1): - part_one.add(G.nodes()[sorted_x[i]]) + part_one.add(nodes_list[sorted_x[i]]) - for v in G.neighbors(G.nodes()[sorted_x[i]]): + for v in G.neighbors(nodes_list[sorted_x[i]]): if v not in part_one: edges_cut = edges_cut + 1 else: @@ -425,8 +426,8 @@ def rc_recursive(node, G, ind): """ if nx.number_of_nodes(G) < 3: n = Node(None) - n.add_child(Node(ind[G.nodes()[0]])) - n.add_child(Node(ind[G.nodes()[1]])) + n.add_child(Node(ind[list(G.nodes())[0]])) + n.add_child(Node(ind[list(G.nodes())[1]])) node.add_child(n) else: C = ratio_cut(G) @@ -438,7 +439,7 @@ def rc_recursive(node, G, ind): rc_recursive(l, G1, ind) node.add_child(l) else: - l = Node(ind[G1.nodes()[0]]) + l = Node(ind[list(G1.nodes())[0]]) node.add_child(l) if nx.number_of_nodes(G2) > 1: @@ -446,7 +447,7 @@ def rc_recursive(node, G, ind): rc_recursive(r, G2, ind) node.add_child(r) else: - r = Node(ind[G2.nodes()[0]]) + r = Node(ind[list(G2.nodes())[0]]) node.add_child(r) diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index da34cd1..0a0995c 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -34,12 +34,14 @@ def sweep_opt(x, F, G, k, ind): for v in G.nodes(): sum_two += F[ind[v]] + nodes_list = list(G.nodes()) + for i in range(N): - part_one.add(G.nodes()[sorted_x[i]]) - sum_one += F[ind[G.nodes()[sorted_x[i]]]] - sum_two -= F[ind[G.nodes()[sorted_x[i]]]] + part_one.add(nodes_list[sorted_x[i]]) + sum_one += F[ind[nodes_list[sorted_x[i]]]] + sum_two -= F[ind[nodes_list[sorted_x[i]]]] - for v in G.neighbors(G.nodes()[sorted_x[i]]): + for v in G.neighbors(nodes_list[sorted_x[i]]): if v not in part_one: cut_size += 1 else: @@ -487,7 +489,8 @@ def optimal_wavelet_basis(G, F, k, npol, method='lobpcg'): for Gi in (G1, G2): if nx.number_of_nodes(Gi) == 1: - best_cut["parent"].add_child(Node(ind[Gi.nodes()[0]])) + best_cut["parent"].add_child( + Node(ind[list(Gi.nodes())[0]])) elif nx.number_of_nodes(Gi) > 1: n = Node(None) From 4d6bc5fc5b97c690b02f7193a43b3f30975cdd30 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 29 Sep 2017 13:03:47 +0200 Subject: [PATCH 58/62] Update lib/io_utils.py --- lib/io_utils.py | 59 ++++++++++++++++++------------------------------- 1 file changed, 22 insertions(+), 37 deletions(-) diff --git a/lib/io_utils.py b/lib/io_utils.py index 3a25d76..1754acf 100644 --- a/lib/io_utils.py +++ b/lib/io_utils.py @@ -1,11 +1,9 @@ """ This module read graph's info from files with the following format: - input_graph_name has info about edges. -Row format: "vertex_A, vertex_B[, edge_weight]" - +Row format: "node_A, node_B[, edge_weight]" input_data_name has info about the graph signal. -Row format : "vertex_id, vertex_value" +Row format : "node_id, node_value" """ import networkx as nx @@ -17,24 +15,22 @@ def read_graph(input_graph_name, input_data_name): Read graph from file. Input: * input_graph_name has info about edges. - Row format: "vertex_A, vertex_B[, edge_weight]" + Row format: "node_A, node_B[, edge_weight]" * input_data_name has info about the graph signal. - Row format : "vertex_id, vertex_value" + Row format : "node_id, node_value" Output: * networkx graph """ # Reading input data input_data = open(input_data_name, 'r') - values = {} + graph_signal = {} for line in input_data: line = line.rstrip() - vec = line.rsplit(',') - - vertex = vec[0] - value = float(vec[1]) - values[vertex] = value + node, value = line.rsplit(',') + value = float(value) + graph_signal[node] = value input_data.close() @@ -44,32 +40,24 @@ def read_graph(input_graph_name, input_data_name): for line in input_graph: line = line.rstrip() - vec = line.rsplit(',') - v1 = vec[0] - v2 = vec[1] + node_A, node_B = line.rsplit(',')[:2] # Note that the edge weight is always set to 1 # even when provided available in input_graph_name - if v1 in values and v2 in values: - G.add_edge(v1, v2, weight=1.) + if node_A in graph_signal and node_B in graph_signal: + G.add_edge(node_A, node_B, weight=1.) + + input_graph.close() # Extracting largest connected component from graph Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True) G = Gcc[0] - graph_signal = {} - # Setting the graph_signal as node attribute - for v in values.keys(): - if v in G: - graph_signal[v] = values[v] + for node, value in graph_signal.items(): + if node in G: + G.node[node]["value"] = value -<<<<<<< Updated upstream:lib/io.py - input_graph.close() - nx.set_node_attributes(G, "value", graph_signal) - -======= ->>>>>>> Stashed changes:lib/io_utils.py return G @@ -78,7 +66,7 @@ def read_values(input_data_name, G): Read the graph signal from file Input: * input_data_name has info about the graph signal. - Row format : "vertex_id, vertex_value" + Row format : "node_id, node_value" * G: networkx graph Output: * F: normalized node values array, ordered by G.nodes() @@ -89,18 +77,15 @@ def read_values(input_data_name, G): # Reading file for line in input_data: line = line.rstrip() - vec = line.rsplit(',') - - vertex = vec[0] - value = float(vec[1]) - graph_signal[vertex] = value + node, value = line.rsplit(',') + graph_signal[node] = float(value) input_data.close() F = [] - for v in G.nodes(): - if v in graph_signal: - F.append(float(graph_signal[v])) + for node in G.nodes(): + if node in graph_signal: + F.append(graph_signal[node]) else: F.append(0.) From 310f9b1931cde244d46fb0c8ec845a13f24dec5c Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 29 Sep 2017 13:12:40 +0200 Subject: [PATCH 59/62] Add Travis CI --- .travis.yml | 18 ++++++++++++++++++ requirements.txt | 6 ++++++ 2 files changed, 24 insertions(+) create mode 100644 .travis.yml create mode 100644 requirements.txt diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..b6f7e48 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,18 @@ +# After changing this file, check it on: +# http://lint.travis-ci.org + +language: python + +sudo: false + +python: + - "3.4" + +install: + - pip install -U pip + - pip install -U -r requirements.txt + +script: + - tar xvzf data.tar.gz + - export PYTHONHASHSEED=0 + - python -m doctest lib/experiments.py diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b1bf9e2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +matplotlib +# The code also supports NetworkX 2.0 but different versions +# could lead to a test failure due to a slightly different output +networkx==1.11 +numpy +scipy From 2d03f97d038f2b5e250db2acc5d3dbfa18a95254 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 29 Sep 2017 23:19:27 +0200 Subject: [PATCH 60/62] Add info for testing --- README.md | 8 +++++--- lib/experiments.py | 6 +++++- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 3cbfe34..0ddde63 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,10 @@ https://nbviewer.jupyter.org/github/arleilps/sparse-wavelets/blob/master/compres Test: ------ -To run the DOCTEST in lib/experiments.py set PYTHONHASHSEED=0 and then +At the moment there is only one doctest in lib/experiments.py. To run the test +you should use python version 3.4 or 3.5, NetworkX 1.11 and set PYTHONHASHSEED=0. +This conditions are needed to constrain the behaviour of the NetworkX function +fiedler_vector(). Once ready, enter the following command: ``` python -m doctest lib/experiments.py -v ``` @@ -25,7 +28,6 @@ python -m doctest lib/experiments.py -v For more details, see the paper: [Graph Wavelets via Sparse Cuts ](http://arxiv.org/abs/1602.03320 "") Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K Singh, Ananthram Swami -ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016 (to appear). +ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016 (to appear). Arlei Silva (arlei@cs.ucsb.edu) - diff --git a/lib/experiments.py b/lib/experiments.py index e4ac6cf..f0c4d78 100644 --- a/lib/experiments.py +++ b/lib/experiments.py @@ -363,7 +363,11 @@ def compression_experiment(G, F, algs, comp_ratios, num): * results: compression results * times: compression times - To run the following DOCTEST set PYTHONHASHSEED=0 + The following DOCTEST is designed for NetworkX 1.11. Different versions + (ex 2.0) return slightly different Fiedler vectors. Furthermore to get the + same vector you should also set PYTHONHASHSEED=0. 'tracemin_lu' seems to + be faster and give more stable results than 'lobpcg'. The resulting + accuracy of the two method is similar. >>> from lib import io_utils, static >>> import lib.datasets as data From 7d1f94b8a786602a4d9e5113fbae5245203bab06 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Fri, 29 Sep 2017 23:36:34 +0200 Subject: [PATCH 61/62] Add support for Python 2.7 --- README.md | 14 ++++++++++++-- lib/static.py | 2 +- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0ddde63..190a10c 100644 --- a/README.md +++ b/README.md @@ -14,15 +14,25 @@ Compression experiments: ----------------------- https://nbviewer.jupyter.org/github/arleilps/sparse-wavelets/blob/master/compression-experiments.ipynb -Test: +Testing: ------ At the moment there is only one doctest in lib/experiments.py. To run the test you should use python version 3.4 or 3.5, NetworkX 1.11 and set PYTHONHASHSEED=0. -This conditions are needed to constrain the behaviour of the NetworkX function +This conditions are used to constrain the behaviour of the NetworkX function fiedler_vector(). Once ready, enter the following command: ``` python -m doctest lib/experiments.py -v ``` + +List of supported Python versions: +------------------ +

    +
    For more details, see the paper: diff --git a/lib/static.py b/lib/static.py index 98b3146..038b21a 100644 --- a/lib/static.py +++ b/lib/static.py @@ -200,7 +200,7 @@ def drop_frequency(self, wtr, n): # Computing number of integers required to represent the # edges cut (rounded) v = n - int(math.ceil(float(size * - math.log2(len(self.G.edges()))) / 64)) + math.log(len(self.G.edges()), 2)) / 64)) for k in range(v, len(sorted_coeffs)): i = sorted_coeffs[k][0] From d25c5f3f9768d1701f6b013afc19ce2d6f9278b9 Mon Sep 17 00:00:00 2001 From: diego-sacconi Date: Sun, 1 Oct 2017 10:49:51 +0200 Subject: [PATCH 62/62] Fix style and add comments to lib/vis.py --- README.md | 2 +- lib/graph_signal_proc.py | 24 +-- lib/optimal_cut.py | 28 ++- lib/vis.py | 361 ++++++++++++++++++++------------- notebooks/synthetic-data.ipynb | 2 +- 5 files changed, 251 insertions(+), 166 deletions(-) diff --git a/README.md b/README.md index 190a10c..fdd06bf 100644 --- a/README.md +++ b/README.md @@ -38,6 +38,6 @@ List of supported Python versions: For more details, see the paper: [Graph Wavelets via Sparse Cuts ](http://arxiv.org/abs/1602.03320 "") Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K Singh, Ananthram Swami -ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016 (to appear). +ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. Arlei Silva (arlei@cs.ucsb.edu) diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py index 6f2f3eb..954dde8 100644 --- a/lib/graph_signal_proc.py +++ b/lib/graph_signal_proc.py @@ -3,8 +3,7 @@ import networkx as nx import numpy as np -from numpy import dot, diag, sqrt -from numpy.linalg import eigh +from numpy import dot, diag import scipy.optimize from scipy import linalg import scipy.fftpack @@ -283,21 +282,6 @@ def set_counts(tree): return count -def sqrtmi(mat): - """ - Computes the square-root inverse of a matrix. - Input: - * mat: matrix - Output: - * square root inverse - """ - eigvals, eigvecs = eigh(mat) - eigvecs = eigvecs[:, eigvals > 0] - eigvals = eigvals[eigvals > 0] - - return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) - - def set_fiedler_method(method): # Set method for Fiedler vector computation global _method @@ -459,9 +443,9 @@ def ratio_cut_hierarchy(G, method='lobpcg'): Input: * G: graph * method: method for Fiedler vector computation. - The default value is 'lobpcg' however 'tracemin_lu' - seems faster and gives determinisic output when used with - PYTHONHASHSEED set to a constant value. + The default value is 'lobpcg' however 'tracemin_lu' seems + faster and it appears to give more stable results when used + with PYTHONHASHSEED set to a constant value. Output: * root: tree root * ind: index with unique integers as values diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py index 0a0995c..4ca3399 100644 --- a/lib/optimal_cut.py +++ b/lib/optimal_cut.py @@ -2,6 +2,8 @@ import networkx as nx import numpy as np +from numpy import dot, diag, sqrt +from numpy.linalg import eigh import scipy from lib.graph_signal_proc import Node @@ -69,7 +71,8 @@ def sweep_opt(x, F, G, k, ind): ############################################################################### -# List of functions used only by the SWT: # +# List of functions used only by the SWT: # +# - sqrtmi() # # - spectral_cut() # # - complete_graph_laplacian # # - weighted_adjacency_complete # @@ -77,6 +80,21 @@ def sweep_opt(x, F, G, k, ind): ############################################################################### +def sqrtmi(mat): + """ + Computes the square-root inverse of a matrix. + Input: + * mat: matrix + Output: + * square root inverse + """ + eigvals, eigvecs = eigh(mat) + eigvecs = eigvecs[:, eigvals > 0] + eigvals = eigvals[eigvals > 0] + + return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) + + def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): """ Spectral cut implementation. @@ -96,7 +114,7 @@ def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind): - size: number of edges cut - energy: cut energy """ - isqrtCL = gsp.sqrtmi(C + beta * L) + isqrtCL = sqrtmi(C + beta * L) M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 0)) @@ -439,9 +457,9 @@ def optimal_wavelet_basis(G, F, k, npol, method='lobpcg'): * k: max edges to be cut * npol: number of chebyshev polynomials, if 0 run exact version * method: method used for Fiedler vector computation. - The default value is 'lobpcg' however 'tracemin_lu' - seems faster and gives determinisic output when used with - PYTHONHASHSEED set to a constant value. + The default value is 'lobpcg' however 'tracemin_lu' seems + faster and it appears to give more stable results when used + with PYTHONHASHSEED set to a constant value. Output: * root: tree root * ind: vertex index vertex: unique integer diff --git a/lib/vis.py b/lib/vis.py index 9bcd0a8..df29f97 100644 --- a/lib/vis.py +++ b/lib/vis.py @@ -1,37 +1,65 @@ -import sys import os import numpy as np +import networkx as nx +import scipy +import lib.optimal_cut as oc -def set_f(G, F, ids=None): - if ids is None: - i = 0 - for v in G.nodes(): + +def add_signal_to_graph(G, F, identifiers=None): + r""" + Assign the signal to the graph's nodes. + Note that G.node[v] returns a dict. + Input: + * G: networkx graph + * F: graph signal + * identifiers: list of node identifiers. It's used to guarantee that + the graph signal values are assigned to the right nodes + """ + if identifiers is None: + for i, v in enumerate(G.nodes()): G.node[v]["value"] = F[i] - i = i + 1 else: - i = 0 - for v in range(len(ids)): - G.node[ids[v]]["value"] = F[i] - i = i + 1 + for i, v in enumerate(identifiers): + G.node[v]["value"] = F[i] -def get_f(G): +def get_signal_from_graph(G): + r""" + Get the signal along with the list of nodes identifiers + """ F = [] + identifiers = [] for v in G.nodes(): + identifiers.append(v) F.append(G.node[v]["value"]) - return np.array(F) + return np.array(F), identifiers def rgb_to_hex(r, g, b): + r""" + Convert integer numbers to hexadecimal format. + Input range for r, g, b: [0 - 255] + Examples: + black: (0, 0, 0) -> #000000 + white: (255, 255, 255) -> #ffffff + """ return '#%02x%02x%02x' % (r, g, b) def rgb(minimum, maximum, value): - mi, ma = float(minimum), float(maximum) - ratio = 2 * (value - mi) / (ma - mi) + r""" + Map 'value', from the range [minimum - maximum], to an RGB tuple. The + output color scale covers a subset of the RGB spectrum + Examples: + value = maximum -> red : #ff0000 + value = 0.5 * (maximum - minimum) -> green : #00ff00 + value = minimum -> blue : #0000ff + """ + minimum, maximum = float(minimum), float(maximum) + ratio = 2 * (value - minimum) / (maximum - minimum) b = int(max(0, 255 * (1 - ratio))) r = int(max(0, 255 * (ratio - 1))) g = 255 - b - r @@ -39,88 +67,161 @@ def rgb(minimum, maximum, value): return rgb_to_hex(r, g, b) -def draw_graph_with_values(G, dot_output_file_name, maximum=None, - minimum=None): +def quote(s): + return '"' + str(s) + '"' + + +def graph_to_dot(G, dot_output_file_name, nodes_color=(0, 255, 0)): + r""" + Write the graph description in .dot format to a file for visualization + The graph doesn't need to have an attached signal + Input: + * G : networkx graph + * dot_output_file_name : name of the output file (.dot format) + * nodes_color : tuple of RGB coordinates (default is green) + """ + output_file = open(dot_output_file_name, 'w') + output_file.write('graph G{\n') + output_file.write('rankdir="LR";\n') + output_file.write('size=\"10,2\";\n') + + for v in G.nodes(): + color = rgb_to_hex(*nodes_color) + output_file.write(quote(v) + '[shape=circle, label="", ' + 'style=filled, fillcolor=' + quote(color) + ', ' + 'penwidth=2, fixedsize=true, width=1, height=1]; \n') + + for edge in G.edges(): + output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) + + ' [penwidth=1];\n') + + output_file.write("}") + + output_file.close() + + +def graph_with_values_to_dot(G, dot_output_file_name, maximum=None, + minimum=None, draw_zero_valued_nodes=False): + r""" + Write the graph description in .dot format to a file for visualization + The graph needs to have an attached signal + Input: + * G : networkx graph with values (attached graph signal) + * dot_output_file_name : name of the output file (.dot format) + * minimum, maximum : are passed as arguments to the rgb() function + * draw_zero_valued_nodes : if False, set penwidth = 0 for nodes + with value 0 + """ + output_file = open(dot_output_file_name, 'w') output_file.write("graph G{\n") - output_file.write("rankdir=\"LR\";\n") + output_file.write("rankdir=LR;\n") output_file.write("size=\"10,2\";\n") if maximum is None: - maximum = -sys.float_info.max - minimum = sys.float_info.max - - for v in G.nodes(): - if G.node[v]["value"] > maximum: - maximum = G.node[v]["value"] - - if G.node[v]["value"] < minimum: - minimum = G.node[v]["value"] + maximum = max([G.node[v]["value"] for v in list(G.nodes())]) + if minimum is None: + minimum = min([G.node[v]["value"] for v in list(G.nodes())]) for v in G.nodes(): color = rgb(minimum, maximum, G.node[v]["value"]) - if G.node[v]["value"] != 0.0: - output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + - str(color) + "\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n") + + msg = (quote(v) + ' [shape=circle, label="", ' + + 'style=filled, fillcolor=' + quote(color) + ', ' + + 'penwidth={}, fixedsize=true, ' + + 'width=1, height=1]; \n') + + if G.node[v]["value"] != 0.0 or draw_zero_valued_nodes: + output_file.write(msg.format(2)) else: - output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( - color) + "\",penwidth=\"0\",fixedsize=true,width=\"1\",height=\"1\"];\n") + output_file.write(msg.format(0)) for edge in G.edges(): - output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + - "\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") + output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) + ' ' + + '[penwidth=1];\n') output_file.write("}") output_file.close() -def draw_graph_dynamic_values(G, FT, fig_output_file_name): - maximum = -sys.float_info.max - minimum = sys.float_info.max +def dyn_graph_with_values_to_svg(G, FT, fig_output_file_name, + path_to_svg_stack, fixed_color_scale=False, + maximum=None, minimum=None, + draw_zero_valued_nodes=False): + r""" + Create an .svg file of a dynamic graph. It stacks the various + graph snapshots vertically. The graph signal change with time + Input: + * G : networkx graph + * FT : temporal graph signal + * fig_output_file_name : name of the output file (.svg format) + * path_to_svg_stack : path to svg_stack.py, clone it from + https://github.com/astraw/svg_stack + * fixed_color_scale : if True the same maximum and minimum are used + for every intermediate figure + * minimum, maximum : are passed as arguments to the rgb() function. + Not used if fixed_color_scale is set to False + * draw_zero_valued_nodes : if False, set penwidth = 0 for nodes + with value 0 + """ + + if fixed_color_scale: + if maximum is None: + maximum = max([max(FT[i]) for i in range(FT.shape[0])]) + if minimum is None: + minimum = min([min(FT[i]) for i in range(FT.shape[0])]) + + svg_names = '' for i in range(FT.shape[0]): - for j in range(FT.shape[1]): - if FT[i][j] > maximum: - maximum = FT[i][j] + add_signal_to_graph(G, FT[i]) - if FT[i][j] < minimum: - minimum = FT[i][j] + dot_file_name = "dyn_graph-" + str(i) + ".dot" + svg_file_name = "dyn_graph-" + str(i) + ".svg" - svg_names = "" + graph_with_values_to_dot(G, dot_file_name, maximum, minimum, + draw_zero_valued_nodes) - for i in range(FT.shape[0]): - set_f(G, FT[i]) - # draw_graph_with_values(G, "dyn_graph-"+str(i)+".dot", maximum, minimum) - draw_graph_with_values(G, "dyn_graph-" + str(i) + ".dot") - os.system("sfdp -Goverlap=prism -Tsvg dyn_graph-" + - str(i) + ".dot > dyn_graph-" + str(i) + ".svg") - os.system("rm dyn_graph-" + str(i) + ".dot") - svg_names = svg_names + " dyn_graph-" + str(i) + ".svg" - - os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 " + + # Use Scalable Force Directed Placement (sfdp), a fast multilevel force + # directed algorithm to layout very large graphs with high quality. + # It is available as part of the graphviz software. + os.system('sfdp - Goverlap=prism - Tsvg ' + dot_file_name + ' > ' + + svg_file_name + ' ; rm ' + dot_file_name) + svg_names += ' ' + svg_file_name + + # Stack all the previously created svg images vertically with no margin + # in between the figures + os.system("python " + path_to_svg_stack + " --direction=v --margin=0 " + svg_names + " > " + fig_output_file_name) for i in range(FT.shape[0]): os.system("rm dyn_graph-" + str(i) + ".svg") -def draw_partitions_with_values(G, partitions, dot_output_file_name, maximum=None, minimum=None): +def partitions_with_values_to_dot(G, partitions, dot_output_file_name, + maximum=None, minimum=None, + draw_zero_valued_nodes=False): + r""" + Create a .DOT file of a graph. The nodes within the same partition + are connected by edges with thicker penwidth (4 vs 1). + The graph doesn't need to have an attached signal + Input: + * G : networkx graph with values (attached graph signal) + * dot_output_file_name : name of the output file (.dot format) + * minimum, maximum : are passed as arguments to the rgb() function + * draw_zero_valued_nodes : if False, set penwidth = 0 for nodes + with value 0 + """ output_file = open(dot_output_file_name, 'w') - output_file.write("graph G{\n") - output_file.write("rankdir=\"LR\";\n") - output_file.write("size=\"10,2\";\n") + output_file.write('graph G{\n') + output_file.write('rankdir=LR;\n') + output_file.write('size="10,2";\n') if maximum is None: - maximum = -sys.float_info.max - minimum = sys.float_info.max - - for v in G.nodes(): - if G.node[v]["value"] > maximum: - maximum = G.node[v]["value"] - - if G.node[v]["value"] < minimum: - minimum = G.node[v]["value"] + maximum = max([G.node[v]["value"] for v in G.nodes]) + if minimum is None: + minimum = min([G.node[v]["value"] for v in G.nodes]) part_map = {} @@ -130,75 +231,60 @@ def draw_partitions_with_values(G, partitions, dot_output_file_name, maximum=Non for v in G.nodes(): color = rgb(minimum, maximum, G.node[v]["value"]) - if G.node[v]["value"] != 0.0: - output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( - color) + "\",penwidth=\"2\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n") + msg = (quote(v) + '[shape=circle, label="", style=filled, fillcolor=' + + quote(color) + ', penwidth={}, fixedsize=true, width=0.5, ' + 'height=0.5]; \n') + if G.node[v]["value"] != 0.0 or draw_zero_valued_nodes: + output_file.write(msg.format(2)) else: - output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( - color) + "\",penwidth=\"0\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n") + output_file.write(msg.format(0)) for edge in G.edges(): if part_map[edge[0]] == part_map[edge[1]]: - output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + - "\"[dir=\"none\",color=\"black\",penwidth=\"4\"];\n") + output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) + + ' [penwidth=4];\n') else: - output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + - "\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") - - output_file.write("}") - - output_file.close() - - -def draw_graph(G, dot_output_file_name): - output_file = open(dot_output_file_name, 'w') - output_file.write("graph G{\n") - output_file.write("rankdir=\"LR\";\n") - output_file.write("size=\"10,2\";\n") - - for v in G.nodes(): - color = rgb_to_hex(0, 255, 0) - output_file.write("\"" + str(v) + "\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\"" + str( - color) + "\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n") - - for edge in G.edges(): - output_file.write("\"" + str(edge[0]) + "\" -- \"" + str(edge[1]) + - "\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n") + output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) + + ' [penwidth=1];\n') output_file.write("}") output_file.close() -def draw_time_graph(G, fig_output_file_name): - svg_names = "" - - for i in range(G.num_snaps()): - draw_graph(G.snap(i), "graph-" + str(i) + ".dot") - os.system("sfdp -Goverlap=prism -Tsvg graph-" + str(i) + ".dot > graph-" + str(i) + ".svg") - os.system("rm graph-" + str(i) + ".dot") - svg_names = svg_names + " graph-" + str(i) + ".svg" - - os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 " + - svg_names + " > " + fig_output_file_name) - - for i in range(G.num_snaps()): - os.system("rm graph-" + str(i) + ".svg") - +def time_graph_to_svg(G, fig_output_file_name, path_to_svg_stack, + nodes_color=(0, 255, 0)): + r""" + Create an .svg file of a dynamic graph. It stacks the various + graph snapshots vertically. The edges change with time + Input: + * G : the graph has an associated list of snapshots (G.snap). + Each of them is a networkx graph. They all have the same + number of nodes and do not need to have an attached signal. + The associated edges can vary + * fig_output_file_name : name of the output file (.svg format) + * path_to_svg_stack : path to svg_stack.py, clone it from + https://github.com/astraw/svg_stack + * nodes_color : tuple of RGB coordinates (default is green) + """ -def draw_time_graph_eig(G, eig, fig_output_file_name): - svg_names = "" - G.set_values(eig) - maximum = np.max(eig) - minimum = np.min(eig) + svg_names = '' for i in range(G.num_snaps()): - draw_graph_with_values(G.snap(i), "graph-" + str(i) + ".dot", minimum, maximum) - os.system("sfdp -Goverlap=prism -Tsvg graph-" + str(i) + ".dot > graph-" + str(i) + ".svg") - os.system("rm graph-" + str(i) + ".dot") - svg_names = svg_names + " graph-" + str(i) + ".svg" - - os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 " + + snap_dot_file_name = "graph-" + str(i) + ".dot" + snap_svg_file_name = "graph-" + str(i) + ".svg" + graph_to_dot(G.snap(i), snap_dot_file_name, nodes_color) + + # Use Scalable Force Directed Placement (sfdp), a fast multilevel force + # directed algorithm to layout very large graphs with high quality. + # It is available as part of the graphviz software. + os.system("sfdp -Goverlap=prism -Tsvg " + snap_dot_file_name + + "> " + snap_svg_file_name + " ; rm " + snap_dot_file_name) + svg_names += " " + snap_svg_file_name + + # Stack all the previously created svg images vertically with no margin + # in between the figures + os.system("python " + path_to_svg_stack + " --direction=v --margin=0 " + svg_names + " > " + fig_output_file_name) for i in range(G.num_snaps()): @@ -207,32 +293,29 @@ def draw_time_graph_eig(G, eig, fig_output_file_name): def eig_vis_opt(G, F, beta): """ - Computes first and second eigenvector of sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization. - Input: - * G: graph - * F: graph signal - * beta: regularization parameter - Output: - * v1: first eigenvector - * v2: second eigenvector + Computes first and second eigenvector of sqrt(C+beta*L)^T CAC + sqrt(C+beta*L) matrix for visualization. + Input: + * G: graph + * F: graph signal + * beta: regularization parameter + Output: + * v1: first eigenvector + * v2: second eigenvector """ - ind = {} - i = 0 - for v in G.nodes(): - ind[v] = i - i = i + 1 + ind = {v: i for i, v in enumerate(G.nodes())} - C = laplacian_complete(nx.number_of_nodes(G)) - A = weighted_adjacency_complete(G, F, ind) + C = oc.complete_graph_laplacian(nx.number_of_nodes(G)) + A = oc.weighted_adjacency_complete(G, F, ind) CAC = np.dot(np.dot(C, A), C) L = nx.laplacian_matrix(G).todense() - isqrtCL = sqrtmi(C + beta * L) + isqrtCL = oc.sqrtmi(C + beta * L) M = np.dot(np.dot(isqrtCL, CAC), isqrtCL) (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 1)) - x1 = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] - x2 = np.asarray(np.dot(eigvecs[:, 1], isqrtCL))[0, :] + v1 = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :] + v2 = np.asarray(np.dot(eigvecs[:, 1], isqrtCL))[0, :] - return x1, x2 + return v1, v2 diff --git a/notebooks/synthetic-data.ipynb b/notebooks/synthetic-data.ipynb index 15c5f32..becd603 100644 --- a/notebooks/synthetic-data.ipynb +++ b/notebooks/synthetic-data.ipynb @@ -50,7 +50,7 @@ "source": [ "sizes = range(200, 1001, 200)\n", "\n", - "num = 1\n", + "num = 10\n", "balance = 1.\n", "sparsity = 0.8\n", "noise = .1\n",

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