AbstractFloat

Author: YoungFaithful

examples/workflow_introduction.jl

@@ -14,7 +14,7 @@ ts_input_data = load_timeseries_data(data_path; T=24, years=[2016])
 
 #= ClustData
 How the struct is setup:
-    ClustData{region::String,K::Int,T::Int,data::Dict{String,Array},weights::Array{Float},mean::Dict{String,Array},sdv::Dict{String,Array}} <: TSData
+    ClustData{region::String,K::Int,T::Int,data::Dict{String,Array},weights::Array{AbstractFloat},mean::Dict{String,Array},sdv::Dict{String,Array}} <: TSData
 -region: specifies region data belongs to
 -K: number of periods
 -T: time steps per period

src/clustering/attribute_weighting.jl

@@ -1,10 +1,10 @@
 """
-function attribute_weighting(data::ClustData,attribute_weights::Dict{String,Float})
+function attribute_weighting(data::ClustData,attribute_weights::Dict{String,AbstractFloat})
 
 apply the different attribute weights based on the dictionary entry for each tech or exact name
 """
 function attribute_weighting(data::ClustData,
-                              attribute_weights::Dict{String,Float}
+                              attribute_weights::Dict{String,AbstractFloat}
                               )
   for name in keys(data.data)
     tech=split(name,"-")[1]

src/clustering/exact_kmedoids.jl

@@ -2,15 +2,15 @@
 
 "Holds results of kmedoids run"
 mutable struct kmedoidsResult
-    medoids::Array{Float}
+    medoids::Array{AbstractFloat}
     assignments::Array{Int}
-    totalcost::Float
+    totalcost::AbstractFloat
 end
 
 
 """
     kmedoids_exact(
-     data::Array{Float},
+     data::Array{AbstractFloat},
      nclust::Int,
      _dist::SemiMetric = SqEuclidean(),
      env::Any;
@@ -21,7 +21,7 @@ Performs the exact kmedoids algorithm as in Kotzur et al, 2017
 optimizer=Gurobi.Optimizer
 """
 function kmedoids_exact(
-   data::Array{Float},
+   data::Array{AbstractFloat},
    nclust::Int,
    optimizer::DataType;
    _dist::SemiMetric = SqEuclidean(),

src/clustering/extreme_vals.jl

@@ -67,7 +67,7 @@ function simple_extr_val_ident(data::ClustData,
 end
 
 """
-    simple_extr_val_ident(data::Array{Float};extremum="max",peak_def="absolute")
+    simple_extr_val_ident(data::Array{AbstractFloat};extremum="max",peak_def="absolute")
 identifies a single simple extreme period from the data and returns column index of extreme period
 - `data_type`: any attribute from the attributes contained within *data*
 - `extremum`: "min" or "max"

src/clustering/intraperiod_segmentation.jl

@@ -28,10 +28,10 @@ function intraperiod_segmentation(data_merged::ClustDataMerged;
 end
 
 """
-      run_clust_segmentation(period::Array{Float,2};n_seg::Int=24,iterations::Int=300,norm_scope::String="full")
+      run_clust_segmentation(period::Array{AbstractFloat,2};n_seg::Int=24,iterations::Int=300,norm_scope::String="full")
 !!! Not yet proven implementation of segmentation introduced by Bahl et al. 2018
 """
-function run_clust_segmentation(period::Array{Float,2};
+function run_clust_segmentation(period::Array{AbstractFloat,2};
                 n_seg::Int=24,
                 iterations::Int=300,
                 norm_scope::String="full")

src/clustering/run_clust.jl

@@ -1,6 +1,6 @@
 
 """
-    run_clust(data::ClustData;norm_op::String="zscore",norm_scope::String="full",method::String="kmeans",representation::String="centroid",n_clust::Int=5,n_init::Int=100,iterations::Int=300,save::String="",attribute_weights::Dict{String,Float}=Dict{String,Float}(),get_all_clust_results::Bool=false,kwargs...)
+    run_clust(data::ClustData;norm_op::String="zscore",norm_scope::String="full",method::String="kmeans",representation::String="centroid",n_clust::Int=5,n_init::Int=100,iterations::Int=300,save::String="",attribute_weights::Dict{String,AbstractFloat}=Dict{String,AbstractFloat}(),get_all_clust_results::Bool=false,kwargs...)
 norm_op: "zscore", "01"(not implemented yet)
 norm_scope: "full","sequence","hourly"
 method: "kmeans","kmedoids","kmedoids_exact","hierarchical"
@@ -15,7 +15,7 @@ function run_clust(data::ClustData;
       n_seg::Int=data.T,
       n_init::Int=100,
       iterations::Int=300,
-      attribute_weights::Dict{String,Float}=Dict{String,Float}(),
+      attribute_weights::Dict{String,AbstractFloat}=Dict{String,AbstractFloat}(),
       save::String="",#QUESTION dead?
       get_all_clust_results::Bool=false,
       kwargs...
@@ -78,10 +78,10 @@ function run_clust(data_norm_merged::ClustDataMerged,
                   orig_k_ids::Array{Int,1}=Array{Int,1}(),
                   kwargs...)
     # initialize data arrays
-    centers = Array{Array{Float},1}(undef,n_init)
+    centers = Array{Array{AbstractFloat},1}(undef,n_init)
     clustids = Array{Array{Int,1},1}(undef,n_init)
-    weights = Array{Array{Float},1}(undef,n_init)
-    cost = Array{Float,1}(undef,n_init)
+    weights = Array{Array{AbstractFloat},1}(undef,n_init)
+    cost = Array{AbstractFloat,1}(undef,n_init)
     iter = Array{Int,1}(undef,n_init)
 
     # clustering
@@ -346,7 +346,7 @@ end
 Helper function to run run_clust_hierarchical_centroids and run_clust_hierarchical_medoid
 """
 function run_clust_hierarchical(
-    data::Array{Float,2},
+    data::Array{AbstractFloat,2},
     n_clust::Int,
     iterations::Int;
     _dist::SemiMetric = SqEuclidean()

src/clustering/shape_based/cluster_gen_dbaclust_parallel.jl

@@ -64,7 +64,7 @@ writetable(joinpath("outfiles",string("parameters_dtw_",region,".txt")),df)
 
  # Function that can be an input to pmap
 
- @everywhere function dbac_par_sc(n_clust::Int,i::Int,rad_sc::Int,seq::Array{Float,2},n_init::Int,iterations::Int,inner_iterations::Int) # function to use with pmap to parallelize sc band calculation
+ @everywhere function dbac_par_sc(n_clust::Int,i::Int,rad_sc::Int,seq::Array{AbstractFloat,2},n_init::Int,iterations::Int,inner_iterations::Int) # function to use with pmap to parallelize sc band calculation
 
   rmin,rmax=sakoe_chiba_band(rad_sc,24)
 

src/utils/load_data.jl

@@ -81,7 +81,7 @@ function add_timeseries_data!(dt::Dict{String,Array},
             else
                 K=K_calc
             end
-            dt[data_name*"-"*string(column[1])]=Float.(column[2][1:(Int(T*K))])
+            dt[data_name*"-"*string(column[1])]=AbstractFloat.(column[2][1:(Int(T*K))])
         end
     end
     return K

src/utils/utils.jl

@@ -212,7 +212,7 @@ function calc_medoids(data::Array,assignments::Array)
   K=maximum(assignments) #number of clusters
   n_per_period=size(data,1)
   n_periods =size(data,2)
-  SSE=Float[]
+  SSE=AbstractFloat[]
   for i=1:K
     push!(SSE,Inf)
   end
@@ -310,7 +310,7 @@ function set_clust_config(;kwargs...)
 end
 
 """
-    run_pure_clust(data::ClustData; norm_op::String="zscore", norm_scope::String="full", method::String="kmeans", representation::String="centroid", n_clust_1::Int=5, n_clust_2::Int=3, n_seg::Int=data.T, n_init::Int=100, iterations::Int=300, attribute_weights::Dict{String,Float}=Dict{String,Float}(), clust::Array{String,1}=Array{String,1}(), get_all_clust_results::Bool=false, kwargs...)
+    run_pure_clust(data::ClustData; norm_op::String="zscore", norm_scope::String="full", method::String="kmeans", representation::String="centroid", n_clust_1::Int=5, n_clust_2::Int=3, n_seg::Int=data.T, n_init::Int=100, iterations::Int=300, attribute_weights::Dict{String,AbstractFloat}=Dict{String,AbstractFloat}(), clust::Array{String,1}=Array{String,1}(), get_all_clust_results::Bool=false, kwargs...)
 Replace the original timeseries of the attributes in clust with their clustered value
 """
 function run_pure_clust(data::ClustData;
@@ -322,7 +322,7 @@ function run_pure_clust(data::ClustData;
                             n_seg::Int=data.T,
                             n_init::Int=100,
                             iterations::Int=300,
-                            attribute_weights::Dict{String,Float}=Dict{String,Float}(),
+                            attribute_weights::Dict{String,AbstractFloat}=Dict{String,AbstractFloat}(),
                             clust::Array{String,1}=Array{String,1}(),
                             get_all_clust_results::Bool=false,
                             kwargs...)