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[Comments] Add comments for the PathUpToH kernel.
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gklearn/kernels/path_up_to_h.py

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class PathUpToH(GraphKernel): # @todo: add function for k_func is None
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def __init__(self, **kwargs):
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"""Initialize the kernel.
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Parameters
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----------
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node_labels : list of strings, optional
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The name list of node labels in the graphs. The default is [].
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edge_labels : list of strings, optional
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The name list of edge labels in the graphs. The default is [].
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depth : int, optional
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The maximum depth of paths to be considered. The default is 10.
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k_func : string, optional
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The kernel function applied using different notions of fingerprint
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similarity. The default is 'MinMax'.
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compute_method : string, optional
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The method to compute the kernel. The default is 'trie'.
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ds_infos : dict, optional
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The dataset information. The default is {}.
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save_paths : boolean, optional
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Whether to save the paths of graphs. The default is True.
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parallel : boolean, optional
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Whether to use parallelization. The default is True.
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n_jobs : int, optional
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The number of jobs to run in parallel. The default is 1.
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chunksize : int, optional
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The chunksize for parallelization. The default is 100.
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normalize : boolean, optional
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Whether to normalize the kernel matrix. The default is False.
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copy_graphs : boolean, optional
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Whether to copy the input graphs. The default is True.
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verbose : int, optional
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The verbosity level. The default is 0.
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"""
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GraphKernel.__init__(
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self, **{
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k: kwargs.get(k) for k in

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