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# A small helper class to house functions needed by KeplerMapper.visualize
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import numpy as np
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import scipy.sparse
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from sklearn import preprocessing
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import json
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from collections import defaultdict
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        if X_names.shape[0] == 0:
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            X_names = np.array(["f_%s" % (i) for i in range(X.shape[1])])
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        # be explicit about the allowed sparse formats
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        if scipy.sparse.issparse(X):
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            if X.format not in ["csr", "csc"]:
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                raise ValueError(
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                    f"sparse matrix format must be csr or csc but found {X.format}")
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        # wrap cluster_X_mean, X_mean, and X_std in np.array(---).squeeze()
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        # to get the same treatment for dense and sparse arrays
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        cluster_X_mean = np.array(
Files Coverage
kmapper 78.65%
Project Totals (10 files) 78.65%
437.4
3.6=.6
TRAVIS_OS_NAME=linux
437.5
3.7=.7
TRAVIS_OS_NAME=linux

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