compute_dist

hyppo.tools.compute_dist(x, y, z=None, metric='euclidean', workers=1, **kwargs)

Distance matrices for the inputs.

Parameters

• x,y,z (ndarray of float) -- Input data matrices. x, y and z must have the same number of samples. That is, the shapes must be (n, p), (n, q) and (n, r) where n is the number of samples and p, q, and r are the number of dimensions. Alternatively, x, y and z can be distance matrices where the shapes must be (n, n). z is an optional ndarray.

• metric (str, callable, or None, default: "euclidean") -- A function that computes the distance among the samples within each data matrix. Valid strings for metric are, as defined in sklearn.metrics.pairwise_distances,

  • From scikit-learn: ["euclidean", "cityblock", "cosine", "l1", "l2", "manhattan"] See the documentation for scipy.spatial.distance for details on these metrics.

  • From scipy.spatial.distance: ["braycurtis", "canberra", "chebyshev", "correlation", "dice", "hamming", "jaccard", "kulsinski", "mahalanobis", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "yule"] See the documentation for scipy.spatial.distance for details on these metrics.

  Set to None or "precomputed" if x, y, and z are already distance matrices. To call a custom function, either create the distance matrix before-hand or create a function of the form metric(x, **kwargs) where x is the data matrix for which pairwise distances are calculated and **kwargs are extra arguements to send to your custom function.

• workers (int, default: 1) -- The number of cores to parallelize the p-value computation over. Supply -1 to use all cores available to the Process.

• **kwargs -- Arbitrary keyword arguments provided to sklearn.metrics.pairwise_distances or a custom distance function.

Returns

distx, disty, distz (ndarray of float) -- Distance matrices based on the metric provided by the user. distz is only returned if z is provided.

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