compute_dist

hyppo.tools.compute_dist(x, y, metric='euclidean', workers=1, **kwargs)

Distance matrices for the inputs.

Parameters

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

• 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 and y 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 (ndarray) -- Distance matrices based on the metric provided by the user.