compute_dist¶

hyppo.tools.compute_dist(*args, metric='euclidean', workers=1, **kwargs)¶

Distance matrices for the input matrices.

Parameters

*args (ndarray of float) -- Variable length input data matrices. The shapes must be (n, p), (n, q), etc., where n is the number of samples and p and q are the number of dimensions.
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 matrices 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

dist_matrices (tuple of ndarray of float) -- Distance matrices based on the metric provided by the user. One matrix is returned for each input matrix, in the same order.

multi_compute_kern

perm_test