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36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
# coding=utf-8
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from org.meteoinfo.math.spatial.distance import DistanceUtil
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from ..core import numeric as np
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__all__ = [
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'pdist'
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]
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def pdist(X, metric='euclidean', **kwargs):
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"""
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Pairwise distances between observations in n-dimensional space.
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:param X: (*array*) An m by n array of m original observations in an n-dimensional space.
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:param metric: (*str*) The distance metric to use. The distance function can be ‘chebyshev’,
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‘cityblock’, ‘correlation’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘mahalanobis’,
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‘matching’, ‘minkowski’.
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:param kwargs: Some possible arguments:
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p : scalar The p-norm to apply for Minkowski, weighted and unweighted. Default: 2.
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:return: （*array*) Pairwise distances, returned as a numeric row vector of length m(m–1)/2,
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corresponding to pairs of observations, where m is the number of observations in X.
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"""
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if metric.lower() == 'minkowski':
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p = kwargs.pop('p', 2)
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_func = DistanceUtil.getDistanceFunc(metric, p)
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else:
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_func = DistanceUtil.getDistanceFunc(metric)
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if _func is None:
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return None
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else:
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if isinstance(X, (list, tuple)):
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X = np.array(X)
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r = DistanceUtil.calculateDistance(_func, X._array);
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return np.NDArray(r) |