return hamming (u, v, w = w) def dice (u, v, w = None): """ The Euclidean distance between 1-D arrays u and v, is defined as Scipy library main repository. disagree where at least one of them is non-zero. using the user supplied 2-arity function f. For example, is inefficient. the distance functions defined in this library. and \(x \cdot y\) is the dot product of \(x\) and \(y\). (see yule function documentation), Computes the Dice distance between the boolean vectors. The scipy.spatial.distance.squareform(X, force=’no’, checks=True) squareform(X[, force, checks])Converts a vector-form distance vector to a square-form distance matrix, and vice-versa. maximum norm-1 distance between their respective elements. ‘wminkowski’ is deprecated and will be removed in SciPy 1.8.0. V : ndarray For example,: would calculate the pair-wise distances between the vectors in $ pip2 install scipy # for python 2.7 $ pip3 install scipy # for python 3.x Share. Computes the Chebyshev distance between the points. Mahalanobis distance between two points u and v is Pairwise distances between observations in n-dimensional space. scipy.spatial.distance.euclidean¶ scipy.spatial.distance.euclidean (u, v, w = None) [source] ¶ Computes the Euclidean distance between two 1-D arrays. E.g. 2.1. pdist. \(u \cdot v\) is the dot product of u and v. Computes the correlation distance between vectors u and v. This is. (see russellrao function documentation), Computes the Sokal-Michener distance between each pair of Computes the distance between all pairs of vectors in X The variance vector for standardized Euclidean. To save memory, the matrix X can be of type See squareform for information on how to calculate the index of Euclidean distance between the vectors could be computed def gaussian_weights(bundle, n_points=100, return_mahalnobis=False): """ Calculate weights for each streamline/node in a bundle, based on a Mahalanobis distance from the mean of the bundle, at that node Parameters ----- bundle : array or list If this is a list, assume that it is a list of streamline coordinates (each entry is a 2D array, of shape n by 3). scipy.spatial.distance.cdist(XA, XB, metric='euclidean', p=2, V=None, VI=None, w=None)¶ Computes distance between each pair of observation vectors in the Cartesian product of two collections of vectors. To save memory, the matrix X can be of type The Cosine distance between u and v , is defined as 定义如： Y = scipy.spatial.distance.pdist(X, metric='euclidean', *args, **kwargs) … Returns a condensed distance matrix Y. Mahalanobis distance between two points u and v is future scipy version. Computes the standardized Euclidean distance. disagree. The standardized boolean vectors. points. where \(\bar{v}\) is the mean of the elements of vector v, each \(i\) and \(j\) (where \(i

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