Computes distance between each pair of observation vectors in the Cartesian product of two collections of vectors. XA is a by array while XB is a by array. A by array is returned. An exception is thrown if XA and XB do not have the same number of columns.
A rectangular distance matrix Y is returned. For each and , the metric dist(u=XA[i], v=XB[j]) is computed and stored in the th entry.
The following are common calling conventions:
Y = cdist(XA, XB, 'euclidean')
Computes the distance between points using Euclidean distance (2norm) as the distance metric between the points. The points are arranged as dimensional row vectors in the matrix X.
Y = cdist(XA, XB, 'minkowski', p)
Computes the distances using the Minkowski distance (norm) where .
Y = cdist(XA, XB, 'cityblock')
Computes the city block or Manhattan distance between the points.
Y = cdist(XA, XB, 'seuclidean', V=None)
Computes the standardized Euclidean distance. The standardized Euclidean distance between two nvectors u and v is
the i’th components of the points. If not passed, it is automatically computed.
Y = cdist(XA, XB, 'sqeuclidean')
Computes the squared Euclidean distance between the vectors.
Y = cdist(XA, XB, 'cosine')
Computes the cosine distance between vectors u and v,
where is the 2norm of its argument *.
Y = cdist(XA, XB, 'correlation')
Computes the correlation distance between vectors u and v. This is
where is the Manhattan (or 1norm) of its argument, and is the common dimensionality of the vectors.
Y = cdist(XA, XB, 'hamming')
Computes the normalized Hamming distance, or the proportion of those vector elements between two nvectors u and v which disagree. To save memory, the matrix X can be of type boolean.
Y = cdist(XA, XB, 'jaccard')
Computes the Jaccard distance between the points. Given two vectors, u and v, the Jaccard distance is the proportion of those elements u[i] and v[i] that disagree where at least one of them is nonzero.
Y = cdist(XA, XB, 'chebyshev')
Computes the Chebyshev distance between the points. The Chebyshev distance between two nvectors u and v is the maximum norm1 distance between their respective elements. More precisely, the distance is given by
Computes the Canberra distance between the points. The Canberra distance between two points u and v is
Computes the BrayCurtis distance between the points. The BrayCurtis distance between two points u and v is
Computes the Mahalanobis distance between the points. The Mahalanobis distance between two points u and v is where (the VI variable) is the inverse covariance. If VI is not None, VI will be used as the inverse covariance matrix.
Computes the Yule distance between the boolean vectors. (see yule function documentation)
Computes the matching distance between the boolean vectors. (see matching function documentation)
Computes the Dice distance between the boolean vectors. (see dice function documentation)
Computes the Kulsinski distance between the boolean vectors. (see kulsinski function documentation)
Computes the RogersTanimoto distance between the boolean vectors. (see rogerstanimoto function documentation)
Computes the RussellRao distance between the boolean vectors. (see russellrao function documentation)
Computes the SokalMichener distance between the boolean vectors. (see sokalmichener function documentation)
Computes the SokalSneath distance between the vectors. (see sokalsneath function documentation)
Computes the weighted Minkowski distance between the vectors. (see sokalsneath function documentation)
Computes the distance between all pairs of vectors in X using the user supplied 2arity function f. For example, Euclidean distance between the vectors could be computed as follows:
dm = cdist(XA, XB, (lambda u, v: np.sqrt(((uv)*(uv).T).sum())))Note that you should avoid passing a reference to one of the distance functions defined in this library. For example,:
dm = cdist(XA, XB, sokalsneath)would calculate the pairwise distances between the vectors in X using the Python function sokalsneath. This would result in sokalsneath being called times, which is inefficient. Instead, the optimized C version is more efficient, and we call it using the following syntax.:
dm = cdist(XA, XB, 'sokalsneath')
Parameters :  XA : ndarray
XB : ndarray
metric : string or function
w : ndarray
p : double
V : ndarray
VI : ndarray


Returns :  Y : ndarray
