bf41af913e2839139e9927118bcf538fb134d71c,scipy/spatial/distance.py,,cdist,#Any#Any#Any#Any#Any#Any#Any#,1723

Before Change

                                             normsB)
        elif mstr in set(["correlation", "co"]):
            XA2 = XA - XA.mean(1)[:, np.newaxis]
            XB2 = XB - XB.mean(1)[:, np.newaxis]
            //X2 = X - np.matlib.repmat(np.mean(X, axis=1).reshape(m, 1), 1, n)
            normsA = np.sqrt(np.sum(XA2 * XA2, axis=1))
            normsB = np.sqrt(np.sum(XB2 * XB2, axis=1))
            _distance_wrap.cdist_cosine_wrap(_convert_to_double(XA2),
                                             _convert_to_double(XB2),
                                             _convert_to_double(dm),
                                             _convert_to_double(normsA),

After Change

            _cosine_cdist(XA, XB, dm)
        elif mstr in set(["correlation", "co"]):
            XA = np.array(XA, dtype=np.double, copy=True)
            XB = np.array(XB, dtype=np.double, copy=True)
            XA -= XA.mean(axis=1)[:, np.newaxis]
            XB -= XB.mean(axis=1)[:, np.newaxis]
            _cosine_cdist(XA, XB, dm)
        elif mstr in set(["mahalanobis", "mahal", "mah"]):

Instances