@deprecated('l1_cross_distances was deprecated in version 0.18 and will be removed in 0.20.')
def l1_cross_distances(X):
 X = check_array(X)
 (n_samples, n_features) = X.shape
 n_nonzero_cross_dist = ((n_samples * (n_samples - 1)) // 2)
 ij = np.zeros((n_nonzero_cross_dist, 2), dtype=np.int)
 D = np.zeros((n_nonzero_cross_dist, n_features))
 ll_1 = 0
 for k in range((n_samples - 1)):
  ll_0 = ll_1
  ll_1 = (((ll_0 + n_samples) - k) - 1)
  ij[ll_0:ll_1, 0] = k
  ij[ll_0:ll_1, 1] = np.arange((k + 1), n_samples)
  D[ll_0:ll_1] = np.abs((X[k] - X[(k + 1):n_samples]))
 return (D, ij)

@ deprecated ( 'l1_cross_distances was deprecated in version 0.18 and will be removed in 0.20.' ) def l1_cross_distances ( X ) : X = check_array ( X ) ( n_samples , n_features ) = X . shape n_nonzero_cross_dist = ( ( n_samples * ( n_samples - 1 ) ) // 2 ) ij = np . zeros ( ( n_nonzero_cross_dist , 2 ) , dtype = np . int ) D = np . zeros ( ( n_nonzero_cross_dist , n_features ) ) ll_1 = 0 for k in range ( ( n_samples - 1 ) ) : ll_0 = ll_1 ll_1 = ( ( ( ll_0 + n_samples ) - k ) - 1 ) ij [ ll_0 : ll_1 , 0 ] = k ij [ ll_0 : ll_1 , 1 ] = np . arange ( ( k + 1 ) , n_samples ) D [ ll_0 : ll_1 ] = np . abs ( ( X [ k ] - X [ ( k + 1 ) : n_samples ] ) ) return ( D , ij )
computes the nonzero componentwise l1 cross-distances between the vectors in x .