Multivariate Pattern Analysis in Python

# mvpa2.clfs.distance

Distance functions to be used in kernels and elsewhere

Functions

`absmin_distance`(a, b) | Returns dinstance max(|a-b|)
`cartesian_distance`(a, b) | Return Cartesian distance between a and b
`corouge`(streamline1, streamline2) | Mean of the mean min distances.
`mahalanobis_distance`(x[, y, w]) | Calculate Mahalanobis distance of the pairs of points.
`manhatten_distance`(a, b) | Return Manhatten distance between a and b
`mean_min`(streamline1, streamline2) | Basic building block to compute several distances between streamlines.
`one_minus_correlation`(X, Y) | Return one minus the correlation matrix between the rows of two matrices.
`pnorm_w`(data1[, data2, weight, p]) | Weighted p-norm between two datasets (scipy.weave implementation)
`pnorm_w_python`(data1[, data2, weight, p, ...]) | Weighted p-norm between two datasets (pure Python implementation)
`squared_euclidean_distance`(data1[, data2, ...]) | Compute weighted euclidean distance matrix between two datasets.