Multivariate Pattern Analysis in Python

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# 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) ||x - x’||_w = (sum_{i=1...N} (w_i*|x_i - x’_i|)**p)**(1/p) :Parameters: data1 : np.ndarray First dataset data2 : np.ndarray or None Optional second dataset weight : np.ndarray or None Optional weights per 2nd dimension (features) p : Power heuristic : str Which heuristic to use: * ‘samples’ – python sweep over 0th dim * ‘features’ – python sweep over 1st dim * ‘auto’ decides automatically.

`squared_euclidean_distance`(data1[, data2, ...]) | Compute weighted euclidean distance matrix between two datasets.

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