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Multivariate Pattern Analysis in Python |
Distance functions to be used in kernels and elsewhere
The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.clfs.distance (for developers).
Returns dinstance max(|a-b|) XXX There must be better name!
Useful to select a whole cube of a given “radius”
See also
Full API documentation of absminDistance() in module mvpa.clfs.distance.
See also
Full API documentation of cartesianDistance() in module mvpa.clfs.distance.
Caclulcate Mahalanobis distance of the pairs of points.
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Inverse covariance matrix can be calculated with the following
w = N.linalg.solve(N.cov(x.T), N.identity(x.shape[1]))
or
w = N.linalg.inv(N.cov(x.T))
See also
Full API documentation of mahalanobisDistance() in module mvpa.clfs.distance.
See also
Full API documentation of manhattenDistance() in module mvpa.clfs.distance.
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)
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See also
Full API documentation of pnorm_w_python() in module mvpa.clfs.distance.
Compute weighted euclidean distance matrix between two datasets.
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See also
Full API documentation of squared_euclidean_distance() in module mvpa.clfs.distance.