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Package mvpa :: Package datasets :: Module metric

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Module metric

Classes and functions to provide sense of distances between sample points

Classes

Metric
Abstract class for any finder.

DescreteMetric
Find neighboring points in descretized space

Functions

cartesianDistance(a, b)
Return Cartesian distance between a and b

source code

absminDistance(a, b)
Returns dinstance max(|a-b|) XXX There must be better name!

source code

manhattenDistance(a, b)
Return Manhatten distance between a and b

source code

mahalanobisDistance(x, y=None, w=None)
Caclulcate Mahalanobis distance of the pairs of points.

source code

Imports: N

Function Details

absminDistance(a, b)

Returns dinstance max(|a-b|) XXX There must be better name!

Useful to select a whole cube of a given "radius"

mahalanobisDistance(x, y=None, w=None)

Caclulcate Mahalanobis distance of the pairs of points.

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))

Parameters:

x - first list of points. Rows are samples, columns are features.
y - second list of points (optional)
w (N.ndarray) - optional inverse covariance matrix between the points. It is computed if not given

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