Module distance

Returns dinstance max(|a-b|) XXX There must be better name! XXX Actually, why is it absmin not absmax?

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

Calculate 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

Parameters:

• data1 (N.ndarray) - first dataset
• data2 (N.ndarray) - second dataset. If None, compute the euclidean distance between the first dataset versus itself. (Defaults to None)
• weight (N.ndarray) - vector of weights, each one associated to each dimension of the dataset (Defaults to None)

Return one minus the correlation matrix between the rows of two matrices.

This functions computes a matrix of correlations between all pairs of rows of two matrices. Unlike NumPy's corrcoef() this function will only considers pairs across matrices and not within, e.g. both elements of a pair never have the same source matrix as origin.

Both arrays need to have the same number of columns.

Example:

>>> X = N.random.rand(20,80)
>>> Y = N.random.rand(5,80)
>>> C = oneMinusCorrelation(X, Y)
>>> print C.shape
(20, 5)

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 (N.ndarray) - First dataset
• data2 (N.ndarray or None) - Optional second dataset
• weight (N.ndarray or None) - Optional weights per 2nd dimension (features)
• p - Power
• heuristic (basestring) -

  Which heuristic to use:

  • 'samples' -- python sweep over 0th dim
  • 'features' -- python sweep over 1st dim
  • 'auto' decides automatically. If # of features (shape[1]) is much larger than # of samples (shape[0]) -- use 'samples', and use 'features' otherwise

• use_sq_euclidean (bool) - Either to use squared_euclidean_distance_matrix for computation if p==2

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 (N.ndarray) - First dataset
• data2 (N.ndarray or None) - Optional second dataset
• weight (N.ndarray or None) - Optional weights per 2nd dimension (features)
• p - Power
• heuristic (basestring) -

  Which heuristic to use:

  • 'samples' -- python sweep over 0th dim

  • 'features' -- python sweep over 1st dim

  • 'auto' decides automatically. If # of features (shape[1]) is much larger than # of samples (shape[0]) -- use 'samples', and use 'features' otherwise

• use_sq_euclidean (bool) - Either to use squared_euclidean_distance_matrix for computation if p==2