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mvpa2.clfs.distance.pnorm_w_python

mvpa2.clfs.distance.pnorm_w_python(data1, data2=None, weight=None, p=2, heuristic='auto', use_sq_euclidean=True)

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. 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