msmbuilder.metrics.baseclasses.Vectorized.__init__

Vectorized.__init__(metric='euclidean', p=2, V=None, VI=None)

Create a Vectorized metric

Parameters:

metric : {‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘euclidean’, ‘minkowski’, ‘sqeuclidean’,’dice’, ‘kulsinki’, ‘matching’, ‘rogerstanimoto’, ‘russellrao’, ‘sokalmichener’, ‘sokalsneath’, ‘yule’, ‘seuclidean’, ‘mahalanobis’, ‘sqmahalanobis’}

Distance metric to equip the vector space with. See http://docs.scipy.org/doc/scipy/reference/spatial.distance.html for details

p : int, optional

p-norm order, used for metric=’minkowski’

V : ndarray, optional

variances, used for metric=’seuclidean’

VI : ndarray, optional

inverse covariance matrix, used for metric=’mahalanobis’