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# mvpa2.misc.stats.DSMatrix¶

class mvpa2.misc.stats.DSMatrix(data_vectors, metric='spearman')

DSMatrix allows for the creation of dissilimarity matrices using arbitrary distance metrics.

Methods

 get_full_matrix() get_metric() get_triangle() get_triangle_vector_form([k]) Returns values from a triangular part of the matrix in vector form get_vector_form()

Initialize DSMatrix

Parameters : data_vectors : ndarray m x n collection of vectors, where m is the number of exemplars and n is the number of features per exemplar metric : string Distance metric to use (e.g., ‘euclidean’, ‘spearman’, ‘pearson’, ‘confusion’)

Methods

 get_full_matrix() get_metric() get_triangle() get_triangle_vector_form([k]) Returns values from a triangular part of the matrix in vector form get_vector_form()
get_full_matrix()
get_metric()
get_triangle()
get_triangle_vector_form(k=0)

Returns values from a triangular part of the matrix in vector form

Parameters : k: int : offset from diagonal. k=0 means all values from the diagonal and those above it, k=1 all values from the cells above the diagonal, etc v: np.ndarray (vector) : array with values from the similarity matrix. If the matrix is shaped p xp, then if k>=0, then v has (p-k)*(p-k+1)/2 elements. If k<0, it has p*p-(p+k)*(p+k-1)/2 elements.
get_vector_form()

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