rsatoolbox.util.matrix module

Collection of different utility Matrices

rsatoolbox.util.matrix.centering(size)[source]

generates a centering matrix

Parameters

size (int) – size of the center matrix

Returns

size * size

Return type

centering_matrix (numpy.ndarray)

rsatoolbox.util.matrix.get_v(n_cond, sigma_k)[source]

get the rdm covariance from sigma_k

rsatoolbox.util.matrix.indicator(index_vector, positive=False)[source]

Indicator matrix with one column per unique element in vector

Parameters
  • index_vector (numpy.ndarray) – n_row vector to code - discrete values (one dimensional)

  • positive (bool) – should the function ignore zero negative entries in the index_vector? Default: false

Returns

nrow x nconditions

indicator matrix

Return type

indicator_matrix (numpy.ndarray)

rsatoolbox.util.matrix.pairwise_contrast(index_vector)[source]

Contrast matrix with one row per unqiue pairwise contrast

Parameters

index_vector (numpy.ndarray) – n_row vector to code discrete values (one dimensional)

Returns

indicator_matrix: n_values * (n_values-1)/2 x n_row contrast matrix

Return type

numpy.ndarray

rsatoolbox.util.matrix.pairwise_contrast_sparse(index_vector)[source]

Contrast matrix with one row per unqiue pairwise contrast

Parameters

index_vector (numpy.ndarray) – n_row vector to code discrete values (one dimensional)

Returns

indicator_matrix:

n_values * (n_values-1)/2 x n_row contrast matrix

Return type

scipy.sparse.csr_matrix

rsatoolbox.util.matrix.row_col_indicator_g(n_cond)[source]

generates a row and column indicator matrix for a vectorized second moment matrix. The vectorized version has the off-diagonal elements first (like in an RDM), and then appends the diagnoal. You can vectorize a second momement matrix G by np.diag(row_i@G@col_i.T) = np.sum(col_i*(row_i@G)),axis=1)

Parameters

n_cond (int) – Number of conditions underlying the second moment

Returns

n_cond (n_cond-1)/2+n_cond * n_cond col_indicator (numpy.ndarray): n_cond (n_cond-1)/2+n_cond * n_cond

Return type

row_indicator (numpy.ndarray)

rsatoolbox.util.matrix.row_col_indicator_rdm(n_cond)[source]

generates a row and column indicator matrix for an RDM vector

Parameters

n_cond (int) – Number of conditions underlying the RDM

Returns

n_cond (n_cond-1)/2 * n_cond col_indicator (numpy.ndarray): n_cond (n_cond-1)/2 * n_cond

Return type

row_indicator (numpy.ndarray)

rsatoolbox.util.matrix.square_between_category_binary_mask(category_1_idxs, category_2_idxs, size)[source]
A square binary matrix indicating between-category links in an adjacency

matrix.

Parameters
  • category_1_idxs (List[int]) – indices of category 1 members in rows/columns.

  • category_2_idxs (List[int]) – indices of category 2 members in rows/columns.

  • size (int) – total size of matrix

Usage example:

>>> square_between_category_binary_mask(category_1_idxs=[1, 2],
                                        category_2_idxs=[3, 4],
                                        size=5)
array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 1., 1.],
       [0., 0., 0., 1., 1.],
       [0., 1., 1., 0., 0.],
       [0., 1., 1., 0., 0.]])
Returns

A square binary numpy.array indicating between-category links in an adjacency matrix.

rsatoolbox.util.matrix.square_category_binary_mask(category_idxs: List[int], size: int)[source]

A square binary matrix indicating within-category links in an adjacency matrix.

Parameters
  • category_idxs (List[int]) – indices of category members in rows/columns.

  • size (int) – total size of matrix.

Usage example:

>>> square_category_binary_mask(category_idxs=[1, 2, 4], size=5)
array([[0., 0., 0., 0., 0.],
       [0., 1., 1., 0., 1.],
       [0., 1., 1., 0., 1.],
       [0., 0., 0., 0., 0.],
       [0., 1., 1., 0., 1.]])
Returns

A square binary numpy.array indicating within-category pairs.