mvpa2.datasets.sources.skl_data.skl_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None)

Generate a sparse symmetric definite positive matrix.


dim: integer, optional (default=1) :

The size of the random matrix to generate.

alpha: float between 0 and 1, optional (default=0.95) :

The probability that a coefficient is non zero (see notes).

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

largest_coef : float between 0 and 1, optional (default=0.9)

The value of the largest coefficient.

smallest_coef : float between 0 and 1, optional (default=0.1)

The value of the smallest coefficient.

norm_diag : boolean, optional (default=False)

Whether to normalize the output matrix to make the leading diagonal elements all 1


prec : sparse matrix of shape (dim, dim)

The generated matrix.

See also



This function has been auto-generated by wrapping make_sparse_spd_matrix() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.