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sklearn.datasets.make_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.


prec: array of shape = [dim, dim] :


The sparsity is actually imposed on the cholesky factor of the matrix. Thus alpha does not translate directly into the filling fraction of the matrix itself.

Examples using sklearn.datasets.make_sparse_spd_matrix