sklearn.datasets.make_sparse_spd_matrix
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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)
[source]
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Generate a sparse symmetric definite positive matrix.
Read more in the User Guide.
Parameters: |
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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 zero (see notes). Larger values enforce more sparsity. -
norm_diag : boolean, optional (default=False) -
Whether to normalize the output matrix to make the leading diagonal elements all 1 -
smallest_coef : float between 0 and 1, optional (default=0.1) -
The value of the smallest coefficient. -
largest_coef : float between 0 and 1, optional (default=0.9) -
The value of the largest coefficient. -
random_state : int, RandomState instance or None (default) -
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary. |
Returns: |
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prec : sparse matrix of shape (dim, dim) -
The generated matrix. |
Notes
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