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@@ -97,16 +97,16 @@
 97 97 density: float, 98 98 chol_entry_min: float = 0.1, 99 99 chol_entry_max: float = 1.0, 100 - format="csr", 100 + format="csr", # pylint: disable="redefined-builtin" 101 101 random_state: Optional[RandomStateArgType] = None, 102 102 ) -> np.ndarray: 103 - """Random sparse symmetric positive definite matrix. 103 + r"""Random sparse symmetric positive definite matrix. 104 104 105 105 Constructs a random sparse symmetric positive definite matrix for a given degree 106 106 of sparsity. The matrix is constructed from its Cholesky factor :math:`L`. Its 107 - diagonal is set to one and all other entries of the lower triangle are sampled 108 - from a uniform distribution with bounds :code:`[chol_entry_min, chol_entry_max]`. 109 - The resulting sparse matrix is then given by :math:`A=LL^\\top`. 107 + diagonal is set to one and all other nonzero entries of the lower triangle are 108 + sampled from a uniform distribution with bounds :code:`[chol_entry_min, 109 + chol_entry_max]`. The resulting sparse matrix is then given by :math:`A=LL^\top`. 110 110 111 111 Parameters 112 112 ----------
@@ -136,9 +136,9 @@
 136 136 >>> sparsemat = random_sparse_spd_matrix(dim=5, density=0.1, random_state=42) 137 137 >>> sparsemat.todense() 138 138 matrix([[1. , 0. , 0. , 0. , 0. ], 139 - [0. , 1. , 0. , 0.30424224, 0. ], 139 + [0. , 1. , 0. , 0.37381802, 0. ], 140 140 [0. , 0. , 1. , 0. , 0. ], 141 - [0. , 0.30424224, 0. , 1.09256334, 0. ], 141 + [0. , 0.37381802, 0. , 1.13973991, 0. ], 142 142 [0. , 0. , 0. , 0. , 1. ]]) 143 143 """ 144 144
@@ -146,24 +146,25 @@