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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 @@
 146 146 ` random_state = _utils.as_random_state(random_state)` 147 147 ` if not 0 <= density <= 1:` 148 148 ` raise ValueError(f"Density must be between 0 and 1, but is {density}.")` 149 - ` chol = scipy.sparse.eye(dim, format=format)` 149 + ` chol = scipy.sparse.eye(dim, format="csr")` 150 150 ` num_off_diag_cholesky = int(0.5 * dim * (dim - 1))` 151 151 ` num_nonzero_entries = int(num_off_diag_cholesky * density)` 152 152 153 153 ` if num_nonzero_entries > 0:` 154 - ` # Samples sparse (non-symmetric) (n, n) matrix` 154 + 155 155 ` sparse_matrix = scipy.sparse.rand(` 156 156 ` m=dim,` 157 157 ` n=dim,` 158 - ` format=format,` 159 - ` density=0.5 * density,` 158 + ` format="csr",` 159 + ` density=density,` 160 160 ` random_state=random_state,` 161 161 ` )` 162 162 163 - ` # Symmetrize sparse matrix` 164 - ` sparse_matrix += sparse_matrix.T` 163 + ` # Rescale entries` 164 + ` sparse_matrix.data *= chol_entry_max - chol_entry_min` 165 + ` sparse_matrix.data += chol_entry_min` 165 166 166 167 ` # Extract lower triangle` 167 168 ` chol += scipy.sparse.tril(A=sparse_matrix, k=-1, format=format)` 168 169 169 - ` return chol @ chol.T` 170 + ` return (chol @ chol.T).asformat(format=format)`
 1 ```coverage: ``` 2 ``` precision: 2 ``` 3 ``` status: ``` 4 ``` project: ``` 5 ``` default: ``` 6 ``` target: auto ``` 7 ``` threshold: 1% ``` 8 ``` patch: ``` 9 ``` default: ``` 10 ``` target: 90% ``` 11 ``` threshold: 1% ``` 12 13 ```comment: ``` 14 ``` layout: "reach, diff, files" ``` 15 ``` behavior: default ``` 16 ``` require_changes: true ```