Commit ⋅ probabilistic-numerics/probnum

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.../probnum/problems/zoo/linalg/_random_spd_matrix.py changed.

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...ems/test_zoo/test_linalg/test_random_spd_matrix.py has changed.

@@ -97,16 +97,16 @@

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    density: float,
    chol_entry_min: float = 0.1,
    chol_entry_max: float = 1.0,
    format="csr",
    format="csr",  # pylint: disable="redefined-builtin"
    random_state: Optional[RandomStateArgType] = None,
) -> np.ndarray:
    """Random sparse symmetric positive definite matrix.
    r"""Random sparse symmetric positive definite matrix.

    Constructs a random sparse symmetric positive definite matrix for a given degree
    of sparsity. The matrix is constructed from its Cholesky factor :math:`L`. Its
    diagonal is set to one and all other entries of the lower triangle are sampled
    from a uniform distribution with bounds :code:`[chol_entry_min, chol_entry_max]`.
    The resulting sparse matrix is then given by :math:`A=LL^\\top`.
    diagonal is set to one and all other nonzero entries of the lower triangle are
    sampled from a uniform distribution with bounds :code:`[chol_entry_min,
    chol_entry_max]`. The resulting sparse matrix is then given by :math:`A=LL^\top`.

    Parameters
    ----------

@@ -136,9 +136,9 @@

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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 @@

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    random_state = _utils.as_random_state(random_state)
    if not 0 <= density <= 1:
        raise ValueError(f"Density must be between 0 and 1, but is {density}.")
    chol = scipy.sparse.eye(dim, format=format)
    chol = scipy.sparse.eye(dim, format="csr")
    num_off_diag_cholesky = int(0.5 * dim * (dim - 1))
    num_nonzero_entries = int(num_off_diag_cholesky * density)

    if num_nonzero_entries > 0:
        # Samples sparse (non-symmetric) (n, n) matrix

        sparse_matrix = scipy.sparse.rand(
            m=dim,
            n=dim,
            format=format,
            density=0.5 * density,
            format="csr",
            density=density,
            random_state=random_state,
        )

        # Symmetrize sparse matrix
        sparse_matrix += sparse_matrix.T
        # Rescale entries
        sparse_matrix.data *= chol_entry_max - chol_entry_min
        sparse_matrix.data += chol_entry_min

        # Extract lower triangle
        chol += scipy.sparse.tril(A=sparse_matrix, k=-1, format=format)

    return chol @ chol.T
    return (chol @ chol.T).asformat(format=format)

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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		----------

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)

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