Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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169 lines
4.5 KiB
169 lines
4.5 KiB
"""Functions to extract parts of sparse matrices
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"""
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__docformat__ = "restructuredtext en"
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__all__ = ['find', 'tril', 'triu']
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from .coo import coo_matrix
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def find(A):
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"""Return the indices and values of the nonzero elements of a matrix
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Parameters
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----------
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A : dense or sparse matrix
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Matrix whose nonzero elements are desired.
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Returns
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-------
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(I,J,V) : tuple of arrays
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I,J, and V contain the row indices, column indices, and values
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of the nonzero matrix entries.
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Examples
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--------
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>>> from scipy.sparse import csr_matrix, find
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>>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]])
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>>> find(A)
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(array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7., 8., 9.]))
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"""
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A = coo_matrix(A, copy=True)
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A.sum_duplicates()
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# remove explicit zeros
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nz_mask = A.data != 0
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return A.row[nz_mask], A.col[nz_mask], A.data[nz_mask]
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def tril(A, k=0, format=None):
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"""Return the lower triangular portion of a matrix in sparse format
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Returns the elements on or below the k-th diagonal of the matrix A.
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- k = 0 corresponds to the main diagonal
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- k > 0 is above the main diagonal
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- k < 0 is below the main diagonal
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Parameters
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----------
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A : dense or sparse matrix
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Matrix whose lower trianglar portion is desired.
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k : integer : optional
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The top-most diagonal of the lower triangle.
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format : string
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Sparse format of the result, e.g. format="csr", etc.
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Returns
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-------
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L : sparse matrix
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Lower triangular portion of A in sparse format.
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See Also
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--------
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triu : upper triangle in sparse format
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Examples
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--------
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>>> from scipy.sparse import csr_matrix, tril
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>>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]],
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... dtype='int32')
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>>> A.toarray()
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array([[1, 2, 0, 0, 3],
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[4, 5, 0, 6, 7],
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[0, 0, 8, 9, 0]])
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>>> tril(A).toarray()
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array([[1, 0, 0, 0, 0],
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[4, 5, 0, 0, 0],
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[0, 0, 8, 0, 0]])
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>>> tril(A).nnz
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4
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>>> tril(A, k=1).toarray()
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array([[1, 2, 0, 0, 0],
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[4, 5, 0, 0, 0],
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[0, 0, 8, 9, 0]])
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>>> tril(A, k=-1).toarray()
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array([[0, 0, 0, 0, 0],
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[4, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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>>> tril(A, format='csc')
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<3x5 sparse matrix of type '<class 'numpy.int32'>'
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with 4 stored elements in Compressed Sparse Column format>
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"""
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# convert to COOrdinate format where things are easy
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A = coo_matrix(A, copy=False)
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mask = A.row + k >= A.col
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return _masked_coo(A, mask).asformat(format)
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def triu(A, k=0, format=None):
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"""Return the upper triangular portion of a matrix in sparse format
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Returns the elements on or above the k-th diagonal of the matrix A.
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- k = 0 corresponds to the main diagonal
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- k > 0 is above the main diagonal
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- k < 0 is below the main diagonal
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Parameters
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----------
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A : dense or sparse matrix
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Matrix whose upper trianglar portion is desired.
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k : integer : optional
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The bottom-most diagonal of the upper triangle.
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format : string
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Sparse format of the result, e.g. format="csr", etc.
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Returns
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-------
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L : sparse matrix
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Upper triangular portion of A in sparse format.
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See Also
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--------
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tril : lower triangle in sparse format
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Examples
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--------
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>>> from scipy.sparse import csr_matrix, triu
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>>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]],
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... dtype='int32')
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>>> A.toarray()
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array([[1, 2, 0, 0, 3],
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[4, 5, 0, 6, 7],
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[0, 0, 8, 9, 0]])
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>>> triu(A).toarray()
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array([[1, 2, 0, 0, 3],
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[0, 5, 0, 6, 7],
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[0, 0, 8, 9, 0]])
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>>> triu(A).nnz
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8
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>>> triu(A, k=1).toarray()
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array([[0, 2, 0, 0, 3],
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[0, 0, 0, 6, 7],
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[0, 0, 0, 9, 0]])
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>>> triu(A, k=-1).toarray()
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array([[1, 2, 0, 0, 3],
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[4, 5, 0, 6, 7],
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[0, 0, 8, 9, 0]])
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>>> triu(A, format='csc')
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<3x5 sparse matrix of type '<class 'numpy.int32'>'
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with 8 stored elements in Compressed Sparse Column format>
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"""
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# convert to COOrdinate format where things are easy
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A = coo_matrix(A, copy=False)
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mask = A.row + k <= A.col
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return _masked_coo(A, mask).asformat(format)
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def _masked_coo(A, mask):
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row = A.row[mask]
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col = A.col[mask]
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data = A.data[mask]
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return coo_matrix((data, (row, col)), shape=A.shape, dtype=A.dtype)
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