Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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122 lines
3.2 KiB
122 lines
3.2 KiB
4 years ago
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__docformat__ = "restructuredtext en"
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__all__ = []
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from numpy import asanyarray, asarray, array, matrix, zeros
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from scipy.sparse.sputils import asmatrix
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from scipy.sparse.linalg.interface import aslinearoperator, LinearOperator, \
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IdentityOperator
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_coerce_rules = {('f','f'):'f', ('f','d'):'d', ('f','F'):'F',
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('f','D'):'D', ('d','f'):'d', ('d','d'):'d',
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('d','F'):'D', ('d','D'):'D', ('F','f'):'F',
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('F','d'):'D', ('F','F'):'F', ('F','D'):'D',
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('D','f'):'D', ('D','d'):'D', ('D','F'):'D',
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('D','D'):'D'}
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def coerce(x,y):
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if x not in 'fdFD':
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x = 'd'
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if y not in 'fdFD':
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y = 'd'
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return _coerce_rules[x,y]
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def id(x):
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return x
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def make_system(A, M, x0, b):
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"""Make a linear system Ax=b
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Parameters
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----------
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A : LinearOperator
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sparse or dense matrix (or any valid input to aslinearoperator)
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M : {LinearOperator, Nones}
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preconditioner
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sparse or dense matrix (or any valid input to aslinearoperator)
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x0 : {array_like, None}
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initial guess to iterative method
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b : array_like
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right hand side
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Returns
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-------
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(A, M, x, b, postprocess)
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A : LinearOperator
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matrix of the linear system
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M : LinearOperator
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preconditioner
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x : rank 1 ndarray
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initial guess
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b : rank 1 ndarray
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right hand side
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postprocess : function
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converts the solution vector to the appropriate
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type and dimensions (e.g. (N,1) matrix)
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"""
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A_ = A
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A = aslinearoperator(A)
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if A.shape[0] != A.shape[1]:
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raise ValueError('expected square matrix, but got shape=%s' % (A.shape,))
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N = A.shape[0]
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b = asanyarray(b)
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if not (b.shape == (N,1) or b.shape == (N,)):
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raise ValueError('A and b have incompatible dimensions')
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if b.dtype.char not in 'fdFD':
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b = b.astype('d') # upcast non-FP types to double
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def postprocess(x):
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if isinstance(b,matrix):
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x = asmatrix(x)
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return x.reshape(b.shape)
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if hasattr(A,'dtype'):
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xtype = A.dtype.char
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else:
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xtype = A.matvec(b).dtype.char
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xtype = coerce(xtype, b.dtype.char)
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b = asarray(b,dtype=xtype) # make b the same type as x
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b = b.ravel()
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if x0 is None:
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x = zeros(N, dtype=xtype)
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else:
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x = array(x0, dtype=xtype)
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if not (x.shape == (N,1) or x.shape == (N,)):
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raise ValueError('A and x have incompatible dimensions')
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x = x.ravel()
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# process preconditioner
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if M is None:
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if hasattr(A_,'psolve'):
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psolve = A_.psolve
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else:
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psolve = id
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if hasattr(A_,'rpsolve'):
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rpsolve = A_.rpsolve
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else:
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rpsolve = id
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if psolve is id and rpsolve is id:
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M = IdentityOperator(shape=A.shape, dtype=A.dtype)
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else:
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M = LinearOperator(A.shape, matvec=psolve, rmatvec=rpsolve,
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dtype=A.dtype)
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else:
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M = aslinearoperator(M)
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if A.shape != M.shape:
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raise ValueError('matrix and preconditioner have different shapes')
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return A, M, x, b, postprocess
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