Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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57 lines
2.3 KiB
57 lines
2.3 KiB
4 years ago
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import numpy as np
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from scipy.sparse import csr_matrix, isspmatrix, isspmatrix_csc
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from ._tools import csgraph_to_dense, csgraph_from_dense,\
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csgraph_masked_from_dense, csgraph_from_masked
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DTYPE = np.float64
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def validate_graph(csgraph, directed, dtype=DTYPE,
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csr_output=True, dense_output=True,
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copy_if_dense=False, copy_if_sparse=False,
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null_value_in=0, null_value_out=np.inf,
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infinity_null=True, nan_null=True):
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"""Routine for validation and conversion of csgraph inputs"""
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if not (csr_output or dense_output):
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raise ValueError("Internal: dense or csr output must be true")
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# if undirected and csc storage, then transposing in-place
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# is quicker than later converting to csr.
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if (not directed) and isspmatrix_csc(csgraph):
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csgraph = csgraph.T
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if isspmatrix(csgraph):
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if csr_output:
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csgraph = csr_matrix(csgraph, dtype=DTYPE, copy=copy_if_sparse)
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else:
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csgraph = csgraph_to_dense(csgraph, null_value=null_value_out)
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elif np.ma.isMaskedArray(csgraph):
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if dense_output:
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mask = csgraph.mask
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csgraph = np.array(csgraph.data, dtype=DTYPE, copy=copy_if_dense)
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csgraph[mask] = null_value_out
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else:
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csgraph = csgraph_from_masked(csgraph)
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else:
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if dense_output:
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csgraph = csgraph_masked_from_dense(csgraph,
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copy=copy_if_dense,
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null_value=null_value_in,
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nan_null=nan_null,
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infinity_null=infinity_null)
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mask = csgraph.mask
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csgraph = np.asarray(csgraph.data, dtype=DTYPE)
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csgraph[mask] = null_value_out
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else:
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csgraph = csgraph_from_dense(csgraph, null_value=null_value_in,
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infinity_null=infinity_null,
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nan_null=nan_null)
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if csgraph.ndim != 2:
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raise ValueError("compressed-sparse graph must be 2-D")
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if csgraph.shape[0] != csgraph.shape[1]:
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raise ValueError("compressed-sparse graph must be shape (N, N)")
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return csgraph
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