Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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63 lines
1.7 KiB
63 lines
1.7 KiB
import numpy as np
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class _FakeMatrix(object):
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def __init__(self, data):
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self._data = data
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self.__array_interface__ = data.__array_interface__
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class _FakeMatrix2(object):
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def __init__(self, data):
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self._data = data
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def __array__(self):
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return self._data
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def _get_array(shape, dtype):
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"""
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Get a test array of given shape and data type.
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Returned NxN matrices are posdef, and 2xN are banded-posdef.
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"""
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if len(shape) == 2 and shape[0] == 2:
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# yield a banded positive definite one
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x = np.zeros(shape, dtype=dtype)
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x[0, 1:] = -1
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x[1] = 2
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return x
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elif len(shape) == 2 and shape[0] == shape[1]:
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# always yield a positive definite matrix
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x = np.zeros(shape, dtype=dtype)
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j = np.arange(shape[0])
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x[j, j] = 2
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x[j[:-1], j[:-1]+1] = -1
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x[j[:-1]+1, j[:-1]] = -1
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return x
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else:
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np.random.seed(1234)
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return np.random.randn(*shape).astype(dtype)
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def _id(x):
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return x
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def assert_no_overwrite(call, shapes, dtypes=None):
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"""
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Test that a call does not overwrite its input arguments
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"""
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if dtypes is None:
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dtypes = [np.float32, np.float64, np.complex64, np.complex128]
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for dtype in dtypes:
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for order in ["C", "F"]:
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for faker in [_id, _FakeMatrix, _FakeMatrix2]:
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orig_inputs = [_get_array(s, dtype) for s in shapes]
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inputs = [faker(x.copy(order)) for x in orig_inputs]
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call(*inputs)
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msg = "call modified inputs [%r, %r]" % (dtype, faker)
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for a, b in zip(inputs, orig_inputs):
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np.testing.assert_equal(a, b, err_msg=msg)
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