Unit tests for HybridGaussianFactorGraph.
Definition at line 28 of file test_HybridFactorGraph.py.
◆ calculate_ratio()
◆ estimate_marginals()
def test_HybridFactorGraph.TestHybridGaussianFactorGraph.estimate_marginals |
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cls, |
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target, |
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HybridBayesNet |
proposal_density, |
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N = 10000 |
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◆ measurements()
◆ test_create()
def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_create |
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◆ test_evaluate()
def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_evaluate |
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◆ test_optimize()
def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_optimize |
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◆ test_ratio()
def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_ratio |
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Given a tiny two variable hybrid model, with 2 measurements, test the
ratio of the bayes net model representing P(z,x,n)=P(z|x, n)P(x)P(n)
and the factor graph P(x, n | z)=P(x | n, z)P(n|z),
both of which represent the same posterior.
Definition at line 249 of file test_HybridFactorGraph.py.
◆ test_tiny()
def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_tiny |
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◆ tiny()
HybridBayesNet test_HybridFactorGraph.TestHybridGaussianFactorGraph.tiny |
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num_measurements = 1 , |
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float |
prior_mean = 5.0 , |
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float |
prior_sigma = 0.5 |
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Create a tiny two variable hybrid model which represents
the generative probability P(Z, x0, mode) = P(Z|x0, mode)P(x0)P(mode).
num_measurements: number of measurements in Z = {z0, z1...}
Definition at line 79 of file test_HybridFactorGraph.py.
The documentation for this class was generated from the following file: