easyvvuq.comparison package¶
Submodules¶
easyvvuq.comparison.base module¶
Provides base class for all comparison/validation elements.
easyvvuq.comparison.validate module¶
Validation by comparing QoI distributions.
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class
easyvvuq.comparison.validate.
ValidateSimilarity
[source]¶
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class
easyvvuq.comparison.validate.
ValidateSimilarityHellinger
[source]¶ Bases:
easyvvuq.comparison.validate.ValidateSimilarity
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dist
(p, q)[source]¶ Compute Hellinger distance between two discrete probability distributions (PDF). The Hellinger distance metric gives an output in the range [0,1] with values closer to 0 meaning the PDFs are more similar.
Parameters: - p (NumPy array)
- q (NumPy array)
Returns: - Hellinger distance between distributions p and q.
- https (//en.wikipedia.org/wiki/Hellinger_distance)
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class
easyvvuq.comparison.validate.
ValidateSimilarityJensenShannon
[source]¶ Bases:
easyvvuq.comparison.validate.ValidateSimilarity
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dist
(p, q)[source]¶ Compute Jensen-Shannon distance between two discrete probability distributions (PDF). It is based on Kullback–Leibler divergence and gives an output metric un the range [0,1] with values closer to 0 meaning the PDFs are more similar.
Parameters: - p (NumPy array)
- q (NumPy array)
Returns: - Jensen-Shannon divergence between distributions p and q.
- https (//en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence)
- https (//en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)
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class
easyvvuq.comparison.validate.
ValidateSimilarityWasserstein
[source]¶ Bases:
easyvvuq.comparison.validate.ValidateSimilarity
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dist
(p, q)[source]¶ Compute Wasserstein distance between two discrete cumulative distributions (CDF). The Wasserstein distance has an unrestricted range with a lower limit of 0. A smaller distance indicates a stronger similarity between between CFDs.
Parameters: - p (NumPy array)
- q (NumPy array)
Returns: - Wasserstein distance between distributions p and q.
- https (//en.wikipedia.org/wiki/Wasserstein_metric)
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