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.
- class easyvvuq.comparison.validate.ValidateSimilarity[source]¶
Bases:
BaseComparisonElement
- class easyvvuq.comparison.validate.ValidateSimilarityHellinger[source]¶
Bases:
ValidateSimilarity
- 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)
- class easyvvuq.comparison.validate.ValidateSimilarityJensenShannon[source]¶
Bases:
ValidateSimilarity
- 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)
- class easyvvuq.comparison.validate.ValidateSimilarityWasserstein[source]¶
Bases:
ValidateSimilarity
- 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)