Measures
- class stonesoup.measures.Measure(mapping: ndarray = None, mapping2: ndarray = None)[source]
Measure base type
A measure provides a means to assess the separation between two
State
objects state1 and state2.- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mapping
- mapping: ndarray
Mapping array which specifies which elements within the state vectors are to be assessed as part of the measure
- class stonesoup.measures.Euclidean(mapping: ndarray = None, mapping2: ndarray = None)[source]
Euclidean distance measure
This measure returns the Euclidean distance between a pair of
State
objects.The Euclidean distance between a pair of state vectors \(u\) and \(v\) is defined as:
\[\sqrt{\sum_{i=1}^{N}{(u_i - v_i)^2}}\]- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mapping
- class stonesoup.measures.EuclideanWeighted(weighting: ndarray, mapping: ndarray = None, mapping2: ndarray = None)[source]
Weighted Euclidean distance measure
This measure returns the Euclidean distance between a pair of
State
objects, taking into account a specified weighting.The Weighted Euclidean distance between a pair of state vectors \(u\) and \(v\) with weighting \(w\) is defined as:
\[\sqrt{\sum_{i=1}^{N}{w_i|(u_i - v_i)^2}}\]Note
The EuclideanWeighted object has a property called weighting, which allows the method to be called on different pairs of states. If different weightings need to be used then multiple
Measure
objects must be created with the specific weighting- Parameters
weighting (
numpy.ndarray
) – Weighting vector for the Euclidean calculationmapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mapping
- class stonesoup.measures.SquaredMahalanobis(mapping: ndarray = None, mapping2: ndarray = None, state_covar_inv_cache_size: int = 128)[source]
Squared Mahalanobis distance measure
This measure returns the Squared Mahalanobis distance between a pair of
State
objects taking into account the distribution (i.e. theCovarianceMatrix
) of the firstState
objectThe Squared Mahalanobis distance between a distribution with mean \(\mu\) and Covariance matrix \(\Sigma\) and a point \(x\) is defined as:
\[( {\mu - x}) \Sigma^{-1} ({\mu - x}^T )\]- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mappingstate_covar_inv_cache_size (
int
, optional) – Number of covariance matrix inversions to cache. Setting to 0 will disable the cache, whilst setting to None will not limit the size of the cache. Default is 128.
- state_covar_inv_cache_size: int
Number of covariance matrix inversions to cache. Setting to 0 will disable the cache, whilst setting to None will not limit the size of the cache. Default is 128.
- class stonesoup.measures.Mahalanobis(mapping: ndarray = None, mapping2: ndarray = None, state_covar_inv_cache_size: int = 128)[source]
Mahalanobis distance measure
This measure returns the Mahalanobis distance between a pair of
State
objects taking into account the distribution (i.e. theCovarianceMatrix
) of the firstState
objectThe Mahalanobis distance between a distribution with mean \(\mu\) and Covariance matrix \(\Sigma\) and a point \(x\) is defined as:
\[\sqrt{( {\mu - x}) \Sigma^{-1} ({\mu - x}^T )}\]- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mappingstate_covar_inv_cache_size (
int
, optional) – Number of covariance matrix inversions to cache. Setting to 0 will disable the cache, whilst setting to None will not limit the size of the cache. Default is 128.
- class stonesoup.measures.SquaredGaussianHellinger(mapping: ndarray = None, mapping2: ndarray = None)[source]
Squared Gaussian Hellinger distance measure
This measure returns the Squared Hellinger distance between a pair of
GaussianState
multivariate objects.The Squared Hellinger distance between two multivariate normal distributions \(P \sim N(\mu_1,\Sigma_1)\) and \(Q \sim N(\mu_2,\Sigma_2)\) is defined as:
\[\begin{split}H^{2}(P, Q) &= 1 - {\frac{\det(\Sigma_1)^{\frac{1}{4}}\det(\Sigma_2)^{\frac{1}{4}}} {\det\left(\frac{\Sigma_1+\Sigma_2}{2}\right)^{\frac{1}{2}}}} \exp\left(-\frac{1}{8}(\mu_1-\mu_2)^T \left(\frac{\Sigma_1+\Sigma_2}{2}\right)^{-1}(\mu_1-\mu_2)\right)\\ &\equiv 1 - \sqrt{\frac{\det(\Sigma_1)^{\frac{1}{2}}\det(\Sigma_2)^{\frac{1}{2}}} {\det\left(\frac{\Sigma_1+\Sigma_2}{2}\right)}} \exp\left(-\frac{1}{8}(\mu_1-\mu_2)^T \left(\frac{\Sigma_1+\Sigma_2}{2}\right)^{-1}(\mu_1-\mu_2)\right)\end{split}\]Note
This distance is bounded between 0 and 1
- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mapping
- __call__(state1, state2)[source]
Calculate the Squared Hellinger distance multivariate normal distributions
- Parameters
state1 (
GaussianState
) –state2 (
GaussianState
) –
- Returns
Squared Hellinger distance between two input
GaussianState
- Return type
- class stonesoup.measures.GaussianHellinger(mapping: ndarray = None, mapping2: ndarray = None)[source]
Gaussian Hellinger distance measure
This measure returns the Hellinger distance between a pair of
GaussianState
multivariate objects.The Hellinger distance between two multivariate normal distributions \(P \sim N(\mu_1,\Sigma_1)\) and \(Q \sim N(\mu_2,\Sigma_2)\) is defined as:
\[H(P,Q) = \sqrt{1 - {\frac{\det(\Sigma_1)^{\frac{1}{4}}\det(\Sigma_2)^{\frac{1}{4}}} {\det\left(\frac{\Sigma_1+\Sigma_2}{2}\right)^{\frac{1}{2}}}} \exp\left(-\frac{1}{8}(\mu_1-\mu_2)^T \left(\frac{\Sigma_1+\Sigma_2}{2}\right)^{-1}(\mu_1-\mu_2)\right)}\]Note
This distance is bounded between 0 and 1
- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mapping
- __call__(state1, state2)[source]
Calculate the Hellinger distance between 2 state elements
- Parameters
state1 (
GaussianState
) –state2 (
GaussianState
) –
- Returns
Hellinger distance between two input
GaussianState
- Return type
- class stonesoup.measures.ObservationAccuracy(mapping: ndarray = None, mapping2: ndarray = None)[source]
Accuracy measure
This measure evaluates the accuracy of a categorical distribution with respect to another.
- Parameters
mapping (
numpy.ndarray
, optional) – Mapping array which specifies which elements within the state vectors are to be assessed as part of the measuremapping2 (
numpy.ndarray
, optional) – A second mapping for when the states being compared exist in different parameter spaces. Defaults to the same as the first mapping