# Source code for stonesoup.measures

from abc import abstractmethod
from functools import lru_cache

import numpy as np
from scipy.spatial import distance

from .base import Base, Property
from .types.state import State

[docs]class Measure(Base):
"""Measure base type

A measure provides a means to assess the separation between two
:class:~.State objects state1 and state2.
"""
mapping: np.ndarray = Property(
default=None,
doc="Mapping array which specifies which elements within the"
" state vectors are to be assessed as part of the measure"
)
mapping2: np.ndarray = Property(
default=None,
doc="A second mapping for when the states being compared exist "
"in different parameter spaces. Defaults to the same as the"
" first mapping"
)

[docs]    def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.mapping2 is not None and self.mapping is None:
raise ValueError("Cannot set mapping2 if mapping is None. "
"If this is really what you meant to do, then"
" set mapping to include all dimensions.")
if self.mapping2 is None and self.mapping is not None:
self.mapping2 = self.mapping

[docs]    @abstractmethod
def __call__(self, state1, state2):
r"""
Compute the distance between a pair of :class:~.State objects

Parameters
----------
state1 : :class:~.State
state2 : :class:~.State

Returns
-------
float
distance measure between a pair of input :class:~.State objects

"""
return NotImplementedError

[docs]class Euclidean(Measure):
r"""Euclidean distance measure

This measure returns the Euclidean distance between a pair of
:class:~.State objects.

The Euclidean distance between a pair of state vectors :math:u and
:math:v is defined as:

.. math::
\sqrt{\sum_{i=1}^{N}{(u_i - v_i)^2}}

"""
[docs]    def __call__(self, state1, state2):
r"""Calculate the Euclidean distance between a pair of state vectors

Parameters
----------
state1 : :class:~.State
state2 : :class:~.State

Returns
-------
float
Euclidean distance between two input :class:~.State

"""
# Calculate Euclidean distance between two state
state_vector1 = getattr(state1, 'mean', state1.state_vector)
state_vector2 = getattr(state2, 'mean', state2.state_vector)

if self.mapping is not None:
return distance.euclidean(state_vector1[self.mapping, 0],
state_vector2[self.mapping2, 0])
else:
return distance.euclidean(state_vector1[:, 0], state_vector2[:, 0])

[docs]class EuclideanWeighted(Measure):
r"""Weighted Euclidean distance measure

This measure returns the Euclidean distance between a pair of
:class:~.State objects, taking into account a specified weighting.

The Weighted Euclidean distance between a pair of state vectors :math:u
and :math:v with weighting :math:w is defined as:

.. math::
\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
:class:Measure objects must be created with the specific weighting

"""
weighting: np.ndarray = Property(doc="Weighting vector for the Euclidean calculation")

[docs]    def __call__(self, state1, state2):
r"""Calculate the weighted Euclidean distance between a pair of state
objects

Parameters
----------
state1 : :class:~.State
state2 : :class:~.State

Returns
-------
dist : float
Weighted euclidean distance between two input
:class:~.State objects

"""
state_vector1 = getattr(state1, 'mean', state1.state_vector)
state_vector2 = getattr(state2, 'mean', state2.state_vector)

if self.mapping is not None:
return distance.euclidean(state_vector1[self.mapping, 0],
state_vector2[self.mapping2, 0],
self.weighting)
else:
return distance.euclidean(state_vector1[:, 0],
state_vector2[:, 0],
self.weighting)

[docs]class SquaredMahalanobis(Measure):
r"""Squared Mahalanobis distance measure

This measure returns the Squared Mahalanobis distance between a pair of
:class:~.State objects taking into account the distribution (i.e.
the :class:~.CovarianceMatrix) of the first :class:.State object

The Squared Mahalanobis distance between a distribution with mean :math:\mu
and Covariance matrix :math:\Sigma and a point :math:x is defined as:

.. math::
( {\mu - x})  \Sigma^{-1}  ({\mu - x}^T )

"""
state_covar_inv_cache_size: int = Property(
default=128,
doc="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.")

[docs]    def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.state_covar_inv_cache_size is None or self.state_covar_inv_cache_size > 0:
self._inv_cov = lru_cache(maxsize=self.state_covar_inv_cache_size)(self._inv_cov)

[docs]    def __call__(self, state1, state2):
r"""Calculate the Squared Mahalanobis distance between a pair of state objects

Parameters
----------
state1 : :class:~.State
state2 : :class:~.State

Returns
-------
float
Squared Mahalanobis distance between a pair of input :class:~.State
objects

"""
state_vector1 = getattr(state1, 'mean', state1.state_vector)
state_vector2 = getattr(state2, 'mean', state2.state_vector)

if self.mapping is not None:
u = state_vector1[self.mapping, 0]
v = state_vector2[self.mapping2, 0]
# extract the mapped covariance data
vi = self._inv_cov(state1, tuple(self.mapping))
else:
u = state_vector1[:, 0]
v = state_vector2[:, 0]
vi = self._inv_cov(state1)

delta = u - v

return np.dot(np.dot(delta, vi), delta)

@staticmethod
def _inv_cov(state, mapping=None):
if mapping:
rows = np.array(mapping, dtype=np.intp)
columns = np.array(mapping, dtype=np.intp)
covar = state.covar[rows[:, np.newaxis], columns]
else:
covar = state.covar

return np.linalg.inv(covar)

[docs]class Mahalanobis(SquaredMahalanobis):
r"""Mahalanobis distance measure

This measure returns the Mahalanobis distance between a pair of
:class:~.State objects taking into account the distribution (i.e.
the :class:~.CovarianceMatrix) of the first :class:.State object

The Mahalanobis distance between a distribution with mean :math:\mu and
Covariance matrix :math:\Sigma and a point :math:x is defined as:

.. math::
\sqrt{( {\mu - x})  \Sigma^{-1}  ({\mu - x}^T )}

"""
[docs]    def __call__(self, state1, state2):
r"""Calculate the Mahalanobis distance between a pair of state objects

Parameters
----------
state1 : :class:~.State
state2 : :class:~.State

Returns
-------
float
Mahalanobis distance between a pair of input :class:~.State
objects

"""
return np.sqrt(super().__call__(state1, state2))

[docs]class SquaredGaussianHellinger(Measure):
r"""Squared Gaussian Hellinger distance measure

This measure returns the Squared Hellinger distance between a pair of
:class:~.GaussianState multivariate objects.

The Squared Hellinger distance between two multivariate normal
distributions :math:P \sim N(\mu_1,\Sigma_1) and
:math:Q \sim N(\mu_2,\Sigma_2) is defined as:

.. math::
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)

Note
----
This distance is bounded between 0 and 1
"""
[docs]    def __call__(self, state1, state2):
r""" Calculate the Squared Hellinger distance multivariate normal
distributions

Parameters
----------
state1 : :class:~.GaussianState
state2 : :class:~.GaussianState

Returns
-------
float
Squared Hellinger distance between two input
:class:~.GaussianState

"""
if hasattr(state1, 'mean'):
state_vector1 = state1.mean
else:
state_vector1 = state1.state_vector

if hasattr(state2, 'mean'):
state_vector2 = state2.mean
else:
state_vector2 = state2.state_vector

if self.mapping is not None:
mu1 = state_vector1[self.mapping, :]
mu2 = state_vector2[self.mapping2, :]

# extract the mapped covariance data
rows = np.array(self.mapping, dtype=np.intp)
columns = np.array(self.mapping, dtype=np.intp)
sigma1 = state1.covar[rows[:, np.newaxis], columns]
sigma2 = state2.covar[rows[:, np.newaxis], columns]
else:
mu1 = state_vector1
mu2 = state_vector2
sigma1 = state1.covar
sigma2 = state2.covar

sigma1_plus_sigma2 = sigma1 + sigma2
mu1_minus_mu2 = mu1 - mu2
E = mu1_minus_mu2.T @ np.linalg.inv(sigma1_plus_sigma2/2) @ mu1_minus_mu2
epsilon = -0.125*E
numerator = np.sqrt(np.linalg.det(sigma1 @ sigma2))
denominator = np.linalg.det(sigma1_plus_sigma2/2)
squared_hellinger = 1 - np.sqrt(numerator/denominator)*np.exp(epsilon)
squared_hellinger = squared_hellinger.item()

if -1e-10 < squared_hellinger < 0.0:
squared_hellinger = 0.0
elif squared_hellinger < 0.0:  # pragma: no cover
raise ValueError("Measure shouldn't be less than 0")  # this should be impossible

return squared_hellinger

[docs]class GaussianHellinger(SquaredGaussianHellinger):
r"""Gaussian Hellinger distance measure

This measure returns the Hellinger distance between a pair of
:class:~.GaussianState multivariate objects.

The Hellinger distance between two multivariate normal distributions
:math:P \sim N(\mu_1,\Sigma_1) and :math:Q \sim N(\mu_2,\Sigma_2)
is defined as:

.. math::
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
"""
[docs]    def __call__(self, state1, state2):
r""" Calculate the Hellinger distance between 2 state elements

Parameters
----------
state1 : :class:~.GaussianState
state2 : :class:~.GaussianState

Returns
-------
float
Hellinger distance between two input :class:~.GaussianState

"""
return np.sqrt(super().__call__(state1, state2))

[docs]class ObservationAccuracy(Measure):
r"""Accuracy measure

This measure evaluates the accuracy of a categorical distribution with respect to another."""

[docs]    def __call__(self, state1, state2):

if isinstance(state1, State):
s1 = state1.state_vector
else:
s1 = state1

if isinstance(state2, State):
s2 = state2.state_vector
else:
s2 = state2

mins = [min(s1, s2) for s1, s2 in zip(s1, s2)]
maxs = [max(s1, s2) for s1, s2 in zip(s1, s2)]
return np.sum(mins)/np.sum(maxs)