Regulariser amendment and implementation in ParticleUpdater

stonesoup/predictor/particle.py

@@ -54,6 +54,7 @@ def predict(self, prior, timestamp=None, **kwargs):
             **kwargs)
 
         return Prediction.from_state(prior,
+                                     parent=prior,
                                      state_vector=new_state_vector,
                                      timestamp=timestamp,
                                      transition_model=self.transition_model)
@@ -341,6 +342,7 @@ def predict(self, prior, timestamp=None, **kwargs):
             existence_probability=predicted_existence,
             parent=untransitioned_state,
             timestamp=timestamp,
+            transition_model=self.transition_model
         )
         return new_particle_state
 

stonesoup/regulariser/particle.py

@@ -1,10 +1,13 @@
 import copy
 import numpy as np
 from scipy.stats import multivariate_normal, uniform
+from typing import Sequence
 
 from .base import Regulariser
 from ..functions import cholesky_eps
 from ..types.state import ParticleState
+from ..models.transition import TransitionModel
+from ..base import Property
 
 
 class MCMCRegulariser(Regulariser):
@@ -14,7 +17,7 @@ class MCMCRegulariser(Regulariser):
     of effectiveness. Sometimes this is not desirable, or possible, when a particular algorithm
     requires the introduction of new samples as part of the filtering process for example.
 
-    This is a particlar implementation of a MCMC move step that uses the Metropolis-Hastings
+    This is a particular implementation of a MCMC move step that uses the Metropolis-Hastings
     algorithm [1]_. After resampling, particles are moved a small amount, according do a Gaussian
     kernel, to a new state only if the Metropolis-Hastings acceptance probability is met by a
     random number assigned to each particle from a uniform random distribution, otherwise they
@@ -24,21 +27,21 @@ class MCMCRegulariser(Regulariser):
     ----------
     .. [1] Robert, Christian P. & Casella, George, Monte Carlo Statistical Methods, Springer, 1999.
 
-    .. [2] Ristic, Branco & Arulampalam, Sanjeev & Gordon, Neil, Beyond the Kalman Filter:
+    .. [2] Ristic, Branko & Arulampalam, Sanjeev & Gordon, Neil, Beyond the Kalman Filter:
         Particle Filters for Target Tracking Applications, Artech House, 2004. """
 
-    def regularise(self, prior, posterior, detections):
+    transition_model: TransitionModel = Property(doc="Transition model used for prediction",
+                                                 default=None)
+
+    def regularise(self, prior, posterior):
         """Regularise the particles
 
         Parameters
         ----------
-        prior : :class:`~.ParticleState` type or list of :class:`~.Particle`
-            prior particle distribution
-        posterior : :class:`~.ParticleState` type or list of :class:`~.Particle`
-            posterior particle distribution
-        detections : set of :class:`~.Detection`
-            set of detections containing clutter,
-            true detections or both
+        prior : :class:`~.ParticleState` type
+            prior particle distribution.
+        posterior : :class:`~.ParticleState` type
+            posterior particle distribution.
 
         Returns
         -------
@@ -47,15 +50,27 @@ def regularise(self, prior, posterior, detections):
         """
 
         if not isinstance(posterior, ParticleState):
-            posterior = ParticleState(None, particle_list=posterior)
+            raise TypeError('Only ParticleState type is supported!')
 
         if not isinstance(prior, ParticleState):
-            prior = ParticleState(None, particle_list=prior)
+            raise TypeError('Only ParticleState type is supported!')
 
         regularised_particles = copy.copy(posterior)
         moved_particles = copy.copy(posterior)
+        transitioned_prior = copy.copy(prior)
+
+        hypotheses = posterior.hypothesis if isinstance(posterior.hypothesis, Sequence) \
+            else [posterior.hypothesis]
+
+        transition_model = hypotheses[0].prediction.transition_model or self.transition_model
+        if transition_model is not None:
+            time_interval = posterior.timestamp - prior.timestamp
+            transitioned_prior.state_vector = \
+                transition_model.function(prior, noise=False, time_interval=time_interval)
+
+        detections = {hypothesis.measurement for hypothesis in hypotheses if hypothesis}
 
-        if detections is not None:
+        if detections:
             ndim = prior.state_vector.shape[0]
             nparticles = len(posterior)
 
@@ -70,13 +85,11 @@ def regularise(self, prior, posterior, detections):
                 hopt * cholesky_eps(covar_est) @ np.random.randn(ndim, nparticles)
 
             # Evaluate likelihoods
-            part_diff = moved_particles.state_vector - prior.state_vector
-            part_diff_mean = np.average(part_diff, axis=1)
-            move_likelihood = multivariate_normal.logpdf((part_diff - part_diff_mean).T,
+            part_diff = moved_particles.state_vector - transitioned_prior.state_vector
+            move_likelihood = multivariate_normal.logpdf(part_diff.T,
                                                          cov=covar_est)
-            post_part_diff = posterior.state_vector - prior.state_vector
-            post_part_diff_mean = np.average(post_part_diff, axis=1)
-            post_likelihood = multivariate_normal.logpdf((post_part_diff - post_part_diff_mean).T,
+            post_part_diff = posterior.state_vector - transitioned_prior.state_vector
+            post_likelihood = multivariate_normal.logpdf(post_part_diff.T,
                                                          cov=covar_est)
 
             # Evaluate measurement likelihoods

stonesoup/regulariser/tests/test_particle.py

@@ -1,17 +1,37 @@
 import numpy as np
 import datetime
+import pytest
 
 from ...types.state import ParticleState
 from ...types.particle import Particle
 from ...types.hypothesis import SingleHypothesis
 from ...types.prediction import ParticleStatePrediction, ParticleMeasurementPrediction
 from ...models.measurement.linear import LinearGaussian
+from ...models.transition.linear import CombinedLinearGaussianTransitionModel, ConstantVelocity
 from ...types.detection import Detection
-from ...types.update import ParticleStateUpdate
+from ...types.update import Update, ParticleStateUpdate
 from ..particle import MCMCRegulariser
 
 
-def test_regulariser():
+@pytest.mark.parametrize(
+    "transition_model, model_flag",
+    [
+        (
+            CombinedLinearGaussianTransitionModel([ConstantVelocity([0.05])]),  # transition_model
+            False,  # model_flag
+        ),
+        (
+            CombinedLinearGaussianTransitionModel([ConstantVelocity([0.05])]),  # transition_model
+            True,  # model_flag
+        ),
+        (
+            None,  # transition_model
+            False,  # model_flag
+        )
+    ],
+    ids=["with_transition_model_init", "without_transition_model_init", "no_transition_model"]
+)
+def test_regulariser(transition_model, model_flag):
     particles = ParticleState(state_vector=None, particle_list=[Particle(np.array([[10], [10]]),
                                                                          1 / 9),
                                                                 Particle(np.array([[10], [20]]),
@@ -32,34 +52,54 @@ def test_regulariser():
                                                                          1 / 9),
                                                                 ])
     timestamp = datetime.datetime.now()
-    prediction = ParticleStatePrediction(None, particle_list=particles.particle_list,
-                                         timestamp=timestamp)
-    meas_pred = ParticleMeasurementPrediction(None, particle_list=particles, timestamp=timestamp)
+    if transition_model is not None:
+        new_state_vector = transition_model.function(particles,
+                                                     noise=True,
+                                                     time_interval=datetime.timedelta(seconds=1))
+    else:
+        new_state_vector = particles.state_vector
+
+    prediction = ParticleStatePrediction(new_state_vector,
+                                         timestamp=timestamp,
+                                         transition_model=transition_model)
+
     measurement_model = LinearGaussian(ndim_state=2, mapping=(0, 1), noise_covar=np.eye(2))
-    measurement = [Detection(state_vector=np.array([[5], [7]]),
-                             timestamp=timestamp, measurement_model=measurement_model)]
-    state_update = ParticleStateUpdate(None, SingleHypothesis(prediction=prediction,
-                                                              measurement=measurement,
-                                                              measurement_prediction=meas_pred),
-                                       particle_list=particles.particle_list, timestamp=timestamp)
-    regulariser = MCMCRegulariser()
+    measurement = Detection(state_vector=np.array([[5], [7]]),
+                            timestamp=timestamp, measurement_model=measurement_model)
+    hypothesis = SingleHypothesis(prediction=prediction,
+                                  measurement=measurement,
+                                  measurement_prediction=None)
+
+    state_update = Update.from_state(state=prediction,
+                                     hypothesis=hypothesis,
+                                     timestamp=timestamp+datetime.timedelta(seconds=1))
+    # A PredictedParticleState is used here as the point at which the regulariser is implemented
+    # in the updater is before the updated state has taken the updated state type.
+    state_update.weight = np.array([1/6, 5/48, 5/48, 5/48, 5/48, 5/48, 5/48, 5/48, 5/48])
+
+    if model_flag:
+        regulariser = MCMCRegulariser()
+    else:
+        regulariser = MCMCRegulariser(transition_model=transition_model)
 
     # state check
-    new_particles = regulariser.regularise(particles, state_update, measurement)
+    new_particles = regulariser.regularise(prediction, state_update)
     # Check the shape of the new state vector
     assert new_particles.state_vector.shape == state_update.state_vector.shape
     # Check weights are unchanged
     assert any(new_particles.weight == state_update.weight)
     # Check that the timestamp is the same
     assert new_particles.timestamp == state_update.timestamp
 
-    # list check
-    new_particles = regulariser.regularise(particles.particle_list, state_update.particle_list,
-                                           measurement)
-    # Check the shape of the new state vector
-    assert new_particles.state_vector.shape == state_update.state_vector.shape
-    # Check weights are unchanged
-    assert any(new_particles.weight == state_update.weight)
+    # list check3
+    with pytest.raises(TypeError) as e:
+        new_particles = regulariser.regularise(particles.particle_list,
+                                               state_update)
+    assert "Only ParticleState type is supported!" in str(e.value)
+    with pytest.raises(Exception) as e:
+        new_particles = regulariser.regularise(particles,
+                                               state_update.particle_list)
+    assert "Only ParticleState type is supported!" in str(e.value)
 
 
 def test_no_measurement():
@@ -92,7 +132,7 @@ def test_no_measurement():
                                        particle_list=particles.particle_list, timestamp=timestamp)
     regulariser = MCMCRegulariser()
 
-    new_particles = regulariser.regularise(particles, state_update, detections=None)
+    new_particles = regulariser.regularise(particles, state_update)
 
     # Check the shape of the new state vector
     assert new_particles.state_vector.shape == state_update.state_vector.shape

stonesoup/types/multihypothesis.py

@@ -1,5 +1,5 @@
-from collections.abc import Sized, Iterable, Container
-from typing import Sequence
+from collections.abc import Sequence
+import typing
 
 from .detection import MissedDetection
 from .numeric import Probability
@@ -10,13 +10,13 @@
 from ..types.prediction import Prediction
 
 
-class MultipleHypothesis(Type, Sized, Iterable, Container):
+class MultipleHypothesis(Type, Sequence):
     """Multiple Hypothesis base type
 
     A Multiple Hypothesis is a container to store a collection of hypotheses.
     """
 
-    single_hypotheses: Sequence[SingleHypothesis] = Property(
+    single_hypotheses: typing.Sequence[SingleHypothesis] = Property(
         default=None,
         doc="The initial list of :class:`~.SingleHypothesis`. Default `None` "
             "which initialises with empty list.")
@@ -119,7 +119,7 @@ def get_missed_detection_probability(self):
         return None
 
 
-class MultipleCompositeHypothesis(Type, Sized, Iterable, Container):
+class MultipleCompositeHypothesis(Type, Sequence):
     """Multiple composite hypothesis type
 
     A Multiple Composite Hypothesis is a container to store a collection of composite hypotheses.
@@ -128,7 +128,7 @@ class MultipleCompositeHypothesis(Type, Sized, Iterable, Container):
     redefined.
     """
 
-    single_hypotheses: Sequence[CompositeHypothesis] = Property(
+    single_hypotheses: typing.Sequence[CompositeHypothesis] = Property(
         default=None,
         doc="The initial list of :class:`~.CompositeHypothesis`. Default `None` which initialises "
             "with empty list.")

stonesoup/updater/particle.py

@@ -46,7 +46,12 @@ def update(self, hypothesis, **kwargs):
         : :class:`~.ParticleState`
             The state posterior
         """
-        predicted_state = copy.copy(hypothesis.prediction)
+
+        predicted_state = Update.from_state(
+            state=hypothesis.prediction,
+            hypothesis=hypothesis,
+            timestamp=hypothesis.prediction.timestamp
+        )
 
         if hypothesis.measurement.measurement_model is None:
             measurement_model = self.measurement_model
@@ -65,13 +70,12 @@ def update(self, hypothesis, **kwargs):
         if self.resampler is not None:
             predicted_state = self.resampler.resample(predicted_state)
 
-        return Update.from_state(
-            state=hypothesis.prediction,
-            state_vector=predicted_state.state_vector,
-            log_weight=predicted_state.log_weight,
-            hypothesis=hypothesis,
-            timestamp=hypothesis.measurement.timestamp,
-            )
+        if self.regulariser is not None:
+            prior = hypothesis.prediction.parent
+            predicted_state = self.regulariser.regularise(prior,
+                                                          predicted_state)
+
+        return predicted_state
 
     @lru_cache()
     def predict_measurement(self, state_prediction, measurement_model=None,
@@ -414,8 +418,12 @@ def update(self, hypotheses, **kwargs):
         # copy prediction
         prediction = hypotheses.single_hypotheses[0].prediction
 
-        updated_state = copy.copy(prediction)
-
+        # updated_state = copy.copy(prediction)
+        updated_state = Update.from_state(
+            state=prediction,
+            hypothesis=hypotheses,
+            timestamp=prediction.timestamp
+        )
         if any(hypotheses):
             detections = [single_hypothesis.measurement
                           for single_hypothesis in hypotheses.single_hypotheses]
@@ -463,16 +471,10 @@ def update(self, hypotheses, **kwargs):
         if any(hypotheses):
             # Regularisation
             if self.regulariser is not None:
-                regularised_parts = self.regulariser.regularise(updated_state.parent,
-                                                                updated_state,
-                                                                detections)
-                updated_state.state_vector = regularised_parts.state_vector
-
-        return Update.from_state(
-            updated_state,
-            timestamp=updated_state.timestamp,
-            hypothesis=hypotheses,
-        )
+                updated_state = self.regulariser.regularise(updated_state.parent,
+                                                            updated_state)
+
+        return updated_state
 
     @staticmethod
     def _log_space_product(A, B):