Source code for stonesoup.hypothesiser.gaussianmixture

from . import Hypothesiser
from ..base import Property
from ..dataassociator.tree import DetectionKDTreeMixIn
from ..types.detection import MissedDetection
from ..types.multihypothesis import MultipleHypothesis
from ..types.prediction import (TaggedWeightedGaussianStatePrediction,
from ..types.state import TaggedWeightedGaussianState

[docs] class GaussianMixtureHypothesiser(Hypothesiser): """Gaussian Mixture Prediction Hypothesiser based on an underlying Hypothesiser Generates a list of :class:`MultipleHypothesis`, where each MultipleHypothesis in the list contains SingleHypotheses pertaining to an individual component-detection hypothesis """ hypothesiser: Hypothesiser = Property( doc="Underlying hypothesiser used to generate detection-target pairs") order_by_detection: bool = Property( default=False, doc="Flag to order the :class:`~.MultipleHypothesis` " "list by detection or component") def generate_hypotheses(self, components, detections, timestamp, **kwargs): return {component: self.hypothesiser.hypothesise( component, detections, timestamp, **kwargs) for component in components}
[docs] def hypothesise(self, components, detections, timestamp, **kwargs): """Form hypotheses for associations between Detections and Gaussian Mixture components. Parameters ---------- components : list of :class:`~.WeightedGaussianState` Components representing the state of the target space detections : set of :class:`~.Detection` Retrieved measurements timestamp : datetime.datetime Time of the detections/predicted states Returns ------- list of :class:`~.MultipleHypothesis` Each :class:`~.MultipleHypothesis` in the list contains a list of :class:`~SingleHypothesis` pertaining to the same Gaussian component unless order_by_detection is true, then they pertain to the same Detection. """ # Check to make sure all detections are obtained from the same time timestamps = set([detection.timestamp for detection in detections]) if len(timestamps) > 1: raise ValueError("All detections must have the same timestamp") components_hypotheses = self.generate_hypotheses( components, detections, timestamp, **kwargs) hypotheses = list() for component, component_hypotheses in components_hypotheses.items(): for hypothesis in component_hypotheses: if isinstance(component, TaggedWeightedGaussianState): # Ensure that a birth component without a measurement retains # the birth tag. This will prevent a track from being made if component.tag == component.BIRTH and \ isinstance(hypothesis.measurement, MissedDetection): tag = component.BIRTH elif component.tag == component.BIRTH: tag = None # a new tag will be made else: tag = component.tag hypothesis.prediction = \ TaggedWeightedGaussianStatePrediction( tag=tag, weight=component.weight, state_vector=hypothesis.prediction.state_vector, covar=hypothesis.prediction.covar, timestamp=hypothesis.prediction.timestamp ) else: hypothesis.prediction = WeightedGaussianStatePrediction( weight=component.weight, state_vector=hypothesis.prediction.state_vector, covar=hypothesis.prediction.covar, timestamp=hypothesis.prediction.timestamp ) # Create Multiple Hypothesis and add to list if len(component_hypotheses) > 0: hypotheses.append(MultipleHypothesis(component_hypotheses)) # Reorder list of MultipleHypothesis so that they are ordered # by detection, not component if self.order_by_detection: single_hypothesis_list = list() # Retrieve all single hypotheses for multiple_hypothesis in hypotheses: for single_hypothesis in multiple_hypothesis: single_hypothesis_list.append(single_hypothesis) reordered_hypotheses = list() # Get miss detected components miss_detections_hypothesis = MultipleHypothesis( [x for x in single_hypothesis_list if not x]) for detection in detections: # Create multiple hypothesis per detection detection_multiple_hypothesis = \ MultipleHypothesis(list([hypothesis for hypothesis in single_hypothesis_list if hypothesis.measurement == detection])) # Add to new list reordered_hypotheses.append(detection_multiple_hypothesis) # Add miss detected hypothesis to end reordered_hypotheses.append(miss_detections_hypothesis) # Assign reordered list to original list hypotheses = reordered_hypotheses return hypotheses
[docs] class GaussianMixtureKDTreeHypothesiser(DetectionKDTreeMixIn, GaussianMixtureHypothesiser): pass