Source code for stonesoup.hypothesiser.distance

# -*- coding: utf-8 -*-
from .base import Hypothesiser
from ..base import Property
from ..measures import Measure
from ..predictor import Predictor
from ..types.detection import MissedDetection
from ..types.hypothesis import SingleDistanceHypothesis
from ..types.multihypothesis import MultipleHypothesis
from ..updater import Updater

[docs]class DistanceHypothesiser(Hypothesiser): """Prediction Hypothesiser based on a Measure Generate track predictions at detection times and score each hypothesised prediction-detection pair using the distance of the supplied :class:`~.Measure` class. """ predictor: Predictor = Property(doc="Predict tracks to detection times") updater: Updater = Property(doc="Updater used to get measurement prediction") measure: Measure = Property( doc="Measure class used to calculate the distance between two states.") missed_distance: float = Property( default=float('inf'), doc="Distance for a missed detection. Default is set to infinity") include_all: bool = Property( default=False, doc="If `True`, hypotheses beyond missed distance will be returned. Default `False`")
[docs] def hypothesise(self, track, detections, timestamp, **kwargs): """ Evaluate and return all track association hypotheses. For a given track and a set of N available detections, return a MultipleHypothesis object with N+1 detections (first detection is a 'MissedDetection'), each with an associated distance measure.. Parameters ---------- track : Track The track object to hypothesise on detections : set of :class:`~.Detection` The available detections timestamp : datetime.datetime A timestamp used when evaluating the state and measurement predictions. Note that if a given detection has a non empty timestamp, then prediction will be performed according to the timestamp of the detection. Returns ------- : :class:`~.MultipleHypothesis` A container of :class:`~SingleDistanceHypothesis` objects """ hypotheses = list() # Common state & measurement prediction prediction = self.predictor.predict(track, timestamp=timestamp, **kwargs) # Missed detection hypothesis with distance as 'missed_distance' hypotheses.append( SingleDistanceHypothesis( prediction, MissedDetection(timestamp=timestamp), self.missed_distance )) # True detection hypotheses for detection in detections: # Re-evaluate prediction prediction = self.predictor.predict( track, timestamp=detection.timestamp, **kwargs) # Compute measurement prediction and distance measure measurement_prediction = self.updater.predict_measurement( prediction, detection.measurement_model, **kwargs) distance = self.measure(measurement_prediction, detection) if self.include_all or distance < self.missed_distance: # True detection hypothesis hypotheses.append( SingleDistanceHypothesis( prediction, detection, distance, measurement_prediction)) return MultipleHypothesis(sorted(hypotheses, reverse=True))