Source code for stonesoup.hypothesiser.composite

from collections import defaultdict
from typing import Sequence

from .base import Hypothesiser
from ..base import Property
from ..predictor.composite import CompositePredictor
from ..types.hypothesis import CompositeProbabilityHypothesis
from ..types.multihypothesis import MultipleHypothesis

[docs] class CompositeHypothesiser(Hypothesiser): """Composite hypothesiser type A composition of ordered sub-hyposisers (:class:`~.Hypothesiser`). Hypothesises each sub-state of a track-detection pair using a corresponding sub-hypothesiser. """ sub_hypothesisers: Sequence[Hypothesiser] = Property( doc="Sequence of sub-hypothesisers comprising the composite hypothesiser. Must not be " "empty. These must be hypothesisers that return probability-weighted hypotheses, in " "order for composite hypothesis weights to be calculated.") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if len(self.sub_hypothesisers) == 0: raise ValueError("Cannot create an empty composite hypothesiser") if any(not isinstance(sub_hypothesiser, Hypothesiser) for sub_hypothesiser in self.sub_hypothesisers): raise ValueError("All sub-hypothesisers must be a hypothesiser type") # create predictor as composition of sub-hypothesisers' predictors sub_predictors = list() for sub_hypothesiser in self.sub_hypothesisers: sub_predictors.append(sub_hypothesiser.predictor) self.predictor = CompositePredictor(sub_predictors)
[docs] def hypothesise(self, track, detections, timestamp): """Evaluate and return all track association hypotheses. For a given track and a set of N available detections, return a MultipleHypothesis object composed of N+1 :class:`~.CompositeProbabilityHypothesis` (including a null hypothesis), each with an associated probability. Parameters ---------- track: :class:`~.Track` The track object to hypothesise on, existing in a composite state space detections: :class:`set` A set of :class:`~CompositeDetection` objects, representing the available detections. timestamp: :class:`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:`~CompositeHypothesis` objects, each of which containing a sequence of :class:`~.SingleHypothesis` objects """ all_hypotheses = list() # Common state & measurement prediction prediction = self.predictor.predict(track, timestamp=timestamp) null_sub_hypotheses = list() # as each detection is composite, it will have a set of sub-hypotheses paired to it detections_hypotheses = defaultdict(list) # loop over the sub-states of the track and sub-hypothesisers for sub_state_index, (sub_state, sub_hypothesiser) in enumerate( zip(track[-1].sub_states, self.sub_hypothesisers)): # store all sub-detections produced from sub-state index i sub_state_detections = set() # need way to get whole composite detections back from their i-th components # create dictionary, keyed by the i-th components, where the value is whole detection # will keep track of all detections that have an i-th component relevant_detections = dict() for detection in detections: try: sub_detection_index = detection.mapping.index(sub_state_index) except ValueError: continue sub_detection = detection[sub_detection_index] sub_state_detections.add(sub_detection) relevant_detections[sub_detection] = detection # get all hypotheses for the i-th component, considering i-th component of track state sub_hypotheses = sub_hypothesiser.hypothesise(sub_state, sub_state_detections, timestamp=timestamp) # get the set of single hypotheses back sub_hypotheses = sub_hypotheses.single_hypotheses # Store sub-null-hypothesis for detections that didn't have i-th component sub_null_hypothesis = None while sub_hypotheses: sub_hypothesis = sub_hypotheses.pop() if not sub_hypothesis: sub_null_hypothesis = sub_hypothesis else: # get whole detection back, using relevant_detection = relevant_detections[sub_hypothesis.measurement] # Add hypothesis to detection's hypotheses container detections_hypotheses[relevant_detection].append(sub_hypothesis) # For detections without i-th component, use sub-missed detection hypothesis for detection in detections - set(relevant_detections.values()): detections_hypotheses[detection].append(sub_null_hypothesis) # Add sub-null-hypothesis to composite null hypothesis null_sub_hypotheses.append(sub_null_hypothesis) # add a composite hypothesis for each detection for detection in detections: # get all sub-hypotheses for detection sub_hypotheses = detections_hypotheses[detection] all_hypotheses.append(CompositeProbabilityHypothesis(prediction=prediction, measurement=detection, sub_hypotheses=sub_hypotheses)) # add null-hypothesis all_hypotheses.append(CompositeProbabilityHypothesis(prediction=prediction, measurement=None, sub_hypotheses=null_sub_hypotheses)) return MultipleHypothesis(all_hypotheses, normalise=True, total_weight=1)
def __contains__(self, item): return self.sub_hypothesisers.__contains__(item) def __getitem__(self, index): """Can be indexed as a list, or sliced, in which case a new composite hypothesiser will be created from the sub-list of sub-hypothesisers.""" if isinstance(index, slice): return self.__class__(self.sub_hypothesisers.__getitem__(index)) return self.sub_hypothesisers.__getitem__(index) def __iter__(self): return self.sub_hypothesisers.__iter__() def __len__(self): return self.sub_hypothesisers.__len__()