from .base import Hypothesiser
from ..base import Property
from ..measures import ObservationAccuracy
from ..predictor.categorical import HMMPredictor
from ..types.detection import MissedDetection
from ..types.hypothesis import SingleProbabilityHypothesis
from ..types.multihypothesis import MultipleHypothesis
from ..types.numeric import Probability
from ..updater.categorical import HMMUpdater
[docs]class HMMHypothesiser(Hypothesiser):
r"""Hypothesiser based on categorical distribution accuracy.
This hypothesiser generates track predictions at detection times and scores each hypothesised
prediction-detection pair according to the accuracy of the corresponding measurement
prediction compared to the detection.
"""
predictor: HMMPredictor = Property(doc="Predictor used to predict tracks to detection times")
updater: HMMUpdater = Property(doc="Updater used to get measurement prediction")
prob_detect: Probability = Property(default=Probability(0.99),
doc="Target Detection Probability")
prob_gate: Probability = Property(default=Probability(0.95),
doc="Gate Probability - prob. gate contains true "
"measurement if detected")
[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
with N+1 detections (first detection is a 'MissedDetection'), each with an associated
accuracy (of prediction emission to measurement), considered the probability of the
hypothesis being true.
Parameters
----------
track: :class:`~.Track`
The track object to hypothesise on. Composed of :class:`~.CategoricalState` types.
detections: :class:`set`
A set of :class:`~.CategoricalDetection` 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:`~.SingleProbabilityHypothesis` objects
"""
hypotheses = list()
prediction = self.predictor.predict(track, timestamp=timestamp)
probability = Probability(1 - self.prob_detect * self.prob_gate)
hypotheses.append(
SingleProbabilityHypothesis(
prediction,
MissedDetection(timestamp=timestamp),
probability
))
for detection in detections:
prediction = self.predictor.predict(track, timestamp=detection.timestamp)
measurement_prediction = self.updater.predict_measurement(
predicted_state=prediction,
measurement_model=detection.measurement_model,
noise=False
)
probability = self.measure(measurement_prediction, detection)
probability = probability * self.prob_detect
probability = Probability(probability, log_value=False)
# True detection hypothesis
hypotheses.append(
SingleProbabilityHypothesis(
prediction,
detection,
probability,
measurement_prediction))
return MultipleHypothesis(hypotheses, normalise=False, total_weight=1)
@property
def measure(self):
return ObservationAccuracy()