# -*- coding: utf-8 -*-
from . import Hypothesiser
from ..base import Property
from ..types.multihypothesis import MultipleHypothesis
from ..types.prediction import (TaggedWeightedGaussianStatePrediction,
WeightedGaussianStatePrediction)
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")
[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")
hypotheses = list()
for component in components:
# Get hypotheses for that component for all measurements
component_hypotheses = self.hypothesiser.hypothesise(component,
detections,
timestamp,
**kwargs)
for hypothesis in component_hypotheses:
if isinstance(component, TaggedWeightedGaussianState):
hypothesis.prediction = \
TaggedWeightedGaussianStatePrediction(
tag=component.tag if component.tag != "birth"
else None,
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