Source code for stonesoup.sensor.categorical

# -*- coding: utf-8 -*-

from scipy.stats import multinomial

from ..base import Property
from ..models.measurement.categorical import MarkovianMeasurementModel
from ..sensor.sensor import Sensor
from ..types.array import StateVector
from ..types.detection import TrueCategoricalDetection

[docs]class HMMSensor(Sensor): r"""Sensor model that observes a categorical state space and returns categorical measurements. Measurements are categorical distributions over a finite set of categories :math:`Z = \{\zeta^n|n\in \mathbf{N}, n\le N\}` (for some finite :math:`N`). """ measurement_model: MarkovianMeasurementModel = Property( doc="Measurement model to generate detection vectors from" ) @property def ndim_state(self): return self.measurement_model.ndim_state @property def ndim_meas(self): return self.measurement_model.ndim_meas
[docs] def measure(self, ground_truths, noise: bool = True, **kwargs): r"""Generate a categorical measurement for a given set of true categorical state. Parameters ---------- ground_truths: Set[:class:`~.CategoricalGroundTruthState`] A set of :class:`~.CategoricalGroundTruthState`. noise: bool Indicates whether measurement vectors are sampled from and the resultant measurement categories returned instead. These are discrete categories instead of a distribution over the measurement space. They are represented by N-tuples, with all components equal to 0, except at an index corresponding to the relevant category. For example :math:`e^k` indicates that the measurement category is :math:`\zeta^k`. If `False`, the resultant distribution is returned. Returns ------- Set[:class:`~.TrueCategoricalDetection`] A set of measurements generated from the given states. The timestamps of the measurements are set equal to that of the corresponding states that they were calculated from. Each measurement stores the ground truth path that it was produced from. """ detections = set() for truth in ground_truths: timestamp = truth.timestamp detection_vector = self.measurement_model.function(truth, noise=noise, **kwargs) if noise: # Sample from resultant distribution rv = multinomial(n=1, p=detection_vector.flatten()) detection_vector = StateVector(rv.rvs(size=1, random_state=None)) detection = TrueCategoricalDetection( state_vector=detection_vector, timestamp=timestamp, categories=self.measurement_model.measurement_categories, measurement_model=self.measurement_model, groundtruth_path=truth ) detections.add(detection) return detections