Bayesian Filtering Library
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32 #ifndef __MEASUREMENT_MODEL__
33 #define __MEASUREMENT_MODEL__
35 #include "../pdf/conditionalpdf.h"
40 #define NUMBER_OF_CONDITIONAL_ARGS 2
53 template<
typename MeasVar,
typename StateVar>
class MeasurementModel {
103 MeasVar
Simulate (
const StateVar& x,
const StateVar& s,
const SampleMthd sampling_method = SampleMthd::DEFAULT,
void * sampling_args = NULL);
116 MeasVar
Simulate (
const StateVar& x,
const SampleMthd sampling_method = SampleMthd::DEFAULT,
void * sampling_args = NULL);
137 #include "measurementmodel.cpp"
141 #endif // __MEASUREMENT_MODEL__
void MeasurementPdfSet(ConditionalPdf< MeasVar, StateVar > *pdf)
Set the MeasurementPDF.
virtual ~MeasurementModel()
Destructor.
bool _systemWithoutSensorParams
System with no sensor params??
int MeasurementSizeGet() const
Get Measurement Size.
bool SystemWithoutSensorParams() const
Number of Conditional Arguments.
ConditionalPdf< MeasVar, StateVar > * MeasurementPdfGet()
Get the MeasurementPDF.
Probability ProbabilityGet(const MeasVar &z, const StateVar &x, const StateVar &s)
Get the probability of a certain measurement.
ConditionalPdf< MeasVar, StateVar > * _MeasurementPdf
ConditionalPdf representing .
Class representing a probability (a double between 0 and 1)
MeasVar Simulate(const StateVar &x, const StateVar &s, const SampleMthd sampling_method=SampleMthd::DEFAULT, void *sampling_args=NULL)
Simulate the Measurement, given a certain state, and an input.
MeasurementModel(ConditionalPdf< MeasVar, StateVar > *Measurementpdf=NULL)
Constructor.