Bayesian Filtering Library
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#include <weightedsample.h>
Public Member Functions | |
WeightedSample (int dimension=0) | |
Constructor. More... | |
virtual | ~WeightedSample () |
Destructor. | |
WeightedSample (const WeightedSample< T > &my_weighted_sample) | |
Copy constructor. | |
double | WeightGet () const |
Get the weight. More... | |
void | WeightSet (double weight) |
Set the weight. More... | |
WeightedSample< T > & | operator= (const WeightedSample< T > &my_sample) |
Operator =. | |
WeightedSample< T > & | operator= (const Sample< T > &my_sample) |
Turn sample into weighted one (weight = 1) | |
T & | ValueGet () |
Get the value of the Sample. | |
const T & | ValueGet () const |
Get the value of the Sample. | |
unsigned int | DimensionGet () const |
void | DimensionSet (unsigned int dim) |
void | ValueSet (const T &value) |
Set the value of the Sample. More... | |
Protected Attributes | |
double | Weight |
The weight. | |
T | Value |
The Sample Value. | |
Friends | |
template<typename S > | |
ostream & | operator<< (ostream &stream, WeightedSample< S > &mws) |
Print a weighted sample. More... | |
Template Class representing a sample of a continuous or discrete pdf, together with a weight denoting the relative importance of that sample. Inheritance is virtual (only important for a particular class hybridweightedsample (see the rob/actsens/cubeincorner CVS tree)
Definition at line 48 of file weightedsample.h.
WeightedSample | ( | int | dimension = 0 | ) |
Constructor.
dimension | of the ColumnVector for the continuous samples, number of discrete states for the discrete case |
Definition at line 93 of file weightedsample.h.
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inherited |
double WeightGet |
Get the weight.
Definition at line 104 of file weightedsample.h.
Referenced by WeightedSample< T >::operator=().
void WeightSet | ( | double | weight | ) |
Set the weight.
weight | the weight :-) |
Definition at line 109 of file weightedsample.h.
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friend |
Print a weighted sample.
stream | the stream to be returned |
mws | the weighted sample to be printed |
Definition at line 116 of file weightedsample.h.