Monte Carlo Pdf: Sample based implementation of Pdf. More...
#include <mcpdf.h>
Public Member Functions | |
virtual MCPdf< T > * | Clone () const |
Clone function. | |
template<> | |
SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
template<> | |
SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
vector< double > & | CumulativePDFGet () |
Add a sample to the list. | |
template<> | |
ColumnVector | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
template<> | |
unsigned int | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
const vector< WeightedSample < T > > & | ListOfSamplesGet () const |
Get the list of weighted samples. | |
bool | ListOfSamplesSet (const vector< WeightedSample< T > > &list_of_samples) |
Set the list of weighted samples. | |
bool | ListOfSamplesSet (const vector< Sample< T > > &list_of_samples) |
Overloading: Set the list of Samples (uniform weights) | |
bool | ListOfSamplesUpdate (const vector< WeightedSample< T > > &list_of_samples) |
Update the list of samples (overloaded) | |
bool | ListOfSamplesUpdate (const vector< Sample< T > > &list_of_samples) |
Update the list of samples (overloaded) | |
template<> | |
MCPdf (unsigned int num_samples, unsigned int dimension) | |
template<> | |
MCPdf (const MCPdf &pdf) | |
MCPdf (unsigned int num_samples=0, unsigned int dimension=0) | |
Constructor. | |
MCPdf (const MCPdf< T > &) | |
copy constructor | |
unsigned int | NumSamplesGet () const |
Get number of samples. | |
void | NumSamplesSet (unsigned int num_samples) |
Set number of samples. | |
bool | SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const |
bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
const WeightedSample< T > & | SampleGet (unsigned int i) const |
Get one sample. | |
virtual | ~MCPdf () |
destructor | |
Protected Member Functions | |
void | CumPDFUpdate () |
After updating weights, we have to update the cumPDF. | |
bool | NormalizeWeights () |
Normalizing the weights. | |
bool | SumWeightsUpdate () |
STL-iterator for cumulative PDF list. | |
Protected Attributes | |
vector< double > | _CumPDF |
STL-iterator. | |
vector< WeightedSample< T > > | _listOfSamples |
STL-list containing the list of samples. | |
double | _SumWeights |
Sum of all weights: used for normalising purposes. | |
Private Attributes | |
SymmetricMatrix | _covariance |
T | _CumSum |
T | _diff |
Matrix | _diffsum |
vector< WeightedSample< T > >::iterator | _it_los |
vector< WeightedSample< T > > | _los |
T | _mean |
Monte Carlo Pdf: Sample based implementation of Pdf.
Class Monte Carlo Pdf: This is a sample based representation of a Pdf P(x), which can both be continu or discrete
BFL::MCPdf< T >::MCPdf | ( | unsigned int | num_samples = 0 , |
unsigned int | dimension = 0 |
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) |
Constructor.
num_samples | the number of samples this pdf has |
dimension | the dimension of these samples. You can use this parameter to avoid runtime memory allocation and |
virtual BFL::MCPdf< T >::~MCPdf | ( | ) | [virtual] |
destructor
BFL::MCPdf< T >::MCPdf | ( | const MCPdf< T > & | ) |
copy constructor
BFL::MCPdf< ColumnVector >::MCPdf | ( | unsigned int | num_samples, |
unsigned int | dimension | ||
) | [inline] |
BFL::MCPdf< ColumnVector >::MCPdf | ( | const MCPdf< T > & | ) | [inline] |
virtual MCPdf<T>* BFL::MCPdf< T >::Clone | ( | ) | const [virtual] |
Clone function.
Implements BFL::BFL::Pdf< T >.
SymmetricMatrix BFL::MCPdf< ColumnVector >::CovarianceGet | ( | ) | const [inline, virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented from BFL::BFL::Pdf< T >.
MatrixWrapper::SymmetricMatrix BFL::MCPdf< T >::CovarianceGet | ( | ) | const [virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented from BFL::BFL::Pdf< T >.
SymmetricMatrix BFL::MCPdf< unsigned int >::CovarianceGet | ( | ) | const [inline, virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented from BFL::BFL::Pdf< T >.
void BFL::MCPdf< T >::CumPDFUpdate | ( | ) | [protected] |
After updating weights, we have to update the cumPDF.
vector<double>& BFL::MCPdf< T >::CumulativePDFGet | ( | ) |
ColumnVector BFL::MCPdf< ColumnVector >::ExpectedValueGet | ( | ) | const [inline, virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented from BFL::BFL::Pdf< T >.
T BFL::MCPdf< T >::ExpectedValueGet | ( | ) | const [virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented from BFL::BFL::Pdf< T >.
unsigned int BFL::MCPdf< unsigned int >::ExpectedValueGet | ( | ) | const [inline, virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented from BFL::BFL::Pdf< T >.
const vector<WeightedSample<T> >& BFL::MCPdf< T >::ListOfSamplesGet | ( | ) | const |
Get the list of weighted samples.
bool BFL::MCPdf< T >::ListOfSamplesSet | ( | const vector< WeightedSample< T > > & | list_of_samples | ) |
Set the list of weighted samples.
list_of_samples | an STL-list containing the list of all weighted samples |
bool BFL::MCPdf< T >::ListOfSamplesSet | ( | const vector< Sample< T > > & | list_of_samples | ) |
Overloading: Set the list of Samples (uniform weights)
list_of_samples | an STL-list containing the list of all samples |
bool BFL::MCPdf< T >::ListOfSamplesUpdate | ( | const vector< WeightedSample< T > > & | list_of_samples | ) |
Update the list of samples (overloaded)
list_of_samples | the list of weighted samples |
bool BFL::MCPdf< T >::ListOfSamplesUpdate | ( | const vector< Sample< T > > & | list_of_samples | ) |
Update the list of samples (overloaded)
list_of_samples | the list of samples |
bool BFL::MCPdf< T >::NormalizeWeights | ( | ) | [protected] |
Normalizing the weights.
unsigned int BFL::MCPdf< T >::NumSamplesGet | ( | ) | const |
Get number of samples.
void BFL::MCPdf< T >::NumSamplesSet | ( | unsigned int | num_samples | ) |
Set number of samples.
num_samples | the number of samples offcourse :-) |
bool BFL::MCPdf< T >::SampleFrom | ( | Sample< T > & | one_sample, |
int | method = DEFAULT , |
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void * | args = NULL |
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) | const |
bool BFL::MCPdf< T >::SampleFrom | ( | vector< Sample< T > > & | list_samples, |
const unsigned int | num_samples, | ||
int | method = DEFAULT , |
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void * | args = NULL |
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) | const |
const WeightedSample<T>& BFL::MCPdf< T >::SampleGet | ( | unsigned int | i | ) | const |
Get one sample.
i | the ith sample |
bool BFL::MCPdf< T >::SumWeightsUpdate | ( | ) | [protected] |
STL-iterator for cumulative PDF list.
After updating weights, we have to recalculate the sum of weights
SymmetricMatrix BFL::MCPdf< T >::_covariance [mutable, private] |
vector<double> BFL::MCPdf< T >::_CumPDF [protected] |
T BFL::MCPdf< T >::_CumSum [mutable, private] |
T BFL::MCPdf< T >::_diff [mutable, private] |
Matrix BFL::MCPdf< T >::_diffsum [mutable, private] |
vector<WeightedSample<T> >::iterator BFL::MCPdf< T >::_it_los [mutable, private] |
vector<WeightedSample<T> > BFL::MCPdf< T >::_listOfSamples [protected] |
vector<WeightedSample<T> > BFL::MCPdf< T >::_los [mutable, private] |
T BFL::MCPdf< T >::_mean [mutable, private] |
double BFL::MCPdf< T >::_SumWeights [protected] |