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#include <BernsteinCopulaSmoother.hh>
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| BernsteinCopulaSmoother (const unsigned *nBinsInEachDim, unsigned dim, double marginTolerance, unsigned maxNormCycles, const CVCalc *cvCalc, bool becomeCvCalcOwner, const Matrix< int > &polyDegreesToUse) |
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bool | isConvolving () const |
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void | setConvolving (const bool b) |
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const std::vector< double > & | bandwidthValues () const |
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const std::vector< double > & | lastCVValues () const |
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const std::vector< double > & | lastRegularizedFractions () const |
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unsigned | getNFilters () const |
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unsigned | lastFilterChosen () const |
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| AbsCopulaSmootherBase (const unsigned *nBinsInEachDim, unsigned dim, double tolerance, unsigned maxNormCycles) |
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unsigned | dim () const |
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ArrayShape | copulaShape () const |
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void | setArchive (gs::AbsArchive *ar, const char *category=0) |
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template<class Point > |
const HistoND< double > & | smooth (unsigned long uniqueId, std::vector< OrderedPointND< Point > > &in, double *bandwidthUsed=0) |
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template<class Point > |
const HistoND< double > & | weightedSmooth (unsigned long uniqueId, const std::vector< std::pair< const Point *, double > > &in, const unsigned *dimsToUse, unsigned nDimsToUse, double *bandwidthUsed=0) |
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| CVCopulaSmoother (const unsigned *nBinsInEachDim, unsigned dim, double marginTolerance, unsigned maxNormCycles, double initialBw, const CVCalc *cvCalc, bool becomeCvCalcOwner, double cvRange, unsigned nCV, bool useConvolve) |
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| CVCopulaSmoother (const unsigned *nBinsInEachDim, unsigned dim, double marginTolerance, unsigned maxNormCycles, const std::vector< double > &bandwidthValues, const CVCalc *cvCalc, bool becomeCvCalcOwner, bool useConvolve) |
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void | setFilter (unsigned i, Filter *filter) |
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| AbsCVCopulaSmoother (const unsigned *nBinsInEachDim, unsigned dim, double marginTolerance, unsigned maxNormCycles, double initialBw, double cvRange, unsigned nCV, bool useConvolve) |
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| AbsCVCopulaSmoother (const unsigned *nBinsInEachDim, unsigned dim, double marginTolerance, unsigned maxNormCycles, const std::vector< double > &bandwidthValues, bool useConvolve) |
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This class builds multivariate copula filters which are tensor products of univariate Bernstein polynomial filters
◆ BernsteinCopulaSmoother()
npstat::BernsteinCopulaSmoother::BernsteinCopulaSmoother |
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const unsigned * |
nBinsInEachDim, |
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unsigned |
dim, |
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double |
marginTolerance, |
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unsigned |
maxNormCycles, |
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const CVCalc * |
cvCalc, |
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bool |
becomeCvCalcOwner, |
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const Matrix< int > & |
polyDegreesToUse |
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) |
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Constructor arguments are as follows:
nBinsInEachDim – number of copula bins in each dimension
dim – copula dimensionality
marginTolerance – tolerance for the margin to be uniform
maxNormCycles – max number of copula normalization cycles
cvCalc – calculator for the quantity being optimized in the cross validation process. May be NULL in which case cross validation will not be used.
becomeCvCalcOwner – tells us whether we should destroy cvCalc in our own destructor
polyDegreesToUse – Bernstein polynomial degrees to try. The row number of the matrix corresponds to the "trial" number and the column number gives the Bernstein polynomial degree for the corresponding dimension.
The documentation for this class was generated from the following file:
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