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npstat::AbsUnfoldND Class Referenceabstract
Inheritance diagram for npstat::AbsUnfoldND:
Member Function Documentation◆ clearInitialApproximation()
Clear the initial approximation to the unfolded solution ◆ getFilter()
Retrieve the smoothing filter used ◆ getInitialApproximation()
Return the initial approximation to the unfolded solution ◆ getObservedShape()
Shape of the expected observed input ◆ getUnfoldedShape()
Shape of the expected unfolded output ◆ probDelta()
L1 distance between two unnormalized distributions ◆ setFilter()
Set the smoothing filter used. The filter will not be copied. The user must ensure that the filter exists while this object is in use. ◆ setInitialApproximation()
Set the initial approximation to the unfolded solution ◆ unfold()
Method to be implemented by derived classes. The covariance matrix of observations should assume linear ordering of the observed data, per ordering by the "ArrayND" class. If the "observationCovarianceMatrix" pointer is NULL, the matrix should be constructed internally, assuming Poisson or multinomial statistics. The "unfoldedCovarianceMatrix" pointer can be NULL as well in which case the corresponding matrix should not be calculated. This function should return "true" on success, "false" on failure. Implemented in npstat::SmoothedEMUnfoldND. ◆ useConvolutions()
Switch between using filtering or convolution ◆ usingConvolutions()
Check if the filter should use "filter" or "convolve" method ◆ validateUnfoldedShape()
This function will throw the "std::invalid_argument" exception if the dimensions are incompatible with those of the response matrix The documentation for this class was generated from the following file:
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