npstat::UnfoldingBandwidthScannerND Class Reference
Constructor & Destructor Documentation◆ UnfoldingBandwidthScannerND()
The constructor arguments are as follows: unfold – An instance of AbsUnfoldND class. filterParameters – Specifications how to build the smoothing filters for each dimension of the unfolded data. The corresponding values will be eventually passed to the "symbetaLOrPEFilter1D" call. The number of elements in this vector must be equal to the rank of the response matrix used by the "unfold" argument. observed – Observed data. Can be modified later using "setObservedData" method. Array shape, however, can not change. nDoFCorrectionFactor – The correction factor to use for determining the number of effective parameters in the fit. If this argument is positive, it will be multiplied by the number of parameters determined by the standard procedure (so set it to 1.0 in order not to apply any correction). If this argument is 0 or negative, the correction factor will be calculated as the fraction of bins filled in the observed data. observationCovariance – Covariance matrix for the observed values. If not NULL, this matrix should be symmetric and positive-definite. An internal copy will be made. oracle – The "correct" unfolded distribution for use in various comparisons with unfolded results. If oracle is NULL, this distribution will be considered unknown, and the corresponding comparisons will not be made. Member Function Documentation◆ clearBias()
Clear bias data ◆ clearInitialApproximation()
Clear the initial approximation to the unfolded solution ◆ covEigenValues()
Return covariance matrix eigenvalues (in the decreasing order) ◆ eigenDeltas()
Return eigenvector differences divided by sigma ◆ getBias()
Return the bias data provided by the last "setBias" call ◆ getInitialApproximation()
Return the initial approximation to the unfolded solution ◆ getObservedData()
Return the observed data ◆ lastBandwidthValues()
Last bandwidth values processed ◆ lastUnfoldingStatus()
Status of the last unfolding call ("true" means success) ◆ nDoFCorrectionFactor()
Correction factor for the number of degrees of freedom ◆ ntuplize()
Write out produced variables into a common buffer. The order of the values will be consistent with the names returned by the "variableNames" method. The function returns the number of variables filled. ◆ observedDim()
Dimensionality of the observed space ◆ process()
Perform unfolding with the given bandwidth values. This method returns "true" on success and "false" on failure. ◆ responseMatrix()
Response matrix ◆ setBias()
Set the bias (this is useful for various studies of uncertainties). If set, this bias will be added to the smoothed oracle data. The dimensions must be compatible with those of the unfolded result. ◆ setInitialApproximation()
Set the initial approximation to the unfolded solution ◆ setNDoFCorrectionFactor()
Set the correction factor for the number of degrees of freedom ◆ setObservedData()
Change the array of observations. Array dimensions must be compatible with those given in the constructor. The pointer to the covariance matrix of observations can be NULL. If provided, this matrix should be symmetric and positive-definite. ◆ smoothedOracle()
Return oracle data smoothed with the last processed bandwidth. If the bias was set, it was added to this data. ◆ unfoldedCovariance()
Last unfolded covariance matrix ◆ unfoldedDim()
Dimensionality of the unfolded space ◆ unfoldedResult()
Last unfolded distribution ◆ usingConvolutions()
Are we using convolutions with our filters? ◆ variableCount()
Number of variables calculated by the "process" method. See the description of "variableNames" method for details. ◆ variableNames()
Names of the variables calculated by calling the "process" method. The meaning of the variables is described below. Variables which have * in front of their names will have meaningful values only in case the known "oracle" distribution was provided in the constructor. bandwidth_N – With N = 0, 1, ..., up to filterParameters.size() - 1. Last bandwidth set processed. All these values will be set to -2.0 if "ntuplize" was called before the first call to "process". foldedSum – Sum of the Poisson means fitted to the observations. unfoldedSum – Sum of the unfolded values. *smoothedOracleSum – Sum of the smoothed oracle data. foldedLogli – Poisson log-likelihood for the observations assuming that the Poisson means are given by the fit. If the covariance matrix of the observations was provided by the user, this variable will be set to -chi^2/2 instead. *unfoldedLogli – Poisson log-likelihood for the unfolded distribution assuming that the Poisson means are given by the oracle data. *unfoldedISE – Integrated squared error for the unfolded distribution w.r.t. the oracle (both distributions are normalized to 1). This variable is the easiest one to check in order to find out whether an oracle was provided (its value will be negative if it wasn't). *unfoldedDiagChisq – Chi-squared of the unfolded distribution w.r.t. the oracle using only the diagonal elements of the unfolding covariance matrix. *smoothedUnfoldedLogli – Poisson log-likelihood for the unfolded distribution assuming that the Poisson means are given by the smoothed racle data. *smoothedUnfoldedISE – Integrated squared error for the unfolded distribution w.r.t. the smoothed oracle (both distributions are normalized to 1). *smoothedUnfoldedDiagChisq – Chi-squared of the unfolded distribution w.r.t. the smoothed oracle using only the diagonal elements of the covariance matrix. modelNDoFEntropic – Effective NDoF for the matrix modelNDoFTrace H = K*E*(K*E)^T which plays a role similar to the hat matrix in regression. Here, E is the error propagation matrix for unfolding uniform observed distribution. "Entropic" means that the exponent of the eigenspectrum entropy is used to define effective NDoF, and "Trace" means that the ratio of the matrix trace to the largest eigenvalue is used. AICcEntropic – AIC (Akaike information criterion) AICcTrace with a correction for the finite sample size. Calculated using "foldedLogli" and corresponding "modelNDoF". smoothingNormfactor – Normalization factor applied during the last smoothing procedure. integratedVariance – Product of the covariance matrix trace and the bin width in the unfolded space. nIterations – Number of iterations used to process this bandwidth (e.g., by the expectation-maximization method). unfoldingStatus – Status returned by the "unfold" call of the unfolding object. The documentation for this class was generated from the following file: Generated by  1.9.1
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