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npstat::UnfoldingBandwidthScanner1D Class Reference
Constructor & Destructor Documentation◆ UnfoldingBandwidthScanner1D()
The constructor arguments are as follows: unfold – An instance of AbsUnfold1D class. observed, lenObserved – Observed data and its length. Can be modified later using "setObservedData" method. observationCovariance – Covariance matrix for the observed values (can be NULL). If provided, this matrix should be symmetric and positive-definite. An internal copy will be made. oracle, lenOracle – The "correct" unfolded distribution for use in various comparisons with unfolded results. If lenOracle == 0, this distribution will be considered unknown (in this case "oracle" pointer can be NULL), and the corresponding comparisons will not be made. symbetaPower – This code will create smoothing filters maxDegree for the unfolded space using the xMinUnfolded function "symbetaLOrPEFilter1D". xMaxUnfolded These parameters will be passed to filterBoundaryMethod the "symbetaLOrPEFilter1D" call. 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. foldedComparators – The comparator objects to use in order to compare observed data with its fit. oracleComparators – The comparator objects to use in order to compare unfolded data with the oracle (in case the oracle is actually provided). Member Function Documentation◆ addNamesWithPrefix()
Add a number of names to the given vector of strings ◆ 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 ◆ getOracleData()
Oracle data. Empty vector is returned if the oracle was not provided in the constructor. ◆ lastBandwidth()
Last bandwidth 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. ◆ observedSize()
Length of the observed data vector ◆ process()
Perform unfolding with the given bandwidth. This method returns "true" on success and "false" on failure. ◆ processAICcBandwidth()
Find the optimal bandwidth according to the AICc criterion and process that bandwidth. The arguments "bwmin", "bwmax", and "nsteps" specify the initial grid in the log space on which bandwidth search is performed ("nsteps" must be larger than 2). "startBw" is the bandwidth from which to start the search and "startingFactor" (>1.0) is an aproximate stepping factor for the beginning of the search. The argument "useEntropicNDoF" specifies whether the code should calculate effective NDoF for AICc using eigenspectrum entropy (if "true") or the trace (if "false") of the relevant matrix. Correspondingly, the code will minimize AICcEntropic or AICcTrace. As the intermediate results are memoized (filters, degrees of freedom, etc), it is best to call this function on different observed data with the same values of "bwmin", "bwmax", and "nsteps" arguments. This function returns the status of the search for the minimum AICc. The results of the "process" call for the corresponding bandwidth can be retrieved in the usual manner, using methods "ntuplize", "lastUnfoldingStatus", "lastBandwidth", "unfoldedResult", etc. ◆ 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 length must be compatible with the length 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 vector of observations. Array size must be compatible with that 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. ◆ smoothedOracleData()
Return oracle data smoothed with the last processed bandwidth. If the bias was set, it was added to this data. ◆ smoothingNDoF()
Function forwarded from AbsUnfold1D ◆ unfoldedCovariance()
Last unfolded covariance matrix ◆ unfoldedResult()
Last unfolded distribution ◆ unfoldedSize()
Length of the unfolded data vector ◆ 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 – Last bandwidth processed. Will be set to -1.0 if processing has failed and to -2.0 if "ntuplize" was called before the first call to "process". In case bandwidth is negative, values of all other variables are undefined (might simply remain unchanged from the previous "process" call). Always check that the bandwidth is non-negative before looking at other variables. 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 oracle 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. filterNDoFEntropic – Effective number of degrees of filterNDoFTrace freedom for the smoothing matrix, (defined for S * S^T). "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. foldingNDoFEntropic – Effective NDoF for the "folding" foldingNDoFTrace matrix F = K*S (defined for F * F^T. K is the response matrix here). F "folds" the unsmoothed distribution in the expectation-maximization iterations. unfoldedNDoFEntropic – Effective NDoF for the unfolding unfoldedNDoFTrace 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. 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. foldedDistance_N – With N = 0, 1, ..., up to foldedPValue_N foldedComparators.size() - 1. Distances and p-values between the observations and their fitted values, calculated by distribution comparators provided in the "foldedComparators" argument of the constructor. *oracleDistance_N – With N = 0, 1, ..., up to *oraclePValue_N oracleComparators.size() - 1. Distances and p-values between oracle data and unfolded values, calculated by distribution comparators provided in the "oracleComparators" argument of the constructor. *smoothedOracleDistance_N – With N = 0, 1, ..., up to *smoothedOraclePValue_N oracleComparators.size() - 1. Distances and p-values between smoothed oracle data and unfolded values, calculated by distribution comparators provided in the "oracleComparators" argument of the constructor. The documentation for this class was generated from the following file: Generated by  1.9.1
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