optiSLang 3D Postprocessing Script API 2025 R1

Statistics

Last update: 16.07.2025

Perform statistical analysis on data objects. More...

Classes

class  ComputeCoefficientOfDetermination< TYPE >
 a struct which collects settings for computing the Coefficient Of Determination More...
 
class  ComputeCorrelation< TYPE >
 a struct which collects settings for computeCorrelation More...
 
class  ComputeCoV< TYPE >
 a struct which collects settings for ComputeCoV More...
 
class  ComputeMax< TYPE >
 a struct which collects settings for ComputeMax More...
 
class  ComputeMaxProbability< TYPE >
 a struct which collects settings for ComputeMaxProbability More...
 
class  ComputeMean< TYPE >
 a struct which collects settings for ComputeMean More...
 
class  ComputeMeanMissing< TYPE >
 a struct which collects settings for ComputeMean More...
 
class  ComputeMeanPlusSigma< TYPE >
 a struct which collects settings for computing 'mean+ k * sigma' More...
 
class  ComputeMin< TYPE >
 a struct which collects settings for ComputeMin More...
 
class  ComputeMinProbability< TYPE >
 a struct which collects settings for ComputeMinProbability More...
 
class  ComputeProbabilitySigmaInterval< TYPE >
 a struct which collects settings for probability of sigma intervals, i.e. of P( mean + l*sigma <= X < mean + u*sigma) More...
 
class  ComputeQualityCapabilityCp< TYPE >
 a struct which collects settings for QCS/Cp More...
 
class  ComputeQualityCapabilityCpk< TYPE >
 a struct which collects settings for QCS/Cpk More...
 
class  ComputeQuantile< TYPE >
 a struct which collects settings for QCS/Cpk More...
 
class  ComputeQuantileInverse< TYPE >
 a struct which collects settings for inverse quantile computation (not-exceedance probability) More...
 
class  ComputeRange< TYPE >
 a struct which collects settings for ComputeRange More...
 
class  ComputeRelativeError< TYPE >
 Computes the relative accuracy between two quantities given using $\epsilon = \frac{x_{\mbox{other}}}{x_{\mbox{ref}}}$ If set, the relative error is computed using $\epsilon = \frac{x_{\mbox{other}}-x_{\mbox{ref}}}{max_i |x_{\mbox{ref}}|_i }$. More...
 
class  ComputeRPCA< TYPE >
 Perform Robust Principal Component Analysis While traditional PCA is very sensitive to data corruption or outliers, RPCA is, as its name implies, robust to data corruption under surprisingly broad conditions. RPCA attempts to split a given matrix M into two matrices S and L: M = L + S where L is a low-rank matrix and S is a sparse matrix of random errors (of arbitrary magnitude and random sign). In the context of SoS, each column vector of M might be a particular field design. Without any prior knowledge about outliers, RPCA is then able to separate correlations between field designs (L) from outliers (S). Security camera footage is a good example, following the M = L + S data model: L represents the slowly changing background, while S represents walking people. See https://arxiv.org/abs/0912.3599 for more examples and a precise definition of the conditions, for RPCA to deliver good results. By default, ComputeRPCA creates two new quantity idents named RPCA[] (L) and RPCAError[] (S). Disable the creation of RPCAError by setting the createRPCAError member variable to false. The algorithm attempts to recover L and S by running the Principal Component Pursuit bi-objective optimization program:

\[ //! \text{minimize} ||L||_* + \lambda ||S||_1 //! \text{subject to} L + S = M //! \]

where $ ||L||_* $ denotes the nuclear norm of $ L $ (i.e. the sum of the singular values of $ L $) and $ ||L||_1 = \sum_{ij}|S_{ij}| $ denotes the $ l_1 $-norm of $ S $ seen as a long vector. Recommended value: $ \lambda = 1/\sqrt{max(dim(M))}$. More...

 
class  ComputeStddev< TYPE >
 a struct which collects settings for ComputeStddev More...
 
class  ComputeStdErrorOfMean< TYPE >
 a struct which collects settings for computing the standard error of mean estimator More...
 
class  ComputeStdErrorOfVariance< TYPE >
 a struct which collects settings for computing the standard error of the variance estimator More...
 
class  ComputeVariance< TYPE >
 a struct which collects settings for ComputeVariance More...
 

Functions

 template (ComputeStdErrorOfMeanNode) ComputeStdErrorOfMean< NODE_DATA >
 
 template (ComputeStdErrorOfVarianceNode) ComputeStdErrorOfVariance< NODE_DATA >
 
 template (ComputeMeanNode) ComputeMean< NODE_DATA >
 
 template (ComputeMeanMissingNode) ComputeMeanMissing< NODE_DATA >
 
 template (ComputeCoVNode) ComputeCoV< NODE_DATA >
 
 template (ComputeMinNode) ComputeMin< NODE_DATA >
 
 template (ComputeMinProbabilityNode) ComputeMinProbability< NODE_DATA >
 
 template (ComputeStdErrorOfMeanElement) ComputeStdErrorOfMean< ELEMENT_DATA >
 
 template (ComputeMeanElement) ComputeMean< ELEMENT_DATA >
 
 template (ComputeStddevNode) ComputeStddev< NODE_DATA >
 
 template (ComputeVarianceNode) ComputeVariance< NODE_DATA >
 
 template (ComputeStdErrorOfVarianceElement) ComputeStdErrorOfVariance< ELEMENT_DATA >
 
 template (ComputeMeanMissingElement) ComputeMeanMissing< ELEMENT_DATA >
 
 template (ComputeMeanMissingScalar) ComputeMeanMissing< SCALAR_DATA >
 
 template (ComputeMinElement) ComputeMin< ELEMENT_DATA >
 
 template (ComputeVarianceElement) ComputeVariance< ELEMENT_DATA >
 
 template (ComputeMinProbabilityElement) ComputeMinProbability< ELEMENT_DATA >
 
 template (ComputeCoVElement) ComputeCoV< ELEMENT_DATA >
 
 template (ComputeMaxNode) ComputeMax< NODE_DATA >
 
 template (ComputeMaxProbabilityNode) ComputeMaxProbability< NODE_DATA >
 
 template (ComputeStdErrorOfMeanScalar) ComputeStdErrorOfMean< SCALAR_DATA >
 
 template (ComputeStddevElement) ComputeStddev< ELEMENT_DATA >
 
 template (ComputeMeanScalar) ComputeMean< SCALAR_DATA >
 
 template (ComputeStdErrorOfVarianceScalar) ComputeStdErrorOfVariance< SCALAR_DATA >
 
 template (ComputeCoVScalar) ComputeCoV< SCALAR_DATA >
 
 template (ComputeVarianceScalar) ComputeVariance< SCALAR_DATA >
 
 template (ComputeMinScalar) ComputeMin< SCALAR_DATA >
 
 template (ComputeStddevScalar) ComputeStddev< SCALAR_DATA >
 
 template (ComputeMaxElement) ComputeMax< ELEMENT_DATA >
 
 template (ComputeMaxProbabilityElement) ComputeMaxProbability< ELEMENT_DATA >
 
 template (ComputeMaxScalar) ComputeMax< SCALAR_DATA >
 
 template (ComputeRangeNode) ComputeRange< NODE_DATA >
 
 template (ComputeQuantileInverseNode) ComputeQuantileInverse< NODE_DATA >
 
 template (ComputeRangeElement) ComputeRange< ELEMENT_DATA >
 
 template (ComputeQuantileInverseElement) ComputeQuantileInverse< ELEMENT_DATA >
 
 template (ComputeRangeScalar) ComputeRange< SCALAR_DATA >
 
 template (ComputeQuantileInverseScalar) ComputeQuantileInverse< SCALAR_DATA >
 
 template (ComputeQuantileNode) ComputeQuantile< NODE_DATA >
 
 template (ComputeMeanPlusSigmaNode) ComputeMeanPlusSigma< NODE_DATA >
 
 template (ComputeQuantileElement) ComputeQuantile< ELEMENT_DATA >
 
 template (ComputeQuantileScalar) ComputeQuantile< SCALAR_DATA >
 
 template (ComputeMeanPlusSigmaElement) ComputeMeanPlusSigma< ELEMENT_DATA >
 
 template (ComputeProbabilitySigmaIntervalNode) ComputeProbabilitySigmaInterval< NODE_DATA >
 
 template (ComputeQualityCapabilityCpkNode) ComputeQualityCapabilityCpk< NODE_DATA >
 
 template (ComputeMeanPlusSigmaScalar) ComputeMeanPlusSigma< SCALAR_DATA >
 
 template (ComputeQualityCapabilityCpNode) ComputeQualityCapabilityCp< NODE_DATA >
 
 template (ComputeProbabilitySigmaIntervalElement) ComputeProbabilitySigmaInterval< ELEMENT_DATA >
 
 template (ComputeProbabilitySigmaIntervalScalar) ComputeProbabilitySigmaInterval< SCALAR_DATA >
 
 template (ComputeQualityCapabilityCpElement) ComputeQualityCapabilityCp< ELEMENT_DATA >
 
 template (ComputeQualityCapabilityCpkElement) ComputeQualityCapabilityCpk< ELEMENT_DATA >
 
 template (ComputeRPCANode) ComputeRPCA< NODE_DATA >
 
 template (ComputeQualityCapabilityCpScalar) ComputeQualityCapabilityCp< SCALAR_DATA >
 
 template (ComputeQualityCapabilityCpkScalar) ComputeQualityCapabilityCpk< SCALAR_DATA >
 
 template (ComputeRPCAElement) ComputeRPCA< ELEMENT_DATA >
 
 template (ComputeCoefficientOfDeterminationNode) ComputeCoefficientOfDetermination< NODE_DATA >
 
 template (ComputeCoefficientOfDeterminationElement) ComputeCoefficientOfDetermination< ELEMENT_DATA >
 
 template (ComputeCoefficientOfDeterminationScalar) ComputeCoefficientOfDetermination< SCALAR_DATA >
 
 template (ComputeCorrelationNode) ComputeCorrelation< NODE_DATA >
 
 template (ComputeCorrelationElement) ComputeCorrelation< ELEMENT_DATA >
 
 template (ComputeCorrelationScalar) ComputeCorrelation< SCALAR_DATA >
 
 template (ComputeRelativeNodeError) ComputeRelativeError< NODE_DATA >
 
 template (ComputeRelativeElementError) ComputeRelativeError< ELEMENT_DATA >
 
 template (ComputeRelativeScalarError) ComputeRelativeError< SCALAR_DATA >
 

Detailed Description

Perform statistical analysis on data objects.

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