NeurApp 1.1
NeurApp - software for exploring approximation by artificial neural networks on functions of one or two variables.

IG::Neural::NeuralTrainingParameters::ComparerBase Class Reference

Base comparer class (implementation of the IComparer<NeuralTrainingParameters> interface) for conmparing objects of type NeuralTrainingParameters More...

List of all members.

Protected Member Functions

double OutputNorm (IVector vec)

Protected Attributes

IVector _outputScalingLengths
bool _compareMinError = false
int _numLastErrors = 1
int _numBundles = 0
bool _compareByTrainingRmsError = true
bool _compareByTrainingMaxError = false
bool _compareByVerificationRmsError = false
bool _compareByVerificationMaxError = false

Properties

IVector OutputScalingLengths [get, set]
 Vector of scaling lengths for calculation of weighted norms.
bool CompareMinError [get, set]
 Whether the min error in convergence table is used for comparison when errors are compared. If false then mean value of last errors is used.
int NumLastErrors [get, set]
 Number of last errors in convergence list for calculataing the mean value of error. The default valeu is 1 which represent the last error in the converhence list.
int NumBundles [get, set]
 Number of bundles where sorting of convergences stars. The default valeu is 0 which represent normal sorting after the training is done.
bool CompareByTrainingRmsError [get, set]
 Whether training RMS errors from convergence tavble are compared.
bool CompareByTrainingMaxError [get, set]
 Whether Maximal absolute training errors from convergence tavble are compared.
bool CompareByVerificationRmsError [get, set]
 Whether verification RMS errors from convergence tavble are compared.
bool CompareByVerificationMaxError [get, set]
 Whether Maximal absolute verification errors from convergence tavble are compared.

Private Member Functions

int IComparer
< NeuralTrainingParameters >. 
Compare (NeuralTrainingParameters a, NeuralTrainingParameters b)

Detailed Description

Base comparer class (implementation of the IComparer<NeuralTrainingParameters> interface) for conmparing objects of type NeuralTrainingParameters


Member Function Documentation

double IG::Neural::NeuralTrainingParameters::ComparerBase::OutputNorm ( IVector  vec) [inline, protected]
int IComparer<NeuralTrainingParameters>. IG::Neural::NeuralTrainingParameters::ComparerBase::Compare ( NeuralTrainingParameters  a,
NeuralTrainingParameters  b 
) [inline, private]

Member Data Documentation


Property Documentation

IVector IG::Neural::NeuralTrainingParameters::ComparerBase::OutputScalingLengths [get, set]

Vector of scaling lengths for calculation of weighted norms.

bool IG::Neural::NeuralTrainingParameters::ComparerBase::CompareMinError [get, set]

Whether the min error in convergence table is used for comparison when errors are compared. If false then mean value of last errors is used.

int IG::Neural::NeuralTrainingParameters::ComparerBase::NumLastErrors [get, set]

Number of last errors in convergence list for calculataing the mean value of error. The default valeu is 1 which represent the last error in the converhence list.

int IG::Neural::NeuralTrainingParameters::ComparerBase::NumBundles [get, set]

Number of bundles where sorting of convergences stars. The default valeu is 0 which represent normal sorting after the training is done.

bool IG::Neural::NeuralTrainingParameters::ComparerBase::CompareByTrainingRmsError [get, set]

Whether training RMS errors from convergence tavble are compared.

bool IG::Neural::NeuralTrainingParameters::ComparerBase::CompareByTrainingMaxError [get, set]

Whether Maximal absolute training errors from convergence tavble are compared.

bool IG::Neural::NeuralTrainingParameters::ComparerBase::CompareByVerificationRmsError [get, set]

Whether verification RMS errors from convergence tavble are compared.

bool IG::Neural::NeuralTrainingParameters::ComparerBase::CompareByVerificationMaxError [get, set]

Whether Maximal absolute verification errors from convergence tavble are compared.


The documentation for this class was generated from the following file:
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