IGLib
1.5
The IGLib base library for development of numerical, technical and business applications.
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A data transfer object (DTO) for neural approximation classes that implement the INeuralApproximator interface. $A Igor Mar11; More...
Public Member Functions | |
NeuralApproximatorDtoBase () | |
void | SetRestoringInternalState (bool doRestore) |
Sets the flag indicating whether internal state of the (trained) neural network should be restored, if possible, when the contents of the current DTO (data transfer object) is copied to a neural approximator object. Default value of the flag is true. More... | |
bool | GetRestoringInternalState () |
Returns a flag indicating whether internal state of the (trained) neural network should be restored, if possible, when the contents of the current DTO (data transfer object) is copied to a neural approximator object. Default value of the flag is true and can be changed by the SetRestoringInternalState() method. More... | |
Public Attributes | |
int | InputLength |
int | OutputLength |
int | NumHiddenLayers |
int[] | NumHiddenNeurons |
string | NeuralApproximatorType |
string | NetworkStateFilePath |
string | NetworkStateRelativePath |
double | LearningRate |
double | SigmoidAlphaValue |
double | Momentum |
SampledDataSetDto | TrainingData |
IndexListDto | VerificationIndices |
int | MaxEpochs |
int | EpochsInBundle |
VectorDtoBase | ToleranceRms |
Tolerance over RMS error of output over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account. More... | |
VectorDtoBase | ToleranceRmsRelativeToRange |
Relative tolerances on RMS errors of outputs over training points, relative to the correspoinding ranges of output data. More... | |
double | ToleranceRmsRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on RMS errors of outputs can be set to the same value. More... | |
VectorDtoBase | ToleranceMax |
Tolerance on maximal error of output over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account. More... | |
VectorDtoBase | ToleranceMaxRelativeToRange |
Relative tolerances on max. abs. errors of outputs over training points, relative to the correspoinding ranges of output data. More... | |
double | ToleranceMaxRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on max. abs. errors of outputs can be set to the same value. More... | |
double | InputBoundsSafetyFactor |
double | OutputBoundsSafetyFactor |
BoundingBoxDtoBase | InputDataBounds |
BoundingBoxDtoBase | OutputDataBounds |
BoundingBoxDtoBase | InputNeuronsRange |
BoundingBoxDtoBase | OutputNeuronsRange |
bool | SaveConvergenceRms |
VectorDtoBase[] | ConvergenceListRms |
int[] | EpochNumbers |
VectorDtoBase[] | ConvergenceErrorsTrainingRmsTable |
VectorDtoBase[] | ConvergenceErrorsTrainingMaxTable |
VectorDtoBase[] | ConvergenceErrorsVerificationRmsTable |
VectorDtoBase[] | ConvergenceErrorsVerificationMaxTable |
Protected Member Functions | |
bool | IsAppropriateType (INeuralApproximator obj) |
Returns true if the specified neural network approximator object is of a correct type, according to type indicated by the NeuralApproximatorType property, or false otherwise. More... | |
override void | CopyFromPlain (INeuralApproximator obj) |
Copies data from the specified neural approximator object to the current DTO (data transfer object). More... | |
override void | CopyToPlain (ref INeuralApproximator obj) |
Copies contents of the current DTO (data transfer object) to the specified neural approximator object. If it is indicated on that object that the internal neural network state has been stored to a file then this state is restored from that file, too. This enables saving of trained networks for future use. More... | |
Properties | |
VectorDtoBase | ToleranceRMS [get, set] |
Variable with old name, which is kept here for compatibility of files that were created by serialization in previous versions of code. More... | |
int | OutputLevel [get, set] |
Level of information that is output to the console by some methods. More... | |
Private Attributes | |
int | _outputLevel = Util.OutputLevel |
bool | _restoreInternalState = true |
A data transfer object (DTO) for neural approximation classes that implement the INeuralApproximator interface. $A Igor Mar11;
Type | : | class | |
Type | : | INeuralApproximator |
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inlineprotected |
Returns true if the specified neural network approximator object is of a correct type, according to type indicated by the NeuralApproximatorType property, or false otherwise.
obj | Object whose type is checked. |
References IG.Num.INeuralApproximator.NeuralApproximatorType.
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inline |
Sets the flag indicating whether internal state of the (trained) neural network should be restored, if possible, when the contents of the current DTO (data transfer object) is copied to a neural approximator object. Default value of the flag is true.
doRestore | Value of the flag to be set. Default is true. |
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inline |
Returns a flag indicating whether internal state of the (trained) neural network should be restored, if possible, when the contents of the current DTO (data transfer object) is copied to a neural approximator object. Default value of the flag is true and can be changed by the SetRestoringInternalState() method.
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inlineprotected |
Copies data from the specified neural approximator object to the current DTO (data transfer object).
obj | Object from which data is copied. |
Actual type of the object is also stored. In this way, neural network approximator of the correct type can be created when restoring data from a stored location.
References IG.Num.INeuralApproximator.ConvergenceErrorsTrainingMaxList, IG.Num.INeuralApproximator.ConvergenceErrorsTrainingRmsList, IG.Num.INeuralApproximator.ConvergenceErrorsVerificationMaxList, IG.Num.INeuralApproximator.ConvergenceErrorsVerificationRmsList, IG.Num.INeuralApproximator.EpochNumbers, IG.Num.INeuralApproximator.EpochsInBundle, IG.Num.INeuralApproximator.GetNumNeuronsInHiddenLayer(), IG.Num.INeuralApproximator.InputBoundsSafetyFactor, IG.Num.INeuralApproximator.InputDataBounds, IG.Num.INeuralApproximator.InputLength, IG.Num.INeuralApproximator.InputNeuronsRange, IG.Num.INeuralApproximator.LearningRate, IG.Lib.ILockable.Lock, IG.Num.INeuralApproximator.MaxEpochs, IG.Num.INeuralApproximator.Momentum, IG.Num.INeuralApproximator.NetworkStateFilePath, IG.Num.INeuralApproximator.NetworkStateRelativePath, IG.Num.INeuralApproximator.NeuralApproximatorType, IG.Num.INeuralApproximator.NumHiddenLayers, IG.Num.INeuralApproximator.OutputBoundsSafetyFactor, IG.Num.INeuralApproximator.OutputDataBounds, IG.Num.INeuralApproximator.OutputLength, IG.Num.INeuralApproximator.OutputLevel, IG.Num.INeuralApproximator.OutputNeuronsRange, IG.Num.INeuralApproximator.SaveConvergenceRms, IG.Num.INeuralApproximator.SigmoidAlphaValue, IG.Num.INeuralApproximator.ToleranceMax, IG.Num.INeuralApproximator.ToleranceMaxRelativeToRange, IG.Num.INeuralApproximator.ToleranceMaxRelativeToRangeScalar, IG.Num.INeuralApproximator.ToleranceRms, IG.Num.INeuralApproximator.ToleranceRmsRelativeToRange, IG.Num.INeuralApproximator.ToleranceRmsRelativeToRangeScalar, IG.Num.INeuralApproximator.TrainingData, and IG.Num.INeuralApproximator.VerificationIndices.
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inlineprotected |
Copies contents of the current DTO (data transfer object) to the specified neural approximator object. If it is indicated on that object that the internal neural network state has been stored to a file then this state is restored from that file, too. This enables saving of trained networks for future use.
obj | Neural network approximator object where data is stored. |
int IG.Num.NeuralApproximatorDtoBase< Type >.InputLength |
int IG.Num.NeuralApproximatorDtoBase< Type >.OutputLength |
int IG.Num.NeuralApproximatorDtoBase< Type >.NumHiddenLayers |
int [] IG.Num.NeuralApproximatorDtoBase< Type >.NumHiddenNeurons |
string IG.Num.NeuralApproximatorDtoBase< Type >.NeuralApproximatorType |
string IG.Num.NeuralApproximatorDtoBase< Type >.NetworkStateFilePath |
string IG.Num.NeuralApproximatorDtoBase< Type >.NetworkStateRelativePath |
double IG.Num.NeuralApproximatorDtoBase< Type >.LearningRate |
double IG.Num.NeuralApproximatorDtoBase< Type >.SigmoidAlphaValue |
double IG.Num.NeuralApproximatorDtoBase< Type >.Momentum |
SampledDataSetDto IG.Num.NeuralApproximatorDtoBase< Type >.TrainingData |
IndexListDto IG.Num.NeuralApproximatorDtoBase< Type >.VerificationIndices |
int IG.Num.NeuralApproximatorDtoBase< Type >.MaxEpochs |
int IG.Num.NeuralApproximatorDtoBase< Type >.EpochsInBundle |
VectorDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.ToleranceRms |
Tolerance over RMS error of output over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account.
VectorDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.ToleranceRmsRelativeToRange |
Relative tolerances on RMS errors of outputs over training points, relative to the correspoinding ranges of output data.
double IG.Num.NeuralApproximatorDtoBase< Type >.ToleranceRmsRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on RMS errors of outputs can be set to the same value.
VectorDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.ToleranceMax |
Tolerance on maximal error of output over training points. Training will continue until error becomes below tolerance or until maximal number of epochs is reached. If less or equal than 0 then this tolerance is not taken into account.
VectorDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.ToleranceMaxRelativeToRange |
Relative tolerances on max. abs. errors of outputs over training points, relative to the correspoinding ranges of output data.
double IG.Num.NeuralApproximatorDtoBase< Type >.ToleranceMaxRelativeToRangeScalar |
Scalar through which all components of the Relative tolerances on max. abs. errors of outputs can be set to the same value.
double IG.Num.NeuralApproximatorDtoBase< Type >.InputBoundsSafetyFactor |
double IG.Num.NeuralApproximatorDtoBase< Type >.OutputBoundsSafetyFactor |
BoundingBoxDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.InputDataBounds |
BoundingBoxDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.OutputDataBounds |
BoundingBoxDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.InputNeuronsRange |
BoundingBoxDtoBase IG.Num.NeuralApproximatorDtoBase< Type >.OutputNeuronsRange |
bool IG.Num.NeuralApproximatorDtoBase< Type >.SaveConvergenceRms |
VectorDtoBase [] IG.Num.NeuralApproximatorDtoBase< Type >.ConvergenceListRms |
int [] IG.Num.NeuralApproximatorDtoBase< Type >.EpochNumbers |
VectorDtoBase [] IG.Num.NeuralApproximatorDtoBase< Type >.ConvergenceErrorsTrainingRmsTable |
VectorDtoBase [] IG.Num.NeuralApproximatorDtoBase< Type >.ConvergenceErrorsTrainingMaxTable |
VectorDtoBase [] IG.Num.NeuralApproximatorDtoBase< Type >.ConvergenceErrorsVerificationRmsTable |
VectorDtoBase [] IG.Num.NeuralApproximatorDtoBase< Type >.ConvergenceErrorsVerificationMaxTable |
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getset |
Variable with old name, which is kept here for compatibility of files that were created by serialization in previous versions of code.
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getsetprivate |
Level of information that is output to the console by some methods.