IGLib
1.7.2
The IGLib base library EXTENDED - with other lilbraries and applications.
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Base class for loadable scripts that are used as custom applications that inclued neural networks approximations and optimization. More...
Public Member Functions | |
LoadableScriptShellNeuralIT () | |
override void | Script_AddCommands (ICommandLineApplicationInterpreter interpreter, SortedList< string, string > helpStrings) |
Adds commands to the internal interpreter. More... | |
void | TrainANN () |
Train the artificial neural network. More... | |
void | PlotTrainingRmsError (List< NeuralTrainingParameters > trainParameters, bool showMinAndMaxError, bool showMinToMaxError, bool showBestNErrors, int numErrorsShow, int numBoundles) |
Plots graph for training Rms errors. More... | |
void | PlotTrainingMaxError (List< NeuralTrainingParameters > trainParameters, bool showMinAndMaxError, bool showMinToMaxError, bool showBestNErrors, int numErrorsShow, int numBoundles) |
Plots graph for training Max errors. More... | |
void | PlotVerificationRmsError (List< NeuralTrainingParameters > trainParameters, bool showMinAndMaxError, bool showMinToMaxError, bool showBestNErrors, int numErrorsShow, int numBoundles) |
Plots graph for verification Rms errors. More... | |
void | PlotVerificationMaxError (List< NeuralTrainingParameters > trainParameters, bool showMinAndMaxError, bool showMinToMaxError, bool showBestNErrors, int numErrorsShow, int numBoundles) |
Plots graph for verification Max errors. More... | |
virtual void | ApplyPlotSettings (PlotterZedGraph plotter, PlotZedGraphBase plot) |
Applies basic plot settings for various kinds of plots. More... | |
virtual void | ApplyPlotSettingsCurveRGB (PlotterZedGraph plotter, PlotZedgraphCurve plot, double r, double g, double b) |
virtual void | ApplyPlotSettingsCurve (PlotterZedGraph plotter, PlotZedgraphCurve plot) |
Applies basic plot settings for basic kinds of curve plots. More... | |
virtual void | ApplyPlotSettingsCurve (PlotterZedGraph plotter, PlotZedgraphCurve plot, int i, int max) |
Applies basic plot settings for basic kinds of curve plots where groups of curves are plotted. More... | |
virtual void | ApplyPlotSettingsCurveBlueRed (PlotterZedGraph plotter, PlotZedgraphCurve plot, int i, int max) |
Applies basic plot settings for basic kinds of curve plots where groups of curves are plotted. More... | |
virtual void | ApplyPlotSettingsCurveMonotoneBlue (PlotterZedGraph plotter, PlotZedgraphCurve plot, int i, int max) |
virtual void | ApplyPlotSettingsCurveMonotoneRed (PlotterZedGraph plotter, PlotZedgraphCurve plot, int i, int max) |
virtual void | PlotNeuralResponse (IVector parameters, int whichOut, int whichParam, double minParam, double maxParam, int numPoints) |
Plots a parametrix stydy of neural network approximated response by plotting dependency of the specified output on the specified parameter. More... | |
virtual void | PlotNetworkResponse (int numPoints) |
Plots some parametric studies of responses generated by the trained network. More... | |
virtual void | PlotNetworkResponseGrouped (IVector parameters, int whichOut, IBoundingBox parameterBounds, int numPoints) |
Plots a parametrix stydy of neural network approximated response by plotting dependency of the specified output on the specified parameter. More... | |
virtual void | PlotNetworkResponseGrouped (int numPoints) |
Plots some parametric studies of responses generated by the trained network. More... | |
virtual void | PlotAnalysisTable (AnalysisResults[] tabres) |
Plots the specified table of analysis results. More... | |
virtual void | PlotApproximationTable (IVector[][] tabApproximationResults) |
Plots the specified table of calculated approximation results. More... | |
void | PlotVerificationError (bool showErrorInPercentage, bool verificationPoFromFile) |
Plots the ANN approximation on verification points and real points. More... | |
void | PlotVerificationError (bool verificationFromFile, int whichOutput) |
Plots the ANN approximation on verification points and real points. More... | |
void | PlotVerificationError (bool showErrorInPercentage, bool verificationFromFile, int whichOutput) |
Plots the ANN approximation on verification points and real points. More... | |
void | CalculateVerificationError (int whichOut, ref List< IVector > verificationPointsResponse) |
Calculates response on verification points. More... | |
void | CalculateVerificationError (bool verificationFromFile, int whichOut, ref List< IVector > verificationPointsResponse) |
Calculates response on verification points. More... | |
void | PlotTrainingError () |
Plots the ANN approximation on training points and real points. More... | |
void | PlotTrainingError (int whichOutput) |
Plots the ANN approximation on training points and real points. More... | |
void | PlotTrainingError (bool showErrorInPercentage, int whichOutput) |
Plots the ANN approximation on training points and real points. More... | |
void | CalculateTrainingError (int whichOut, ref List< IVector > trainingPointsResponse) |
Calculates response on training points. More... | |
void | PointsOnLine () |
Network response on the points calculated between two selected points. More... | |
void | PlotResponseOnLine (IVector p1Training, IVector p2Training, IVector p1Verification, IVector p2Verification, int numPoints) |
Network response on the points calculated between two selected points. More... | |
void | PlotTrainingAndVerificationPointsResponse (List< IVector > selectedTrainingPoints, List< IVector > selectedVerificationPoints, int whichOutput, int whichParameter, double min, double max) |
Plots the ANN approcimation on the verification points and/or real points. More... | |
SampledDataSet | GetVerificationFromApproximator () |
Returns verification points from complete training data set in approximator. More... | |
SampledDataSet | GetTrainingFromApproximator () |
Returns training points from complete training data set in approximator. More... | |
void | SensitivityStart () |
Perform sensitiviy test from training and verification data. More... | |
void | SensitivityPerform (IVector trainingCenterPoint, IVector verificationCenterPoint) |
void | CenterPointResponse () |
Plots the ANN approcimation on the center point from verification point and/or real point. More... | |
void | PlotNetworkCenteredPointResponse (IVector trainingCenterPoint, IVector verificationCenterPoint) |
Plots the ANN approcimation on the center point from verification point and/or real point. More... | |
void | CalculateSensitivity (IVector selectedTrainingPoint, IVector selectedVerificationPoint, int whichParameter, double min, double max) |
void | PlotTrainingAndVerificationPointResponse (IVector selectedTrainingPoint, IVector selectedVerificationPoint, int whichOutput, int whichParameter, double min, double max) |
Plots the ANN approcimation on the verification point and real point. More... | |
void | CenterPoint (INeuralApproximator Approximator, ref IVector inputCenterParameters, bool centerOnTrainingPoint) |
Calculate the average center point for each parameter from complete data set. More... | |
void | CenterPoint (SampledDataSet DataSet, ref IVector inputCenterParameters) |
Calculate the average center point for each parameter from complete data set. More... | |
virtual string | TrainNetwork (string[] arguments) |
virtual string | TrainParallelExample (string[] arguments) |
Performs parallel training of neural networks with different parameters and default data. More... | |
virtual string | PlotConvergence (string[] arguments) |
Plot different convergences from traininglimits file More... | |
virtual string | PlotTrainingTime (string[] arguments) |
Plot graph sorted on training time. More... | |
virtual string | PlotVerificationResponse (string[] arguments) |
Plot the approximated response of the neural network in the verification points. More... | |
virtual string | PlotTrainingResponse (string[] arguments) |
Plot the approximated response of the neural network in the training points. More... | |
virtual string | PlotNetworkResponseOnLineAll (string[] arguments) |
Plot the approximated responses of the neural network on two selected points and on the points between them. More... | |
string | PlotNetworkCenteredPoint (string[] arguments) |
Pots parametric study on center point from training and/or verification sets. More... | |
string | Sensitivity (string[] arguments) |
Execute sensitivity test and saves to CSV file. More... | |
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LoadableScriptShellNeuralITOriginalBase () | |
override void | Analyse (Num.IAnalysisResults anRes) |
Dummy analysis, jsut throws the exception. More... | |
virtual string | Test (string[] arguments) |
Runs a custom basic test. More... | |
virtual string | Custom (string[] arguments) |
Runs a custom test. More... | |
virtual string | RunParallelSimulations (string[] arguments) |
Runs multiple simulations in parallel threads and saves results in each parallel thread. More... | |
virtual string | CollectParallelSimulationResults (string[] arguments) |
Collects results of simulations run in parallel threads, joins them into a single object and saves them. More... | |
override void | Script_AddCommands (ICommandLineApplicationInterpreter interpreter, SortedList< string, string > helpStrings) |
Adds commands to the internal interpreter. More... | |
virtual IVector | TestApproximation (IVector parameters) |
Performs a test approximation at the specified vector of parameters and outputs and returns results. More... | |
virtual IVector[][] | TestApproximationTable (int numPoints) |
Calculates a table of specified number of approximations, with parameters running between two points that are chosen by the algorithm. More... | |
virtual IVector[][] | TestApproximationTable (IVector param1, IVector param2, int numPoints) |
Performs a table of approximatinos between two specified vectors of approximation input parameters, and outputs results. It also returns the table of results (approximated output values in form of vectors). More... | |
virtual AnalysisResults | TestAnalysis (IVector parameters) |
Performs test analysis at the specified optimization parameters and outputs results. More... | |
virtual AnalysisResults[] | TestAnalysisTable (int numPoints) |
Calculates a table of specified number of analyses, with parameters running between two points that are chosen by the algorithm. More... | |
virtual AnalysisResults[] | TestAnalysisTable (IVector param1, IVector param2, int numPoints) |
Performs a table of direct analyses between two specified vectors of optimization parameters, and outputs results. It also returns the table of analysis results. More... | |
virtual void | OptimizeSimplex () |
virtual void | TransfNeuralToOptimizationParameters (IVector neuralParameters, ref IVector optimizationParameters) |
Maps input parameters for neural network to optimization input parameters. More... | |
virtual void | TransfOptimizationToNeuralParameters (IVector optimizationParameters, ref IVector neuralParameters) |
Maps optimization input parameters to neural network input parameters. More... | |
delegate string | ParallelRunDelegate (int threadIndex) |
virtual void | ParSimBegin (int numThreads) |
Launches parallel jobs. More... | |
virtual void | GatherParallelResults (int numThreads) |
Reads results form all parallel threads, gathers them in a single training set, and saves them in the template optimiation directory. More... | |
virtual string | ParSimGetOptimizationDirectoryPath (int threadIndex) |
Returns path to the directory containing optimization data for the specified parallel thread. More... | |
virtual string | ParSimGetMutexName (int threadIndex) |
Returns name of the mutex for locking data on disk that is used by the specified parallel thread. More... | |
virtual string | ParSimGetParallelResultsFilePath (int threadIndex) |
Returns path of the file where resuls of calculation in the specified parallel thread are stored. More... | |
virtual string | GetParallelResultFilePath () |
Returns path of the file where resuls of calculation in the specified parallel thread are stored. More... | |
virtual void | ParSimPreparelDirectory (int threadIndex) |
Prepares data for running calculations in the parallel thread with the specified index. More... | |
void | ParSimAddResults (int threadIndex, SampledDataSet results) |
Reads results of the specified thread and adds them to the existing training set. More... | |
string | ParSimRunJob (int threadIndex) |
Runs the job in the specified parallel thread. More... | |
virtual void | ParSimGetNextInput (ref IVector simInput) |
Generates the next vector of simulation input parameters that will be used for calculation of a new parallel simulation. More... | |
virtual void | ParSimGetNextNeuralInput (ref IVector simInput) |
Generates the next vector of neural input parameters that will be used for calculation of a new training element. More... | |
virtual string | ParSimGetSimulationDirectoryPath (int threadIndex) |
Returns the simulation directory for the specified parallel calculation thread. More... | |
virtual IResponseEvaluatorVectorSimple | ParSimGetSimulator (int threadIndex) |
Creates simulator file manager for the simulator that will the specified parallel task. More... | |
override void | TransfSimulationToNeuralInput (IVector original, ref IVector result) |
Transforms the specified vector of simulation input parameters to the vector of neural input parameters and stores the vector to the specified variable. More... | |
override void | TransfNeuralToSimulationInput (IVector original, ref IVector result) |
Transforms the specified vector of neural input parameters to the vector of simulation input parameters and stores the vector to the specified variable. More... | |
override void | TransfSimulationToNeuralOutput (IVector original, ref IVector result) |
Transforms the specified vector of simulation output values (results) to the vector of neural output values and stores the vector to the specified variable. More... | |
virtual void | TransfNeuralToSimulationOutput (IVector original, ref IVector result) |
Transforms the specified vector of neural output values to the vector of simulation output values (results) and stores the vector to the specified variable. More... | |
virtual SampledDataElement | GetTrainingElement (int i) |
Gets the training element with the specified index (chosen out of al training points, including verificaion points). More... | |
virtual bool | IsVerificationPoint (int trainingPointIndex) |
Whether the training point with the specified index is a verification points and has not been used in training. More... | |
List< SampledDataElement > | GetTrainingElements (bool includeVerificationPoints) |
Returns a list of all neural training elements. More... | |
List< SampledDataElement > | GetTrainingElements (bool includeVerificationPoints, bool includeNonVerificationPoints) |
Returns a list of all neural training elements. More... | |
virtual void | GetNeuralInputPerturbance (double ratio, ref IVector perturbance) |
Returns a perturbance vector whose componenets represent magnitudes of perturbances of neural input parameters with the specified relative ratio with corresponding parameters' scaling lengths. More... | |
virtual void | GetNeuralOutputPerturbance (double ratio, ref IVector perturbance) |
Returns a perturbance vector whose componenets represent magnitudes of perturbances of neural output values with the specified relative ratio with corresponding values' scaling lengths. More... | |
virtual void | GetNeuralInputVector (double relativeComponents, ref IVector inputVector) |
Returns an input vector whose components are at specified relative distances (with respect to full range) from lower bounds on componens of input vectors. More... | |
virtual void | GetNeuralOutputVector (double relativeComponents, ref IVector outputVector) |
Returns an output vector whose components are at specified relative distances (with respect to full range) from lower bounds on componens of input vectors. More... | |
virtual void | GetNeuralInputVector (IVector relativeComponents, ref IVector inputVector) |
Returns an input vector whose components are at specified relative distances (with respect to full range) from lower bounds on componens of input vectors. More... | |
virtual void | GetNeuralOutputVector (IVector relativeComponents, ref IVector outputVector) |
Returns an output vector whose components are at specified relative distances (with respect to full range) from lower bounds on componens of input vectors. More... | |
virtual void | GetNeuralInputRelative (IVector inputVector, ref IVector relativeComponents) |
Calculates relative components (running from 0 to 1 within bounds for specified vector component) for the specified vector of neural input parameters. More... | |
virtual void | GetNeuralOutputRelative (IVector outputVector, ref IVector relativeComponents) |
Calculates relative components (running from 0 to 1 within bounds for specified vector component) for the specified vector of neural output values. More... | |
virtual double | InputDistance (IVector v1, IVector v2) |
Returns measure of distance between two vectors in the space of input parameters. Euclidean norm scaled accorging to individual parameter ranges is returned. More... | |
virtual double | OutputDistance (IVector v1, IVector v2) |
Returns measure of distance between two vectors in the space of output parameters. Euclidean norm scaled accorging to individual parameter ranges is returned. More... | |
virtual double | InputDistance (IVector v1, int trainingPointIndex) |
Returns measure of distance between the specified neural input vector and vector of input parameters of the training point with the specified index. More... | |
virtual double | OutputDistance (IVector v1, int trainingPointIndex) |
Returns measure of distance between the specified neural output values vector and vector of output values of the training point with the specified index. More... | |
virtual int | GetClosestInputIndex (IVector v1, bool includeVerificationPoints) |
Returns index of training element with the shortest distance of its input parameters to the specified vector. More... | |
virtual int | GetClosestOutputIndex (IVector v1, bool includeVerificationPoints) |
Returns index of training element with the shortest distance of its output values to the specified vector of output values. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, params IVector[] points) |
For each point in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, bool printIndividualPointsComp, params IVector[] points) |
For each point in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, params SampledDataElement[] points) |
For each point (training element) in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, bool printIndividualPointsComp, params SampledDataElement[] points) |
For each point (training element) in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
virtual void | NeuralCalculate (params double[] inputParameters) |
Calculates approximation at specified input parameters. More... | |
virtual void | NeuralCalculate (IVector inputParameters) |
Calculates approximation at specified input parameters. More... | |
virtual void | NeuralCalculate (IVector inputParameters, ref IVector outputValues) |
Calculates approximation at specified input parameters. More... | |
virtual List< SampledDataElement > | GetRandomTrainingElements (int numElements, bool includeVerificationPoints) |
Creates and returns a list of training elements that are randomly chosen from the current training set. Trainingpoints that are not verification points are always included in the set of points for selection. More... | |
virtual List< SampledDataElement > | GetRandomTrainingElements (int numElements, bool includeVerificationPoints, bool includeNonVerificationPoints) |
Creates and returns a list of training elements that are randomly chosen from the current training set. More... | |
virtual List< SampledDataElement > | GetRandomTrainingElements (IRandomGenerator rand, int numElements, bool includeVerificationPoints) |
Creates and returns a list of training elements that are randomly chosen from the current training set. Trainingpoints that are not verification points are always included in the set of points for selection. More... | |
virtual List< SampledDataElement > | GetRandomTrainingElements (IRandomGenerator rand, int numElements, bool includeVerificationPoints, bool includeNonVerificationPoints) |
Creates and returns a list of training elements that are randomly chosen from the current training set. More... | |
virtual IVector | GetRandomNeuralInput () |
Creates and returns a random vector of neural input parameters whose elements (components) lie within the lower and upper bounds on parameters. More... | |
virtual void | GetRandomNeuralInput (IRandomGenerator rand, ref IVector result) |
Generates a random vector of neural input parameters whose elements (components) lie within the lower and upper bounds on parameters, and stores it to the specified vector. More... | |
virtual IVector | GetRandomNeuralInput (IRandomGenerator rand) |
Creates and returns a random vector of neural input parameters whose elements (components) lie within the lower and upper bounds on parameters. More... | |
virtual void | GetRandomNeuralInput (ref IVector result) |
Generates a random vector of neural input parameters whose elements (components) lie within the lower and upper bounds on parameters. More... | |
virtual IVector | GetRandomNeuralOutput () |
Creates and returns a random vector of neural output values whose elements (components) lie within the lower and upper bounds on values. More... | |
virtual IVector | GetRandomNeuralOutput (IRandomGenerator rand) |
Creates and returns a random vector of neural output values whose elements (components) lie within the lower and upper bounds on values. More... | |
virtual void | GetRandomNeuralOutput (ref IVector result) |
Generates a random vector of neural output values whose elements (components) lie within the lower and upper bounds on values. More... | |
virtual void | GetRandomNeuralOutput (IRandomGenerator rand, ref IVector result) |
Generates a random vector of neural output values whose elements (components) lie within the lower and upper bounds on neural output values, and stores it to the specified vector. More... | |
virtual void | PrepareNeuronsTable (int minNeurons, int maxNeurons, int numNeurons, ref int[] neurpnsTable) |
Prepares table of neurons in geometric sequence. More... | |
void | TrainANN (int annType, int NumNeurons, int MaxEpochs, int EpochsInBundle, double LearnignRate, double Momentum, double InputSafetyFactor, double OutputSafetyFactor, double PercentVerificationPoints) |
Train the artificial neural network. More... | |
virtual string | CreateDistortedModelData (string[] arguments) |
Creates data for distorted model and stores the data in the specified directory. More... | |
virtual string | StochasticCreateInput (string[] arguments) |
Creates a sample input file for analysis of influence of stochastic variables. More... | |
virtual string | StochasticInfluence (string[] arguments) |
Analysis influence of stochastivc input parameters with specified means and standard deviations on the output values of the model. More... | |
virtual string | CreateBinarySampledData (string[] arguments) |
Reads sampled data from the standard location and stores it in the same directory in binary form, in the file with extension .bin *_binaryTrainingDataExtension(. More... | |
virtual string | RestoreBinarySampledData (string[] arguments) |
Reads sampled data in binary form from from the standard location and stores it in the same directory in JSON, in the file with extension .jsonrestored (_restoredTrainingDataExtension). More... | |
virtual void | TestDistances (int referencePointIndex, int maxNumPoints) |
Test of distances of a specified number of training points with respect to the training point with specified index, in the input parameters space as well as in the output parameter space. More... | |
virtual void | TestNeuralSpeed (int numEvaluations) |
Performs test of speed of calculation of neural network. More... | |
virtual void | PrintNeuralData () |
This example demonstrates how to extract data necessary for definition of optimization problems. More... | |
void | SaveSensitivityCSV () |
Saves sensitivity test in csv file. More... | |
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virtual void | InitSimCastingRobert (string rootDirectory, string projectName, string simulationName) |
Initializes the data for casting simulation interface. More... | |
virtual void | InitSimConvectionRobert (string rootDirectory, string projectName, string simulationName) |
Initializes the data for Convection simulation interface. More... | |
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LoadableScriptShellNeural () | |
override void | Analyse (Num.IAnalysisResults anRes) |
Dummy analysis, jsut throws the exception. More... | |
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LoadableScriptShellNeuralBase () | |
override void | Analyse (Num.IAnalysisResults anRes) |
Dummy analysis, jsut throws the exception. More... | |
delegate string | ParallelRunDelegate (int threadIndex) |
void | ParSimAddResults (int threadIndex, SampledDataSet results) |
Reads results of the specified thread and adds them to the existing training set. More... | |
string | ParSimRunJob (int threadIndex) |
Runs the job in the specified parallel thread. More... | |
List< SampledDataElement > | GetTrainingElements (bool includeVerificationPoints) |
Returns a list of all neural training elements. More... | |
List< SampledDataElement > | GetTrainingElements (bool includeVerificationPoints, bool includeNonVerificationPoints) |
Returns a list of all neural training elements. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, params IVector[] points) |
For each point in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, bool printIndividualPointsComp, params IVector[] points) |
For each point in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, params SampledDataElement[] points) |
For each point (training element) in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TestClosestPoints (int numClosestPoints, bool includeVerificationPoints, bool printByComponents, bool printIndividualPointsComp, params SampledDataElement[] points) |
For each point (training element) in the specified array, the training points are sorted according to the distance to this point, and data for the specified number of closest points are written. More... | |
void | TrainANN (int annType, int NumNeurons, int MaxEpochs, int EpochsInBundle, double LearnignRate, double Momentum, double InputSafetyFactor, double OutputSafetyFactor, double PercentVerificationPoints) |
Train the artificial neural network. More... | |
void | SaveSensitivityCSV () |
Saves sensitivity test in csv file. More... | |
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LoadableScriptShellBase () | |
Constructor. More... | |
virtual bool | RepairSimulationParameters (IVector parameters) |
Repairs simulation parameters, if necessary, in such a way that values are consistent with simuation data (e.g. spacing of nodes). More... | |
virtual void | SimulatorCalculateResponse (IResponseEvaluatorVectorSimple simulator, IVector inputParameters, ref IVector outputValues) |
Calculates vector response by the specified simulator, and stores output values to the specified vector variable. More... | |
void | SimulatorCalculateResponse (IVector parameters, ref IVector outputValues) |
Calculates vector response by the main simulator of the script, and stores output values to the specified vector variable. More... | |
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LoadableScriptOptShellBaseControllable () | |
Creates a LoadableScriptOptBase object. More... | |
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LoadableScriptOptBase () | |
Creates the LoadableScriptOptBase object. More... | |
abstract void | Analyse (IAnalysisResults anRes) |
Performs direct analysis for optimization problems. This method must be overridden in derived classes where one wants to have direct analysis defined. More... | |
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LoadableScriptBase () | |
Argument-less constructor. If argument-less constructor is called then initialization is not performed and will be performed later. More... | |
string | Run (string[] arguments) |
Performs the action of this object. Override this in derived classes! More... | |
void | Initialize (string[] arguments) |
Initializes the object. If not called explicitly, this method is automatically called at the first call to the Run method. More... | |
virtual string | Script_DefaultInitialize (string[] arguments) |
Default initialization method for scripts. More... | |
virtual string | Script_DefaultRun (string[] arguments) |
Default run method for the script. Can be used when only installed commands are run by hte script. More... | |
virtual ICommandLineApplicationInterpreter | Script_CreateInterpreterWithoutCommands () |
Creates and returns an interpreter that can be used as script's internal interpreter for running script's commands. More... | |
delegate string | Script_CommandDelegate (string[] args) |
Delegate for commands that are installed on script's internal interpreter (property Script_Interpreter). More... | |
void | Script_AddCommand (string commandName, Script_CommandDelegate command, string helpString) |
Adds a new internal script command under specified name to the internal interpreter of the current script object. More... | |
virtual void | Script_AddCommand (ICommandLineApplicationInterpreter interpreter, SortedList< string, string > helpStrings, string commandName, Script_CommandDelegate command, string helpString) |
Adds a new internal script command under specified name to the internal interpreter of the current script object. More... | |
string | Script_GetHelpString (string scriptCommandName) |
Returns help string for internal script command with specified name, or null if help string is not installed for such a command. More... | |
void | Script_PrintCommandsHelp () |
Prits help for the installed internal commands of the script. More... | |
virtual bool | Script_ContainsCommand (string commandName) |
Returns true if the internal script's interpreter contains a command with specified name, false otherwise. More... | |
virtual bool | Script_ContainsScriptCommand (string commandName) |
Returns true if the specified command is script command (i.e. its first argument is command-name and it is run through the Script_CommandAdapter object). More... | |
virtual void | Script_RemoveCommand (string commandName) |
Removes the specified internal script command from the internal interpreter of the current scripting object. More... | |
virtual void | Script_RemoveAllCommands () |
Removes ALL internal script commands from the internal interpreter of the current scripting object. More... | |
string | Script_Run (string[] arguments) |
Runs internal script command. More... | |
string | Script_Run (string commandName, params string[] otherArguments) |
Runs internal script command. More... | |
virtual void | Script_PrintArguments (string messageString, string[] arguments) |
Prints the specified array of string arguments (usually passed as command-line arguments). More... | |
Public Attributes | |
const string | ConstTrainNetwork = "TrainNetwork" |
Comamnd name for training the network. More... | |
const string | ConstHelpTrainNetwork = "Trains NN on basis of standard data. Args: <numLayers> <numNeurons>" |
const string | ConstTrainParallelExample = "TrainParallel" |
Command name for parallel training of neural network. More... | |
const string | ConstHelpTrainParallelExample = "Performs multiple trainings of neural networks at different parameters in parallel." |
const string | ConstPlotConvergence = "plotconvergence" |
Comamnd name for plotting convergence errors. More... | |
const string | ConstHelpPlotConvergence = "Plots convergence errors of parallel trainings. Args: " |
const string | ConstPlotTrainingTime = "plottrainingtime" |
Comamnd name for plotting training time. More... | |
const string | ConstHelpPlotTrainingTime = "Plots training times of parallel trainings. Args: " |
const string | ConstPlotVerificationResponse = "PlotVerificationResponse" |
Comamnd name for testing network response for verification points(plots, etc.). More... | |
const string | ConstHelpVerificationResponse = "Plots verification response. " |
const string | ConstPlotTrainingResponse = "PlotTrainingResponse" |
Comamnd name for testing network response for verification points(plots, etc.). More... | |
const string | ConstHelpTrainingResponse = "Plots training response. " |
const string | ConstPlotVerificationAndTrainingPoints = "plotverificationandtrainingpoints" |
Comamnd name for testing network response for verification points on line for all Inputs/outputs. More... | |
const string | ConstHelpPlotVerificationAndTrainingPoints = "Plots network response for verification and training points. " |
const string | ConstPlotNetworkResponseOnLineAll = "PlotNetworkResponseOnLineAll" |
Comamnd name for testing network response for verification points on line for all Inputs/outputs. More... | |
const string | ConstHelpNetworkResponseOnLineAll = "Plots network response for points on line from p1 to p2. " |
const string | ConstPlotNetworkCenteredPoint = "PlotNetworkResponseCenteredPoint" |
Comamnd name for testing network response for centered point from verification or training points for all Inputs/outputs. More... | |
const string | ConstHelpPlotNetworkCenteredPoint = "Plots network response for centered point from verification or training set. " |
const string | ConstSensitivity = "Sensitivity" |
Comamnd name for testing network response for centered point from verification or training points for all Inputs/outputs. More... | |
const string | ConstHelpSensitivity = "Executes sensitivity test. " |
int | ParallelNumServersDefault = 4 |
int | ParallelMaxEnqueuedDefault = 10 |
double | LearningRateFrom = 0.05 |
Learning rate from number. More... | |
double | LearningRateTo = 0.8 |
Learning rate to number. More... | |
int | NumLearningRate = 4 |
Number of learning rates in selected region. More... | |
double | MomentumFrom = 0.1 |
Momentum from number. More... | |
double | MomentumTo = 0.6 |
Momentum to number. More... | |
int | NumMomentum = 4 |
Number of momentum in selected region. More... | |
double | AlphaFrom = 1.2 |
Alpha value from number. More... | |
double | AlphaTo = 1.8 |
Alpha value to number. More... | |
int | NumAlpha = 4 |
Number of alpha values in selected region. More... | |
int | MaxEpochs = 1000 |
Max number of epochs. More... | |
int | EpochBundle = 100 |
Number of epochs in bundle. More... | |
double | ToleranceRMS = 0.01 |
Maximum tolerance for RMS. More... | |
int | NumHiddenLayers = 1 |
Number of hidden layer in ANN. More... | |
int[] | NumHiddenNeurons = new int[] { 10 } |
Number of nwueons in each hidden layer. More... | |
double | InputBoundSafetyFactor = 1.3 |
Safety factor for input bound. More... | |
double | OutputBoundSafetyFactor = 1.3 |
Safety factor for output bound. More... | |
bool | allReadFromFile = false |
int | ANNType = 1 |
int | NumNeuronsHidden1 = 40 |
int | NumNeuronsHidden2 = 0 |
double | LearnignRate = 0.3 |
double | Momentum = 0.6 |
double | AlphaValue = 1.0 |
double | RatioVerificationPoints = 0.05 |
bool | showErrorPercentages = false |
bool | verificationPointsFromFile = false |
bool | enableLabels = true |
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const string | ConstTest = "Test" |
Comamnd name for test. More... | |
const string | ConstHelpTest = "Test of functionality." |
const string | ConstCustom = "Custom" |
Comamnd name for test. More... | |
const string | ConstHelpCustom = "Custom command that can be quickly modified as needed, for performing." |
const string | ConstRunParallel = "RunParallel" |
Comamnd name for running parallel simulations. More... | |
const string | ConstHelpRunParallel = "Runs parallel simulations. Args: numThreads numRunsPerThread" |
const string | ConstCollectParallel = "collectparallel" |
Comamnd name for running parallel simulations. More... | |
const string | ConstHelpCollectParallel = "Collects results of parallel simulations. Args: numThreads" |
const string | ConstCreateDistortedModelData = "CreateDistortedModelData" |
Comamnd name for creating distorted model data. More... | |
const string | ConstHelpCreateDistortedModelData |
const string | ConstStochasticCreateInput = "StochasticCreateInput" |
Command name for creating input data for analysis how stochastic variables influence outputs. More... | |
const string | ConstHelpStochasticCreateInput |
const string | ConstStochasticInfluence = "StochasticInfluence" |
Command name for calculating influence of stochastic Gaussian distributed input variables on outputs. More... | |
const string | ConstHelpStochasticInfluence |
const string | ConstCreateBinarySampledData = "CreateBinarySampledData" |
Comamnd name for saving model's sampled data in binary form. More... | |
const string | ConstHelpCreateBinarySampledData |
const string | ConstRestoreBinarySampledData = "RestoreBinarySampledData" |
Comamnd name for restoring sampled data in binary form and storing it in JSON. More... | |
const string | ConstHelpRestoreBinarySampledData |
string | ParallelResultsFilename = "ParallelResults" |
int | ParSimNumRuns = 2 |
Number of runs of simulator in each parallel thread. More... | |
int | ParSimNumThreads = 2 |
Number of jobs that can be executed in parallel. More... | |
int | ParSimSavingFrequency = 4 |
Specifies how often the results are saved. More... | |
string | ParSimResultFilename = "GatheredNeuralTrainingData.json" |
bool | saveGraphs = false |
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const string | ConstTest = "Test" |
Comamnd name for test. More... | |
const string | ConstHelpTest = "Test of functionality." |
const string | ConstCustom = "Custom" |
Comamnd name for test. More... | |
const string | ConstHelpCustom = "Custom command that can be quickly modified as needed, for performing." |
const string | ConstRunParallel = "RunParallel" |
Comamnd name for running parallel simulations. More... | |
const string | ConstHelpRunParallel = "Runs parallel simulations. Args: numThreads numRunsPerThread" |
const string | ConstCollectParallel = "collectparallel" |
Comamnd name for running parallel simulations. More... | |
const string | ConstHelpCollectParallel = "Collects results of parallel simulations. Args: numThreads" |
const string | ConstCreateDistortedModelData = "CreateDistortedModelData" |
Comamnd name for testing network response for centered point from verification or training points for all Inputs/outputs. More... | |
const string | ConstHelpCreateDistortedModelData |
string | ParallelResultsFilename = "ParallelResults" |
int | ParSimNumRuns = 2 |
Number of runs of simulator in each parallel thread. More... | |
int | ParSimNumThreads = 2 |
Number of jobs that can be executed in parallel. More... | |
int | ParSimSavingFrequency = 4 |
Specifies how often the results are saved. More... | |
string | ParSimResultFilename = "GatheredNeuralTrainingData.json" |
bool | saveGraphs = false |
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const string | ConstDefaultHelp = "Help" |
Default command name for help. More... | |
const string | ConstHelpDefaultUniversal = "?" |
Universal name of the help command. More... | |
const string | ConstDefaultTestScrip = "TestScript" |
Default command name for test method. More... | |
Protected Member Functions | |
override void | InitializeThis (string[] arguments) |
Inializes the current script object. More... | |
override void | LoadJson (string path, ref INeuralApproximator network) |
Loads the neural network approximator from the specified file. More... | |
override void | SaveJson (INeuralApproximator approximator, string trainedNetworkFilePath) |
Saves the specified trained network to a file. More... | |
override INeuralApproximator | CreateApproximator (int annType=2) |
Creates and returns a new neural network approximator, with the basic properties pre-set to default values, dependent on the type of the requires approximator. More... | |
virtual NeuralApproximatorBase | CreateParallelApproximator () |
Creates a parallel approximator for parallel training and comparison of training resultw at different parameters. More... | |
virtual NeuralTrainingParameters | TrainNetwork (NeuralTrainingParameters parameters) |
Trains network. Used as execution delegate on job containers. More... | |
virtual void | TrainSerial () |
Performs training of neural networks with the specified parameters in non-parallel way. More... | |
virtual void | TrainParallel () |
Performs parallel training of neural networks with the specified parameters. More... | |
virtual void | TrainParallel (bool isParallel) |
Performs parallel training of neural networks with the specified parameters. More... | |
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override void | InitializeThis (string[] arguments) |
Inializes the current script object. More... | |
virtual void | PrintInternalCommandAndArguments (string[] arguments) |
Prints internal command being executed and its actual arguments More... | |
virtual void | GetSimulationInputDefault (ref IVector result) |
Gets the vector of default values of simulation parameters and stores it in the specified vector. More... | |
virtual void | GetSimulationInputMin (ref IVector result) |
Gets the vector of lower bounds on simulation parameters and stores it in the specified vector. More... | |
virtual void | GetSimulationInputMax (ref IVector result) |
Gets the vector of upper bounds on simulation parameters and stores it in the specified vector. More... | |
virtual void | ParSimPrepareDirectories (int numThreads) |
Prepares data for the specified number of parallel execution threads. More... | |
virtual void | ParSimWaitAllCompletion () |
Waits until all parallel jobs complete. More... | |
virtual void | ParSimAsyncCallback (IAsyncResult ar) |
Callback method for asynchronous runs. More... | |
virtual string | ParSimGetDirectoryExtension (int threadIndex) |
Returns directory extension for data directory used in the specified parallel calculation thread. More... | |
virtual void | GetNeuralInputDefault (ref IVector result) |
Gets the vector of default values of neural parameters and stores it in the specified vector. More... | |
virtual void | GetNeuralInputMin (ref IVector result) |
Gets the vector of lower bounds on neural parameters and stores it in the specified vector. More... | |
virtual void | GetNeuralInputMax (ref IVector result) |
Gets the vector of upper bounds on neural parameters and stores it in the specified vector. More... | |
virtual void | GetRandomNeuralInputFromDataDefinition (IRandomGenerator rand, ref IVector result) |
Generates a random vector of neural input parameters whose elements (components) lie within the lower and upper bounds on parameters, and stores it to the specified vector. More... | |
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override string | RunThis (string[] arguments) |
Throws NotImplementedException. More... | |
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virtual void | CreateNewSimulator (string simulatorPath, ref IResponseEvaluatorVectorSimple simulator) |
Creates a new simulator that uses the specified simulator directory, and stores it to the specified variable. More... | |
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delegate string | CommandMethod (string commandName, string[] args) |
Delegate for internal command methods. More... | |
ICommandLineApplicationInterpreter | Script_CreateInterpreter () |
Creates and returns an interpreter that can be used as script's internal interpreter for running script's commands. More... | |
virtual string | Script_CommandHelp (string[] arguments) |
Prints help. More... | |
virtual string | Script_CommandTestScript (string[] arguments) |
Prints help. More... | |
Properties | |
virtual List < NeuralTrainingParameters > | TrainingParametersAndResults [get] |
List of training parameters for which training should be performed. More... | |
virtual string | TrainingParametersPath [get] |
Path to the default location lwhere training parameters and convergence results from multiple training attempts are stored. More... | |
virtual string | TrainingLimitsPath [get] |
Path to the default location lwhere training limits are stored. More... | |
virtual string | TrainingResultsPath [get] |
Path to the default location lwhere training complete results are stored. More... | |
virtual string | TrainingResultsCSVPath [get] |
Path to the default location lwhere training complete results are stored. More... | |
virtual string | OptimalTrainingParametersPath [get] |
Path to the default location where optimal training parameters for the current problem are stored. More... | |
virtual string | AdditionalTrainingParametersFilename [get, set] |
Eventual relative path of an additional file name where results are stored. More... | |
int | ParallelNumServers [get, set] |
Number of parallel servers that will be used for parallel execution of jobs. More... | |
int | ParallelMaxEnqueued [get, set] |
Maximal number of enqueued jobs when performing parallel execution of jobs. More... | |
int | ParallelSleepTimeMs [get, set] |
Sleeping time in milliseconds. More... | |
bool | ParallelIsServerMode [get, set] |
Whether servers will operate in server mode (true) or by running each job in a new thread (false). More... | |
int | ParallelClientOutputLevel [get, set] |
Client's output level when executing parallel jobs. More... | |
int | ParallelOutputLevel [get, set] |
Output level for parallel dispatcher, servers and job containers when executing parallel jobs. More... | |
bool | ParallelIsTestMode [get, set] |
Specifies whether parallel jobs will be executing in testing mode (where e.g. delay times apply). More... | |
double | ParallelDelayTimeSeconds [get, set] |
Delay in seconds used in parallel execution. This is useful only for testing purposes, default is 0. More... | |
double | ParallelDelayTimeRelativeError [get, set] |
Relative variation in delay time used in parallel execution. This is useful only for testing purposes, default is 0. More... | |
virtual int | ParallelSavingFrequency [get, set] |
Frequency of saving the results of parallel execution: More... | |
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override int | NumOptimizationParameters [get, protected set] |
Number of optimization parameters. More... | |
override int | NumOptimizationConstraints [get, protected set] |
Number of optimization constraints. More... | |
virtual InputOutputDataDefiniton | SimulationDataDefinition [get] |
Simulation data definition (input and output used by simulator). More... | |
virtual int | NumSimulationParameters [get, protected set] |
Number of simulation input parameters. More... | |
virtual int | NumSimulationOutputs [get, protected set] |
Number of simulation output values. More... | |
virtual IBoundingBox | SimulationInputBounds [get, protected set] |
Bounds on simulation input parameters. More... | |
string | ParallelResultsFileExtension [get, protected set] |
virtual NeuraApproximationFileManager | NeuralFM [get] |
File manager that provides access to trained neural network and related data. More... | |
virtual INeuralApproximator | TrainedNetwork [get, protected set] |
Trained neural network. More... | |
virtual List< IVector > | SensitivityVerificationResults [get, protected set] |
virtual List< IVector > | SensitivityTrainingResults [get, protected set] |
virtual INeuralApproximator | XXX_TrainedNetwork [get, protected set] |
Trained neural network. More... | |
virtual InputOutputDataDefiniton | NeuralDataDefinition [get, protected set] |
Neural data definition. More... | |
virtual SampledDataSet | TrainingData [get, set] |
virtual SampledDataSet | VerificationData [get, set] |
virtual int | NumNeuralParameters [get, protected set] |
Number of neural network input parameters. More... | |
virtual int | NumNeuralOutputs [get, protected set] |
Number of neural network output values. More... | |
virtual IBoundingBox | NeuralInputBounds [get, protected set] |
Bounds on neural input parameters. More... | |
virtual SampledDataSet | NeuralTrainingData [get] |
Gets the training data. More... | |
virtual int | NumAllTrainingPoints [get] |
Gets number of all training points, including verification points. More... | |
virtual IVector | SomeNeuralInput [get] |
Returns some vector of input parameters that is within the range. More... | |
virtual IVector | NeuralInputFromFile [get] |
Vector of input parameters read form the file. More... | |
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virtual string | SimCastingRobertRootDirectory [get, protected set] |
Root directory for casting simulator. More... | |
virtual string | SimCastingRobertProjectName [get, protected set] |
Project name for interfacing Robert Vertnik's casting simulator. More... | |
virtual string | SimCastingRobertSimulationName [get, protected set] |
Simulation name for interfacing Robert Vertnik's casting simulator. More... | |
virtual SimCastingRobertFileManager | SimCastingRobertFM [get, set] |
File manager for interfacing Robert Vertnik's casting simulator. Lazy evaluation: The object is created when first accessed, if possible. For this, ProjectName, SimulationName and SimCastingRobertRootDirectory must be defined. More... | |
virtual string | SimConvectionRobertRootDirectory [get, protected set] |
Root directory for Convection simulator. More... | |
virtual string | SimConvectionRobertProjectName [get, protected set] |
Project name for interfacing Robert Vertnik's Convection simulator. More... | |
virtual string | SimConvectionRobertSimulationName [get, protected set] |
Simulation name for interfacing Robert Vertnik's Convection simulator. More... | |
virtual ConvectionRobertFileManager | SimConvectionRobertFM [get, set] |
File manager for interfacing Robert Vertnik's casting simulator. Lazy evaluation: The object is created when first accessed, if possible. For this, ProjectName, SimulationName and SimCastingRobertRootDirectory must be defined. More... | |
virtual ConvectionRev1RobertFileManager | SimConvectionRev1RobertFM [get, set] |
File manager for interfacing Robert Vertnik's casting simulator. Lazy evaluation: The object is created when first accessed, if possible. For this, ProjectName, SimulationName and SimCastingRobertRootDirectory must be defined. More... | |
virtual string | SimKosecDirectoryRelativePath [get, set] |
Relative path of the directory that contains Kosec' simulation data, with respect to optimization directory. More... | |
string | SimKosecDirectory [get, set] |
Path so directory containing Kosec' simulator data. More... | |
virtual SimKosecFileManagerBase | SimKosecFM [get, protected set] |
File manager of simulatior of Gregor Kosec. More... | |
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override IResponseEvaluatorVectorSimple | Simulator [get, protected set] |
override int | NumOptimizationParameters [get, protected set] |
Throws NotImplementedException. More... | |
override int | NumOptimizationConstraints [get, protected set] |
Throws NotImplementedException. More... | |
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abstract int | NumOptimizationParameters [get, protected set] |
Number of optimization parameters. More... | |
abstract int | NumOptimizationConstraints [get, protected set] |
Number of optimization constraints. More... | |
virtual InputOutputDataDefiniton | SimulationDataDefinition [get] |
Simulation data definition (input and output used by simulator). More... | |
virtual int | NumSimulationParameters [get, protected set] |
Number of simulation input parameters. More... | |
virtual int | NumSimulationOutputs [get, protected set] |
Number of simulation output values. More... | |
virtual IBoundingBox | SimulationInputBounds [get, protected set] |
Bounds on simulation input parameters. More... | |
string | ParallelResultsFileExtension [get, protected set] |
virtual NeuraApproximationFileManager | NeuralFM [get] |
File manager that provides access to trained neural network and related data. More... | |
virtual INeuralApproximator | TrainedNetwork [get, protected set] |
Trained neural network. More... | |
virtual List< IVector > | SensitivityVerificationResults [get, protected set] |
virtual List< IVector > | SensitivityTrainingResults [get, protected set] |
virtual INeuralApproximator | XXX_TrainedNetwork [get, protected set] |
Trained neural network. More... | |
virtual InputOutputDataDefiniton | NeuralDataDefinition [get, protected set] |
Neural data definition. More... | |
virtual SampledDataSet | TrainingData [get, set] |
virtual SampledDataSet | VerificationData [get, set] |
virtual int | NumNeuralParameters [get, protected set] |
Number of neural network input parameters. More... | |
virtual int | NumNeuralOutputs [get, protected set] |
Number of neural network output values. More... | |
virtual IBoundingBox | NeuralInputBounds [get, protected set] |
Bounds on neural input parameters. More... | |
virtual SampledDataSet | NeuralTrainingData [get] |
Gets the training data. More... | |
virtual int | NumAllTrainingPoints [get] |
Gets number of all training points, including verification points. More... | |
virtual IVector | SomeNeuralInput [get] |
Returns some vector of input parameters that is within the range. More... | |
virtual IVector | NeuralInputFromFile [get] |
Vector of input parameters read form the file. More... | |
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abstract IResponseEvaluatorVectorSimple | Simulator [get, protected set] |
Simulator that is used to calculate vector response. More... | |
virtual bool | SimulatorSuppressOutput [get, set] |
A flag indicating whether simulators should suppress output or not, default is false. More... | |
virtual string | ProjectName [get, protected set] |
Name of the current project, used in some simulation and other interfaces. More... | |
virtual string | SimulationName [get, protected set] |
Name of the current simulation, used in some simulation and other interfaces. More... | |
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bool | IsLoadable [get, set] |
Either or not the script can be dynamically loaded. More... | |
bool | IsRunnable [get, set] |
Either or not the script can be run (some scripts only support other tasks). More... | |
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StopWatch1 | Timer [get] |
virtual IRandomGenerator | Random [get, protected set] |
Random generator used by the current object. More... | |
virtual string | OptimizationDirectory [get, set] |
Optimization directory. This directory is a base directory for data used optimization and neural network - based approximation servers and for other directories that contain data for specific tasks. More... | |
virtual string | WorkingDirectory [get, set] |
Working directory. This directory is a base directory for data used by the script. It is usually obtained as parent directory of the optimization directory. More... | |
virtual IAnalysis | Analysis [get, protected set] |
Direct analysis object used in optimization. Initialized when first accessed with the embedded class, whose analysis function calls Analyse(...). More... | |
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string | EmbeddedCommandName [get, set] |
Command that was used to launch the current embedded application script. More... | |
object | Lock [get] |
This object's central lock object to be used by other object. Do not use this object for locking in class' methods, for this you should use InternalLock. More... | |
string[] | InitializationArguments [get, set] |
Arguments used by the initialization method. WARNING: arguments can only be set before initialization is performed. Initialization is performed either implicitly at the first call to the Run method or explicitly by calling the Initialize method. More... | |
virtual bool | IsInitialized [get, protected set] |
Whether the object has been initialized or not. More... | |
static int | DefaultOutputLevel [get, set] |
int | OutputLevel [get, set] |
Level of output to console produced by some operations of the current object. More... | |
virtual ICommandLineApplicationInterpreter | Script_Interpreter [get, protected set] |
Script's internal interpreter that takes care for execution of installed internal commands. More... | |
SortedList< string, string > | Script_CommandHelpStrings [get] |
Contains help strings associated with script commands installed on interpreter. More... | |
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string | EmbeddedCommandName [get, set] |
Command that was used to launch the current embedded application script. More... | |
string[] | InitializationArguments [get, set] |
Arguments used by the initialization method. More... | |
bool | IsInitialized [get] |
Whether the object has been initialized or not. More... | |
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object | Lock [get] |
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bool | IsLoadable [get, set] |
Either or not the script can be dynamically loaded. More... | |
bool | IsRunnable [get, set] |
Either or not the script can be run (some scripts only support other tasks). More... | |
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INeuralApproximator | TrainedNetwork [get] |
Traint artificial neural network. More... | |
InputOutputDataDefiniton | NeuralDataDefinition [get] |
Neural data definition. More... | |
Private Attributes | |
List< NeuralTrainingParameters > | _trainingParameters |
int | _parallelMaxEnqueued |
HtmlWriter | html = null |
Additional Inherited Members | |
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static string | TrainedNetworkAlternativeLovation = null |
If different than null, this static property provides alternative location of the file which neural network approximator is resd from, in terms of path relative to optimization directory. More... | |
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static string | TrainedNetworkAlternativeLovation = null |
If different than null, this static property provides alternative location of the file which neural network approximator is resd from, in terms of path relative to optimization directory. More... | |
Base class for loadable scripts that are used as custom applications that inclued neural networks approximations and optimization.
This is a concrete class and can be used directly.
$A Igor xx;
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inline |
|
inlineprotectedvirtual |
Inializes the current script object.
arguments | Initialization arguments. The first argument must be the working directory path. |
Reimplemented from IG.Script.LoadableScriptShellNeural.
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inlineprotectedvirtual |
Loads the neural network approximator from the specified file.
In the derived classes, this method can be overridden in order to handle more types of neurel network approximators.
path | File path. |
network | Neual network into which loaded data is restored. |
Reimplemented from IG.Script.LoadableScriptShellNeuralBase.
References IG.Num.NeuralApproximatorBase.LoadJson().
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inlineprotectedvirtual |
Saves the specified trained network to a file.
This method hould be
approximator | Trained neural network to be saved. |
trainedNetworkFilePath | Path of the file into which trained neural network is saved in JSON form. |
Reimplemented from IG.Script.LoadableScriptShellNeuralBase.
References IG.Num.NeuralApproximatorBase.SaveJson().
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inlineprotectedvirtual |
Creates and returns a new neural network approximator, with the basic properties pre-set to default values, dependent on the type of the requires approximator.
This methods should be overridden in derived classes in order to allow use of other neural approximatiors.
annType | Type of the neural network approximator: |
1 - NeuralApproximatorNeuron (only availeble in more specialized libraries for internal use).
2 - NeuralApproximatorAforge (basic neural network approximator in this library).
Reimplemented from IG.Script.LoadableScriptShellNeuralBase.
References IG.Num.INeuralApproximator.InputNeuronsRange, IG.Num.INeuralApproximator.OutputNeuronsRange, IG.Num.IBoundingBox.Reset(), IG.Num.INeuralApproximator.SigmoidAlphaValue, and IG.Num.IBoundingBox.UpdateAll().
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inlinevirtual |
Adds commands to the internal interpreter.
interpreter | Interpreter where commands are executed. |
helpStrings | List containg help strings. |
Reimplemented from IG.Script.LoadableScriptShellNeuralBase.
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inline |
Train the artificial neural network.
annType | 1 - NeuronDotNet, 2 - Aforge. |
NumNeurons | Number of neirons in 1st hidden layer. |
MaxEpochs | Max epochs. |
EpochsInBundle | Epochs in bundle. |
LearnignRate | Learning rate. |
Momentum | Momentum. |
InputSafetyFactor | Input safety factor. |
OutputSafetyFactor | Output safety factor. |
PercentVerificationPoints | Percentage for verification points. |
References IG.Num.NeuralApproximatorBase.CalculateOutput(), IG.Num.NeuralApproximatorBase.ConvergenceErrorsTrainingRmsList, IG.Lib.IndexList.CreateRandom(), IG.Num.NeuralApproximatorBase.EpochsInBundle, IG.Num.NeuralApproximatorBase.InputBoundsSafetyFactor, IG.Num.NeuralApproximatorBase.InputLength, IG.Num.NeuralApproximatorBase.InputNeuronsRange, IG.Num.NeuralApproximatorBase.LearningRate, IG.Num.SampledDataSet.LoadJson(), IG.Num.NeuralApproximatorBase.MaxEpochs, IG.Num.NeuralApproximatorBase.Momentum, IG.Num.NeuralApproximatorBase.MultipleNetworks, IG.Num.NeuralApproximatorBase.OutputBoundsSafetyFactor, IG.Num.NeuralApproximatorBase.OutputLength, IG.Num.NeuralApproximatorBase.OutputLevel, IG.Num.NeuralApproximatorBase.OutputNeuronsRange, IG.Num.IBoundingBox.Reset(), IG.Num.NeuralApproximatorBase.SaveConvergenceRms, IG.Neural.NeuralApproximatorBaseExt.SaveJson(), IG.Num.NeuralApproximatorBase.SetHiddenLayers(), IG.Num.NeuralApproximatorBase.SetTrainingAndVerificationData(), IG.Num.NeuralApproximatorBase.SigmoidAlphaValue, IG.Lib.StopWatch1.Start(), IG.Lib.StopWatch1.Stop(), IG.Num.VectorBase.Subtract(), IG.Lib.StopWatch1.Time, IG.Num.NeuralApproximatorBase.ToleranceRms, IG.Num.IVector.ToString(), IG.Num.NeuralApproximatorBase.ToString(), IG.Num.NeuralApproximatorBase.TrainingData, IG.Num.NeuralApproximatorBase.TrainNetwork(), IG.Num.IBoundingBox.UpdateAll(), and IG.Num.NeuralApproximatorBase.VerificationIndices.
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inlineprotectedvirtual |
Creates a parallel approximator for parallel training and comparison of training resultw at different parameters.
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inlineprotectedvirtual |
Trains network. Used as execution delegate on job containers.
Network that is trained must be created within this method, and training data must be provided to it (this should be done within a block that is locked by object's main lock).
parameters | Training parameters used for training. |
References IG.Num.NeuralApproximatorBase.ConvergenceErrorsTrainingMaxList, IG.Num.NeuralApproximatorBase.ConvergenceErrorsTrainingRmsList, IG.Num.NeuralApproximatorBase.ConvergenceErrorsVerificationMaxList, IG.Num.NeuralApproximatorBase.ConvergenceErrorsVerificationRmsList, IG.Num.NeuralTrainingParameters.CopyFrom(), IG.Num.NeuralTrainingParameters.CopyTo(), IG.Num.NeuralApproximatorBase.EpochCount, IG.Num.NeuralApproximatorBase.GetErrorsTrainingMax(), IG.Num.NeuralApproximatorBase.GetErrorsTrainingMeanAbs(), IG.Num.NeuralApproximatorBase.GetErrorsTrainingRms(), IG.Num.NeuralApproximatorBase.GetErrorsVerificationMax(), IG.Num.NeuralApproximatorBase.GetErrorsVerificationMeanAbs(), IG.Num.NeuralApproximatorBase.GetErrorsVerificationRms(), IG.Num.NeuralTrainingParameters.NumEpochs, IG.Num.NeuralApproximatorBase.SaveConvergenceRms, IG.Num.NeuralApproximatorBase.SetTrainingAndVerificationData(), IG.Lib.StopWatch1.Start(), IG.Lib.StopWatch1.Stop(), IG.Lib.StopWatch1.Time, IG.Num.NeuralTrainingParameters.TrainingTime, and IG.Num.NeuralApproximatorBase.TrainNetwork().
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inlineprotectedvirtual |
Performs training of neural networks with the specified parameters in non-parallel way.
|
inlineprotectedvirtual |
Performs parallel training of neural networks with the specified parameters.
|
inlineprotectedvirtual |
Performs parallel training of neural networks with the specified parameters.
Has the ability to run jobs on the same therad where function is executed.
isParallel | If true then jobs are run serially on the same thread on which function is called. |
References IG.Num.ParallelJobContainerBase.DefaultIsTestMode, IG.Num.ParallelJobContainerBase.DefaultOutputLevel, IG.Num.NeuralTrainingParameters.IsNetworkTrained, IG.Num.NeuralTrainingParameters.SaveJson(), IG.Lib.StopWatch1.Start(), and IG.Lib.StopWatch1.Time.
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inline |
Plots graph for training Rms errors.
trainParameters | List of neural training parameters. |
showMinAndMaxError | Plot minimum and maximum error convergences. |
showMinToMaxError | Plot error convergences from min to max. |
showBestNErrors | Plot selected number of best error convergences. |
numErrorsShow | Number of error convergences shown on graph |
numBoundles | Number of bundles where sorting of convergences stars. |
References IG.Neural.NeuralTrainingResultsPlotter1.PlotConvergenceTrainingRms().
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inline |
Plots graph for training Max errors.
trainParameters | List of neural training parameters. |
showMinAndMaxError | Plot minimum and maximum error convergences. |
showMinToMaxError | Plot error convergences from min to max. |
showBestNErrors | Plot selected number of best error convergences. |
numErrorsShow | Number of error convergences shown on graph |
numBoundles | Number of bundles where sorting of convergences stars. |
References IG.Neural.NeuralTrainingResultsPlotter1.PlotConvergenceTrainingMax().
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inline |
Plots graph for verification Rms errors.
trainParameters | List of neural training parameters. |
showMinAndMaxError | Plot minimum and maximum error convergences. |
showMinToMaxError | Plot error convergences from min to max. |
showBestNErrors | Plot selected number of best error convergences. |
numErrorsShow | Number of error convergences shown on graph |
numBoundles | Number of bundles where sorting of convergences stars. |
References IG.Neural.NeuralTrainingResultsPlotter1.PlotConvergenceVerificationRms().
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inline |
Plots graph for verification Max errors.
trainParameters | List of neural training parameters. |
showMinAndMaxError | Plot minimum and maximum error convergences. |
showMinToMaxError | Plot error convergences from min to max. |
showBestNErrors | Plot selected number of best error convergences. |
numErrorsShow | Number of error convergences shown on graph |
numBoundles | Number of bundles where sorting of convergences stars. |
References IG.Neural.NeuralTrainingResultsPlotter1.PlotConvergenceVerificationMax().
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inlinevirtual |
Applies basic plot settings for various kinds of plots.
plotter | Plotter used for plotting graphs for which settings are set. |
plot | Plot that contains graphic objects. |
|
inlinevirtual |
References IG.Gr.PlotZedgraphCurve.LineColor, and IG.Gr.PlotZedgraphCurve.PointColor.
|
inlinevirtual |
Applies basic plot settings for basic kinds of curve plots.
plotter | Plotter used for plotting graphs for which settings are set. |
plot | Curve plot that contains graphic objects. |
References IG.Gr.PlotZedgraphCurve.LineWidth, IG.Gr.PlotZedgraphCurve.PointColor, and IG.Gr.PlotZedgraphCurve.PointSize.
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inlinevirtual |
Applies basic plot settings for basic kinds of curve plots where groups of curves are plotted.
plotter | Plotter used for plotting graphs for which settings are set. |
plot | Curve plot that contains graphic objects. |
i | Current index of the plot (running from 0) for which settings are applied. |
max | Maximal index of the plot for which settings are applied. |
References IG.Lib.ColorScaleBase.CreateRainbow(), IG.Lib.ColorScaleBase.GetColor(), IG.Gr.PlotZedgraphCurve.LineColor, IG.Gr.PlotZedgraphCurve.LineWidth, IG.Gr.PlotZedgraphCurve.PointColor, and IG.Gr.PlotZedgraphCurve.PointSize.
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inlinevirtual |
Applies basic plot settings for basic kinds of curve plots where groups of curves are plotted.
plotter | Plotter used for plotting graphs for which settings are set. |
plot | Curve plot that contains graphic objects. |
i | Current index of the plot (running from 0) for which settings are applied. |
max | Maximal index of the plot for which settings are applied. |
References IG.Lib.ColorScaleBase.Create(), IG.Lib.ColorScaleBase.GetColor(), IG.Gr.PlotZedgraphCurve.LineColor, IG.Gr.PlotZedgraphCurve.LineWidth, IG.Gr.PlotZedgraphCurve.PointColor, and IG.Gr.PlotZedgraphCurve.PointsVisible.
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inlinevirtual |
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inlinevirtual |
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inlinevirtual |
Plots a parametrix stydy of neural network approximated response by plotting dependency of the specified output on the specified parameter.
parameters | Vector of parameters around which parametric study is performed. For all plotted points, all elements of vector of parameters except the element that is changed are obtained from this vector. |
whichOut | Index of the approximated output value that is plotted. |
whichParam | Specifies which parameter is changed on the plot. |
minParam | Lower bound for the variable parameter. |
maxParam | Upper bound for the variable parameter. |
numPoints | Number of points in which approximated response is calculated. |
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Num.VectorBase.Copy(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Gr.ZedGraphWindow.GraphControl, IG.Gr.PlotterZedGraph.Title, IG.Gr.PlotterZedGraph.Update(), IG.Gr.PlotterZedGraph.XAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabel.
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inlinevirtual |
Plots some parametric studies of responses generated by the trained network.
numPoints | Number of plotted points on each plot where neural network response is calculated. |
|
inlinevirtual |
Plots a parametrix stydy of neural network approximated response by plotting dependency of the specified output on the specified parameter.
parameters | Vector of parameters around which parametric study is performed. For all plotted points, all elements of vector of parameters except the element that is changed are obtained from this vector. |
whichOut | Index of the approximated output value that is plotted. |
whichParam | Specifies which parameter is changed on the plot. |
minParam | Lower bound for the variable parameter. |
maxParam | Upper bound for the variable parameter. |
numPoints | Number of points in which approximated response is calculated. |
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Num.VectorBase.Copy(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Num.IBoundingBox.GetMax(), IG.Num.IBoundingBox.GetMin(), IG.Gr.ZedGraphWindow.GraphControl, IG.Gr.PlotterZedGraph.Title, IG.Gr.PlotterZedGraph.Update(), IG.Gr.PlotterZedGraph.XAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabel.
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inlinevirtual |
Plots some parametric studies of responses generated by the trained network.
numPoints | Number of plotted points on each plot where neural network response is calculated. |
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inlinevirtual |
Plots the specified table of analysis results.
tabres | Table of results to be plotted. |
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Num.AnalysisResults.CalculatedConstraints, IG.Num.AnalysisResults.CalculatedObjective, IG.Gr.PlotterZedGraph.CreateDefault(), IG.Gr.ZedGraphWindow.GraphControl, IG.Num.AnalysisResults.NumConstraints, IG.Num.AnalysisResults.Parameters, IG.Num.VectorBase.Subtract(), IG.Gr.PlotterZedGraph.Title, IG.Gr.PlotterZedGraph.Update(), IG.Gr.PlotterZedGraph.XAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabel.
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inlinevirtual |
Plots the specified table of calculated approximation results.
tabApproximationResults | Array of two arrays - of approximation inputs and of corresponding approximatino outputs. |
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Gr.ZedGraphWindow.GraphControl, IG.Num.VectorBase.Subtract(), IG.Gr.PlotterZedGraph.Title, IG.Gr.PlotterZedGraph.Update(), IG.Gr.PlotterZedGraph.XAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabel.
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inline |
Plots the ANN approximation on verification points and real points.
showErrorInPercentage | True - show error in percentage, false - show (real / approximation) difference. |
verificationPoFromFile | True - verification points loaded from file. |
References IG.Lib.HtmlWriter.AddHeading1(), IG.Lib.HtmlWriter.AddHeading2(), IG.Lib.HtmlWriter.AddImage(), IG.Lib.HtmlWriter.AddNewLine(), IG.Lib.HtmlWriter.CloseWriter(), IG.Lib.HtmlWriter.EndDocument(), and IG.Lib.UtilSystem.OpenFileInDefaultBrowser().
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inline |
Plots the ANN approximation on verification points and real points.
verificationFromFile | True - verification points loaded from file. |
whichOutput | Which output to plot. |
|
inline |
Plots the ANN approximation on verification points and real points.
showErrorInPercentage | True - show error in percentage, false - show (real / approximation) difference. |
verificationFromFile | True - verification points loaded from file. |
whichOutput | Which output to plot. |
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Num.SampledDataSet.GetOutputRange(), IG.Gr.ZedGraphWindow.GraphControl, IG.Gr.PlotterZedGraph.GraphPane, IG.Gr.PlotZedGraphBase.LegendString, IG.Lib.SortedUniqueItemList< Type >.Length, IG.Num.SampledDataSet.Length, IG.Num.SampledDataSet.LoadJson(), IG.Gr.PlotterZedGraph.Title, IG.Num.INeuralApproximator.TrainingData, IG.Gr.PlotterZedGraph.Update(), IG.Num.INeuralApproximator.VerificationIndices, IG.Gr.PlotterZedGraph.XAxis, IG.Gr.PlotterZedGraph.XAxisLabel, IG.Gr.PlotterZedGraph.YAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabelFont.
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inline |
Calculates response on verification points.
verificationFromFile | True - verification points loaded from file. |
whichOut | Which output to plot. |
verificationPointsResponse | 0 - relativeError(%), 1 - approximation value, 2 - real value, 3 - id. |
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inline |
Calculates response on verification points.
verificationFromFile | True - verification points loaded from file. |
whichOut | Which output to plot. |
verificationPointsResponse | 0 - relativeError(%), 1 - approximation value, 2 - real value, 3 - id. |
References IG.Num.SampledDataSet.LoadJson().
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inline |
Plots the ANN approximation on training points and real points.
References IG.Lib.HtmlWriter.AddHeading1(), IG.Lib.HtmlWriter.AddHeading2(), IG.Lib.HtmlWriter.AddImage(), IG.Lib.HtmlWriter.AddNewLine(), IG.Lib.HtmlWriter.CloseWriter(), IG.Lib.HtmlWriter.EndDocument(), and IG.Lib.UtilSystem.OpenFileInDefaultBrowser().
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inline |
Plots the ANN approximation on training points and real points.
whichOutput | Which output to plot. |
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inline |
Plots the ANN approximation on training points and real points.
showErrorInPercentage | True - show error in percentage, false - show (real / approximation) difference. |
whichOutput | Which output to plot. |
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Lib.SortedUniqueItemList< Type >.Contains(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Num.SampledDataSet.GetOutputRange(), IG.Gr.ZedGraphWindow.GraphControl, IG.Gr.PlotterZedGraph.GraphPane, IG.Gr.PlotZedGraphBase.LegendString, IG.Num.SampledDataSet.Length, IG.Gr.PlotterZedGraph.Title, IG.Num.INeuralApproximator.TrainingData, IG.Gr.PlotterZedGraph.Update(), IG.Num.INeuralApproximator.VerificationIndices, IG.Gr.PlotterZedGraph.XAxis, IG.Gr.PlotterZedGraph.XAxisLabel, IG.Gr.PlotterZedGraph.YAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabelFont.
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inline |
Calculates response on training points.
whichOut | Which output to plot. |
verificationPointsResponse | 0 - relativeError(%), 1 - approximation value, 2 - real value, 3 - id. |
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inline |
Network response on the points calculated between two selected points.
$A Tako78 Nov12;
References IG.Num.SampledDataSet.GetInputParameters(), IG.Num.SampledDataSet.GetInputRange(), IG.Num.SampledDataSet.InputLength, IG.Num.SampledDataSet.Length, and IG.Num.INeuralApproximator.TrainingData.
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inline |
Network response on the points calculated between two selected points.
p1Training | First defined point. |
p2Training | Second defined point. |
numPoints | Number of points on the line between p1 and p2. |
References IG.Num.VectorBase.Add(), IG.Lib.HtmlWriter.AddHeading1(), IG.Lib.HtmlWriter.AddHeading2(), IG.Lib.HtmlWriter.AddImage(), IG.Lib.HtmlWriter.AddNewLine(), IG.Lib.HtmlWriter.AddParagraph(), IG.Lib.HtmlWriter.CloseWriter(), IG.Num.VectorBase.Copy(), IG.Lib.HtmlWriter.EndDocument(), IG.Num.VectorBase.Multiply(), IG.Lib.UtilSystem.OpenFileInDefaultBrowser(), and IG.Num.VectorBase.Subtract().
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inline |
Plots the ANN approcimation on the verification points and/or real points.
selectedTrainingPoints | Training points. |
selectedVerificationPoints | Verification points. |
whichOutput | Which output to plot. |
whichParameter | Which parameter to plot. |
min | Minimum range. |
max | Maximum range. |
$A Tako78 Nov12;
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Num.IVector.GetCopy(), IG.Gr.ZedGraphWindow.GraphControl, IG.Gr.PlotterZedGraph.GraphPane, IG.Gr.PlotZedGraphBase.LegendString, IG.Gr.PlotterZedGraph.Title, IG.Gr.PlotterZedGraph.Update(), IG.Gr.PlotterZedGraph.XAxis, IG.Gr.PlotterZedGraph.XAxisLabel, IG.Gr.PlotterZedGraph.YAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabelFont.
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inline |
Returns verification points from complete training data set in approximator.
$A Tako78 Nov12;
References IG.Num.SampledDataSet.AddElement(), IG.Lib.SortedUniqueItemList< Type >.Contains(), IG.Num.SampledDataSet.GetInputParameters(), IG.Num.SampledDataSet.GetOutputValues(), IG.Num.SampledDataSet.InputLength, IG.Num.SampledDataSet.Length, IG.Num.SampledDataSet.OutputLength, IG.Num.INeuralApproximator.TrainingData, and IG.Num.INeuralApproximator.VerificationIndices.
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inline |
Returns training points from complete training data set in approximator.
$A Tako78 Nov12;
References IG.Num.SampledDataSet.AddElement(), IG.Lib.SortedUniqueItemList< Type >.Contains(), IG.Num.SampledDataSet.GetInputParameters(), IG.Num.SampledDataSet.GetOutputValues(), IG.Num.SampledDataSet.InputLength, IG.Num.SampledDataSet.Length, IG.Num.SampledDataSet.OutputLength, IG.Num.INeuralApproximator.TrainingData, and IG.Num.INeuralApproximator.VerificationIndices.
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inline |
Perform sensitiviy test from training and verification data.
$A Tako78 Jul13;
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inline |
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inline |
Plots the ANN approcimation on the center point from verification point and/or real point.
$A Tako78 Nov12;
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inline |
Plots the ANN approcimation on the center point from verification point and/or real point.
$A Tako78 Nov12;
References IG.Lib.HtmlWriter.AddHeading1(), IG.Lib.HtmlWriter.AddHeading2(), IG.Lib.HtmlWriter.AddImage(), IG.Lib.HtmlWriter.AddNewLine(), IG.Lib.HtmlWriter.AddParagraph(), IG.Lib.HtmlWriter.CloseWriter(), IG.Lib.HtmlWriter.EndDocument(), and IG.Lib.UtilSystem.OpenFileInDefaultBrowser().
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inline |
References IG.Num.VectorBase.Copy().
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inline |
Plots the ANN approcimation on the verification point and real point.
selectedTrainingPoint | Training point. |
selectedVerificationPoint | Verification point. |
whichOutput | Which output to plot. |
whichParameter | Which parameter to plot. |
min | Minimum range. |
max | Maximum range. |
$A Tako78 Nov12;
References IG.Gr.PlotZedgraphCurve.AddPoint(), IG.Num.VectorBase.Copy(), IG.Gr.PlotterZedGraph.CreateDefault(), IG.Gr.ZedGraphWindow.GraphControl, IG.Gr.PlotterZedGraph.GraphPane, IG.Gr.PlotZedGraphBase.LegendString, IG.Gr.PlotterZedGraph.Title, IG.Gr.PlotterZedGraph.Update(), IG.Gr.PlotterZedGraph.XAxis, IG.Gr.PlotterZedGraph.XAxisLabel, IG.Gr.PlotterZedGraph.YAxisLabel, and IG.Gr.PlotterZedGraph.YAxisLabelFont.
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inline |
Calculate the average center point for each parameter from complete data set.
TrainingData | Verification or training data set. |
inputCenterParameters | Centered input parameters. |
$A Tako78 Nov12;
References IG.Num.SampledDataSet.AddElement(), IG.Lib.SortedUniqueItemList< Type >.Contains(), IG.Num.SampledDataSet.GetInputParameters(), IG.Num.SampledDataSet.GetOutputValues(), IG.Num.SampledDataSet.InputLength, IG.Num.SampledDataSet.Length, IG.Num.SampledDataSet.OutputLength, IG.Num.INeuralApproximator.TrainingData, and IG.Num.INeuralApproximator.VerificationIndices.
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inline |
Calculate the average center point for each parameter from complete data set.
DataSet | Verification or training data set. |
inputCenterParameters | Centered input parameters. |
$A Tako78 Nov12;
References IG.Num.SampledDataSet.GetInputParameters(), IG.Num.SampledDataSet.InputLength, and IG.Num.SampledDataSet.Length.
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inlinevirtual |
Reimplemented in IG.Script.S_12_01_Paper_NeuralOptCasting_00, and IG.Script.S_12_02_paper_neural_process_chain_model.
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inlinevirtual |
Performs parallel training of neural networks with different parameters and default data.
This conains only example of how parallel training is performed. You can override and modify this code in derived classes in order to use the method for specific trainings with hard-coded parameters.
arguments | Arguments that specify behavior of this method. |
References IG.Num.NeuralTrainingParameters.LoadJson(), IG.Num.NeuralTrainingLimits.LoadJson(), IG.Num.NeuralTrainingTable.LoadJson(), IG.Num.NeuralTrainingTable.SaveJson(), IG.Num.NeuralTrainingTable.TrainingLimits, and IG.Num.NeuralTrainingTable.TrainingParameters.
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inlinevirtual |
Plot different convergences from traininglimits file
arguments | Command arguments. |
$A Tako78 Sep12;
References IG.Neural.NeuralTrainingResultsPlotter1.DataDefinition, IG.Num.NeuralTrainingTable.LoadJson(), IG.Num.NeuralTrainingParameters.ComparerBase.NumLastErrors, IG.Neural.NeuralTrainingResultsPlotter1.OptimizationDirectory, IG.Num.NeuralTrainingParameters.SaveCSV(), and IG.Neural.NeuralTrainingResultsPlotter1.SaveGraphs.
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inlinevirtual |
Plot graph sorted on training time.
arguments | Command arguments. |
$A Tako78 Sep12;
References IG.Neural.NeuralTrainingResultsPlotter.DataDefinition, IG.Neural.NeuralTrainingResultsPlotter1.DataDefinition, IG.Num.NeuralTrainingTable.LoadJson(), and IG.Neural.NeuralTrainingResultsPlotter1.PlotTrainingTime().
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inlinevirtual |
Plot the approximated response of the neural network in the verification points.
arguments | Command arguments, first one (index 0) is always command name. |
TaKo78, feb 2012;
Reimplemented in IG.Script.S_12_02_paper_neural_process_chain_model.
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inlinevirtual |
Plot the approximated response of the neural network in the training points.
arguments | Command arguments, first one (index 0) is always command name. |
Reimplemented in IG.Script.S_12_02_paper_neural_process_chain_model.
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inlinevirtual |
Plot the approximated responses of the neural network on two selected points and on the points between them.
arguments | Command arguments, first one (index 0) is always command name. |
Reimplemented in IG.Script.S_12_02_paper_neural_process_chain_model.
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inline |
Pots parametric study on center point from training and/or verification sets.
arguments | Command arguments, first one (index 0) is always command name. |
$A Tako78 Nov12;
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inline |
Execute sensitivity test and saves to CSV file.
arguments | Command arguments, first one (index 0) is always command name. |
$A Tako78 Jul13;
const string IG.Script.LoadableScriptShellNeuralIT.ConstTrainNetwork = "TrainNetwork" |
Comamnd name for training the network.
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpTrainNetwork = "Trains NN on basis of standard data. Args: <numLayers> <numNeurons>" |
const string IG.Script.LoadableScriptShellNeuralIT.ConstTrainParallelExample = "TrainParallel" |
Command name for parallel training of neural network.
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpTrainParallelExample = "Performs multiple trainings of neural networks at different parameters in parallel." |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotConvergence = "plotconvergence" |
Comamnd name for plotting convergence errors.
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpPlotConvergence = "Plots convergence errors of parallel trainings. Args: " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotTrainingTime = "plottrainingtime" |
Comamnd name for plotting training time.
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpPlotTrainingTime = "Plots training times of parallel trainings. Args: " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotVerificationResponse = "PlotVerificationResponse" |
Comamnd name for testing network response for verification points(plots, etc.).
Referenced by IG.Test.ProgramTestIgorIT.TestMain().
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpVerificationResponse = "Plots verification response. " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotTrainingResponse = "PlotTrainingResponse" |
Comamnd name for testing network response for verification points(plots, etc.).
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpTrainingResponse = "Plots training response. " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotVerificationAndTrainingPoints = "plotverificationandtrainingpoints" |
Comamnd name for testing network response for verification points on line for all Inputs/outputs.
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpPlotVerificationAndTrainingPoints = "Plots network response for verification and training points. " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotNetworkResponseOnLineAll = "PlotNetworkResponseOnLineAll" |
Comamnd name for testing network response for verification points on line for all Inputs/outputs.
Referenced by IG.Test.ProgramTestIgorIT.TestMain().
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpNetworkResponseOnLineAll = "Plots network response for points on line from p1 to p2. " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstPlotNetworkCenteredPoint = "PlotNetworkResponseCenteredPoint" |
Comamnd name for testing network response for centered point from verification or training points for all Inputs/outputs.
Referenced by IG.Test.ProgramTestIgorIT.TestMain().
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpPlotNetworkCenteredPoint = "Plots network response for centered point from verification or training set. " |
const string IG.Script.LoadableScriptShellNeuralIT.ConstSensitivity = "Sensitivity" |
Comamnd name for testing network response for centered point from verification or training points for all Inputs/outputs.
Referenced by IG.Test.ProgramTestIgorIT.TestMain().
const string IG.Script.LoadableScriptShellNeuralIT.ConstHelpSensitivity = "Executes sensitivity test. " |
int IG.Script.LoadableScriptShellNeuralIT.ParallelNumServersDefault = 4 |
int IG.Script.LoadableScriptShellNeuralIT.ParallelMaxEnqueuedDefault = 10 |
double IG.Script.LoadableScriptShellNeuralIT.LearningRateFrom = 0.05 |
Learning rate from number.
double IG.Script.LoadableScriptShellNeuralIT.LearningRateTo = 0.8 |
Learning rate to number.
int IG.Script.LoadableScriptShellNeuralIT.NumLearningRate = 4 |
Number of learning rates in selected region.
double IG.Script.LoadableScriptShellNeuralIT.MomentumFrom = 0.1 |
Momentum from number.
double IG.Script.LoadableScriptShellNeuralIT.MomentumTo = 0.6 |
Momentum to number.
int IG.Script.LoadableScriptShellNeuralIT.NumMomentum = 4 |
Number of momentum in selected region.
double IG.Script.LoadableScriptShellNeuralIT.AlphaFrom = 1.2 |
Alpha value from number.
double IG.Script.LoadableScriptShellNeuralIT.AlphaTo = 1.8 |
Alpha value to number.
int IG.Script.LoadableScriptShellNeuralIT.NumAlpha = 4 |
Number of alpha values in selected region.
int IG.Script.LoadableScriptShellNeuralIT.MaxEpochs = 1000 |
Max number of epochs.
int IG.Script.LoadableScriptShellNeuralIT.EpochBundle = 100 |
Number of epochs in bundle.
double IG.Script.LoadableScriptShellNeuralIT.ToleranceRMS = 0.01 |
Maximum tolerance for RMS.
int IG.Script.LoadableScriptShellNeuralIT.NumHiddenLayers = 1 |
Number of hidden layer in ANN.
int [] IG.Script.LoadableScriptShellNeuralIT.NumHiddenNeurons = new int[] { 10 } |
Number of nwueons in each hidden layer.
double IG.Script.LoadableScriptShellNeuralIT.InputBoundSafetyFactor = 1.3 |
Safety factor for input bound.
double IG.Script.LoadableScriptShellNeuralIT.OutputBoundSafetyFactor = 1.3 |
Safety factor for output bound.
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private |
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private |
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private |
bool IG.Script.LoadableScriptShellNeuralIT.allReadFromFile = false |
int IG.Script.LoadableScriptShellNeuralIT.ANNType = 1 |
int IG.Script.LoadableScriptShellNeuralIT.NumNeuronsHidden1 = 40 |
int IG.Script.LoadableScriptShellNeuralIT.NumNeuronsHidden2 = 0 |
double IG.Script.LoadableScriptShellNeuralIT.LearnignRate = 0.3 |
double IG.Script.LoadableScriptShellNeuralIT.Momentum = 0.6 |
double IG.Script.LoadableScriptShellNeuralIT.AlphaValue = 1.0 |
double IG.Script.LoadableScriptShellNeuralIT.RatioVerificationPoints = 0.05 |
bool IG.Script.LoadableScriptShellNeuralIT.showErrorPercentages = false |
bool IG.Script.LoadableScriptShellNeuralIT.verificationPointsFromFile = false |
bool IG.Script.LoadableScriptShellNeuralIT.enableLabels = true |
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protected |
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protected |
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protected |
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protected |
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getprotected |
List of training parameters for which training should be performed.
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get |
Path to the default location lwhere training parameters and convergence results from multiple training attempts are stored.
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get |
Path to the default location lwhere training limits are stored.
$A Tako78 Aug12;
|
get |
Path to the default location lwhere training complete results are stored.
$A Tako78 Aug12;
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get |
Path to the default location lwhere training complete results are stored.
$A Tako78 Aug12;
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get |
Path to the default location where optimal training parameters for the current problem are stored.
Optimal in this context meand "best known".
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getset |
Eventual relative path of an additional file name where results are stored.
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getset |
Number of parallel servers that will be used for parallel execution of jobs.
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getset |
Maximal number of enqueued jobs when performing parallel execution of jobs.
This is not enforced by all methods of this class that perform perallel execution.
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getset |
Sleeping time in milliseconds.
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getset |
Whether servers will operate in server mode (true) or by running each job in a new thread (false).
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getset |
Client's output level when executing parallel jobs.
This specifies how much information is output to console from the client code that sends jobs for parallel execution.
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getset |
Output level for parallel dispatcher, servers and job containers when executing parallel jobs.
This specifies how much information is output to console from the when parallel jobs are being handled by the dispatcher and servers.
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getset |
Specifies whether parallel jobs will be executing in testing mode (where e.g. delay times apply).
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getset |
Delay in seconds used in parallel execution. This is useful only for testing purposes, default is 0.
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getset |
Relative variation in delay time used in parallel execution. This is useful only for testing purposes, default is 0.
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getset |
Frequency of saving the results of parallel execution:
less than 0: results are not saved.
0: results are saved only at the end, when all results are calculated.
1: Results are saved every time a new result is calculated.
N > 0: Resalts are saved every time after N new results are calculated