Modifier and Type | Method and Description |
---|---|
MLDataSet |
Cmd.obtainTrainingSet()
Obtain the training set.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
AnalystUtility.loadCSV(File file)
Load a CSV file into an MLDataSet.
|
MLDataSet |
AnalystUtility.loadCSV(String filename)
Load a CSV file into an MLDataSet.
|
Modifier and Type | Field and Description |
---|---|
protected MLDataSet |
Ensemble.aggregatorDataSet |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
Ensemble.getTrainingSet(int setNumber)
Extract a specific training set from the Ensemble
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
EnsembleTrainFactory.getTraining(MLMethod method,
MLDataSet trainingData) |
void |
Ensemble.setTrainingData(MLDataSet data)
Set which training data to base the training on
|
Modifier and Type | Class and Description |
---|---|
class |
EnsembleDataSet |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
EnsembleDataSet.openAdditional() |
Constructor and Description |
---|
EnsembleDataSet(MLDataSet mlds) |
Modifier and Type | Field and Description |
---|---|
protected MLDataSet |
EnsembleDataSetFactory.dataSource |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
EnsembleDataSetFactory.getInputData() |
Modifier and Type | Method and Description |
---|---|
void |
EnsembleDataSetFactory.setInputData(MLDataSet dataSource) |
Modifier and Type | Method and Description |
---|---|
MLTrain |
ManhattanPropagationFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
ResilientPropagationFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
ScaledConjugateGradientFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
LevenbergMarquardtFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
MLTrain |
BackpropagationFactory.getTraining(MLMethod mlMethod,
MLDataSet trainingData) |
Modifier and Type | Field and Description |
---|---|
protected MLDataSet |
BasicHessian.training
The training data that provides the ideal values.
|
Modifier and Type | Method and Description |
---|---|
void |
HessianFD.init(BasicNetwork theNetwork,
MLDataSet theTraining)
Init the class.
|
void |
HessianCR.init(BasicNetwork theNetwork,
MLDataSet theTraining)
Init the class.
|
void |
ComputeHessian.init(BasicNetwork theNetwork,
MLDataSet theTraining)
Init the class.
|
void |
BasicHessian.init(BasicNetwork theNetwork,
MLDataSet theTraining)
Init the class.
|
Constructor and Description |
---|
ChainRuleWorker(FlatNetwork theNetwork,
MLDataSet theTraining,
int theLow,
int theHigh)
Construct the chain rule worker.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
MLCluster.createDataSet()
Create a machine learning dataset from the data.
|
Modifier and Type | Method and Description |
---|---|
double |
MLError.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
int[] |
MLStateSequence.getStatesForSequence(MLDataSet oseq)
Get the sates for the given sequence.
|
double |
MLStateSequence.probability(MLDataSet oseq)
Determine the probability of the specified sequence.
|
double |
MLStateSequence.probability(MLDataSet seq,
int[] states)
Determine the probability for the specified sequence and states.
|
Modifier and Type | Method and Description |
---|---|
double |
BayesianNetwork.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
Constructor and Description |
---|
TrainBayesian(BayesianNetwork theNetwork,
MLDataSet theData,
int theMaximumParents)
Construct a Bayesian trainer.
|
TrainBayesian(BayesianNetwork theNetwork,
MLDataSet theData,
int theMaximumParents,
BayesianInit theInit,
BayesSearch theSearch,
BayesEstimator theEstimator)
Construct a Bayesian trainer.
|
Modifier and Type | Method and Description |
---|---|
void |
SimpleEstimator.init(TrainBayesian theTrainer,
BayesianNetwork theNetwork,
MLDataSet theData)
Init the estimator.
|
void |
EstimatorNone.init(TrainBayesian theTrainer,
BayesianNetwork theNetwork,
MLDataSet theData)
Init the estimator.
|
void |
BayesEstimator.init(TrainBayesian theTrainer,
BayesianNetwork theNetwork,
MLDataSet theData)
Init the estimator.
|
Modifier and Type | Method and Description |
---|---|
void |
SearchNone.init(TrainBayesian theTrainer,
BayesianNetwork theNetwork,
MLDataSet theData)
Init the search object.
|
Modifier and Type | Method and Description |
---|---|
void |
SearchK2.init(TrainBayesian theTrainer,
BayesianNetwork theNetwork,
MLDataSet theData)
Init the search object.
|
void |
BayesSearch.init(TrainBayesian theTrainer,
BayesianNetwork theNetwork,
MLDataSet theData)
Init the search object.
|
Modifier and Type | Interface and Description |
---|---|
interface |
MLSequenceSet
A sequence set is a collection of data sets.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
MLSequenceSet.getSequence(int i)
Get an individual sequence.
|
MLDataSet |
MLDataSet.openAdditional()
Opens an additional instance of this dataset.
|
Modifier and Type | Method and Description |
---|---|
Collection<MLDataSet> |
MLSequenceSet.getSequences() |
Modifier and Type | Method and Description |
---|---|
void |
MLSequenceSet.add(MLDataSet sequence)
Add a new sequence.
|
Modifier and Type | Class and Description |
---|---|
class |
AutoFloatDataSet |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
AutoFloatDataSet.openAdditional() |
Modifier and Type | Class and Description |
---|---|
class |
BasicMLDataSet
Stores data in an ArrayList.
|
class |
BasicMLSequenceSet
A basic implementation of the MLSequenceSet.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
BasicMLSequenceSet.getSequence(int i) |
MLDataSet |
BasicMLSequenceSet.openAdditional()
Opens an additional instance of this dataset.
|
MLDataSet |
BasicMLDataSet.openAdditional()
Opens an additional instance of this dataset.
|
Modifier and Type | Method and Description |
---|---|
Collection<MLDataSet> |
BasicMLSequenceSet.getSequences() |
Modifier and Type | Method and Description |
---|---|
void |
BasicMLSequenceSet.add(MLDataSet sequence) |
static List<MLDataPair> |
BasicMLDataSet.toList(MLDataSet theSet)
Concert the data set to a list.
|
Constructor and Description |
---|
BasicMLDataSet(MLDataSet set)
Copy whatever dataset type is specified into a memory dataset.
|
BasicMLSequenceSet(MLDataSet set)
Copy whatever dataset type is specified into a memory dataset.
|
Modifier and Type | Class and Description |
---|---|
class |
BufferedMLDataSet
This class is not memory based, so very long files can be used, without
running out of memory.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
MemoryDataLoader.external2Memory()
Convert an external file format, such as CSV, to an Encog memory training
set.
|
MLDataSet |
BufferedMLDataSet.loadToMemory()
Load the binary dataset to memory.
|
Modifier and Type | Method and Description |
---|---|
void |
BufferedMLDataSet.load(MLDataSet training)
Load the specified training set.
|
Constructor and Description |
---|
NeuralDataSetCODEC(MLDataSet theDataset)
Construct a CODEC.
|
Modifier and Type | Class and Description |
---|---|
class |
FoldedDataSet
A folded data set allows you to "fold" the data into several equal(or nearly
equal) datasets.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
FoldedDataSet.getUnderlying() |
MLDataSet |
FoldedDataSet.openAdditional()
Opens an additional instance of this dataset.
|
Constructor and Description |
---|
FoldedDataSet(MLDataSet theUnderlying)
Create a folded dataset.
|
Modifier and Type | Class and Description |
---|---|
class |
MarketMLDataSet
A data set that is designed to hold market data.
|
Modifier and Type | Class and Description |
---|---|
class |
CSVNeuralDataSet
An implementation of the NeuralDataSet interface designed to provide a CSV
file to the neural network.
|
Modifier and Type | Class and Description |
---|---|
class |
TemporalMLDataSet
This class implements a temporal neural data set.
|
Modifier and Type | Class and Description |
---|---|
class |
MatrixMLDataSet
The MatrixMLDataSet can use a large 2D matrix of doubles to internally hold
data.
|
class |
VersatileMLDataSet
The versatile dataset supports several advanced features.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
MatrixMLDataSet.openAdditional()
Opens an additional instance of this dataset.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
TrainEA.getTraining()
Returns null, does not use a training set, rather uses a score function.
|
Constructor and Description |
---|
TrainEA(Population thePopulation,
MLDataSet trainingData)
Create a trainer for training data.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
MLTrainFactory.create(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
GeneticFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an annealing trainer.
|
MLTrain |
SVMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a SVM trainer.
|
MLTrain |
ClusterSOMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a cluster SOM trainer.
|
MLTrain |
LMAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a LMA trainer.
|
MLTrain |
NEATGAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an NEAT GA trainer.
|
MLTrain |
PSOFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a PSO trainer.
|
MLTrain |
QuickPropFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a quick propagation trainer.
|
MLTrain |
TrainBayesianFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a K2 trainer.
|
MLTrain |
RBFSVDFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a RBF-SVD trainer.
|
MLTrain |
AnnealFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an annealing trainer.
|
MLTrain |
SCGFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a SCG trainer.
|
MLTrain |
NeighborhoodSOMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a LMA trainer.
|
MLTrain |
BackPropFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a backpropagation trainer.
|
MLTrain |
PNNTrainFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a PNN trainer.
|
MLTrain |
NelderMeadFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a Nelder Mead trainer.
|
MLTrain |
ManhattanFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a Manhattan trainer.
|
MLTrain |
SVMSearchFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a SVM trainer.
|
MLTrain |
EPLGAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an EPL GA trainer.
|
MLTrain |
RPROPFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a RPROP trainer.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
TrainGaussian.getTraining() |
Constructor and Description |
---|
TrainGaussian(GaussianFitting theMethod,
MLDataSet theTraining) |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
TrainLinearRegression.getTraining() |
Modifier and Type | Method and Description |
---|---|
double |
LinearRegression.calculateError(MLDataSet data) |
Constructor and Description |
---|
TrainLinearRegression(LinearRegression theMethod,
MLDataSet theTraining) |
Modifier and Type | Method and Description |
---|---|
int[] |
HiddenMarkovModel.getStatesForSequence(MLDataSet seq) |
double |
HiddenMarkovModel.lnProbability(MLDataSet seq) |
double |
HiddenMarkovModel.probability(MLDataSet seq) |
double |
HiddenMarkovModel.probability(MLDataSet seq,
int[] states) |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
MarkovGenerator.observationSequence(int length) |
Modifier and Type | Method and Description |
---|---|
protected void |
ForwardBackwardScaledCalculator.computeAlpha(HiddenMarkovModel hmm,
MLDataSet oseq) |
protected void |
ForwardBackwardCalculator.computeAlpha(HiddenMarkovModel hmm,
MLDataSet oseq)
Compute alpha.
|
protected void |
ForwardBackwardScaledCalculator.computeBeta(HiddenMarkovModel hmm,
MLDataSet oseq) |
protected void |
ForwardBackwardCalculator.computeBeta(HiddenMarkovModel hmm,
MLDataSet oseq)
Compute the beta step.
|
Constructor and Description |
---|
ForwardBackwardCalculator(MLDataSet oseq,
HiddenMarkovModel hmm)
Construct the forward/backward calculator.
|
ForwardBackwardCalculator(MLDataSet oseq,
HiddenMarkovModel hmm,
EnumSet<ForwardBackwardCalculator.Computation> flags)
Construct the object.
|
ForwardBackwardScaledCalculator(MLDataSet oseq,
HiddenMarkovModel hmm) |
ForwardBackwardScaledCalculator(MLDataSet oseq,
HiddenMarkovModel hmm,
EnumSet<ForwardBackwardCalculator.Computation> flags) |
ViterbiCalculator(MLDataSet oseq,
HiddenMarkovModel hmm) |
Modifier and Type | Method and Description |
---|---|
void |
DiscreteDistribution.fit(MLDataSet co)
Fit this distribution to the specified data.
|
void |
StateDistribution.fit(MLDataSet set)
Fit this distribution to the specified data set.
|
void |
ContinousDistribution.fit(MLDataSet co)
Fit this distribution to the specified data set.
|
void |
DiscreteDistribution.fit(MLDataSet co,
double[] weights)
Fit this distribution to the specified data, with weights.
|
void |
StateDistribution.fit(MLDataSet set,
double[] weights)
Fit this distribution to the specified data set, given the specified
weights, per element.
|
void |
ContinousDistribution.fit(MLDataSet co,
double[] weights)
Fit this distribution to the specified data set, given the specified
weights, per element.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
BaseBaumWelch.getTraining() |
Modifier and Type | Method and Description |
---|---|
abstract double[][][] |
BaseBaumWelch.estimateXi(MLDataSet sequence,
ForwardBackwardCalculator fbc,
HiddenMarkovModel hmm) |
double[][][] |
TrainBaumWelchScaled.estimateXi(MLDataSet sequence,
ForwardBackwardCalculator fbc,
HiddenMarkovModel hmm) |
double[][][] |
TrainBaumWelch.estimateXi(MLDataSet sequence,
ForwardBackwardCalculator fbc,
HiddenMarkovModel hmm) |
abstract ForwardBackwardCalculator |
BaseBaumWelch.generateForwardBackwardCalculator(MLDataSet sequence,
HiddenMarkovModel hmm) |
ForwardBackwardCalculator |
TrainBaumWelchScaled.generateForwardBackwardCalculator(MLDataSet sequence,
HiddenMarkovModel hmm) |
ForwardBackwardCalculator |
TrainBaumWelch.generateForwardBackwardCalculator(MLDataSet sequence,
HiddenMarkovModel hmm) |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
TrainKMeans.getTraining() |
Constructor and Description |
---|
Clusters(int k,
MLDataSet observations) |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
BasicCluster.createDataSet()
Create a dataset from the clustered data.
|
Constructor and Description |
---|
KMeansClustering(int theK,
MLDataSet theSet)
Construct the K-Means object.
|
Modifier and Type | Method and Description |
---|---|
double |
EncogModel.calculateError(MLMethod method,
MLDataSet data)
Calculate the error for the given method and dataset.
|
Modifier and Type | Method and Description |
---|---|
double |
EncogProgram.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
Modifier and Type | Method and Description |
---|---|
double |
SVM.calculateError(MLDataSet data)
Calculate the error for this SVM.
|
Modifier and Type | Method and Description |
---|---|
static svm_problem |
EncodeSVMProblem.encode(MLDataSet training,
int outputIndex)
Encode the Encog dataset.
|
Constructor and Description |
---|
SVMSearchTrain(SVM method,
MLDataSet training)
Construct a trainer for an SVM network.
|
SVMTrain(SVM method,
MLDataSet dataSet)
Construct a trainer for an SVM network.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
BasicTraining.getTraining() |
MLDataSet |
MLTrain.getTraining() |
Modifier and Type | Method and Description |
---|---|
void |
BasicTraining.setTraining(MLDataSet training)
Set the training object that this strategy is working with.
|
Constructor and Description |
---|
EarlyStoppingStrategy(MLDataSet theValidationSet,
MLDataSet theTestSet)
Construct the early stopping strategy.
|
EarlyStoppingStrategy(MLDataSet theValidationSet,
MLDataSet theTestSet,
int theStripLength,
double theAlpha,
double theMinEfficiency)
Construct the early stopping strategy.
|
SimpleEarlyStoppingStrategy(MLDataSet theValidationSet) |
SimpleEarlyStoppingStrategy(MLDataSet theValidationSet,
int theCheckFrequency) |
Modifier and Type | Method and Description |
---|---|
double |
CPN.calculateError(MLDataSet data)
Calculate the error for this neural network.
|
Constructor and Description |
---|
TrainInstar(CPN theNetwork,
MLDataSet theTraining,
double theLearningRate,
boolean theInitWeights)
Construct the instar training object.
|
TrainOutstar(CPN theNetwork,
MLDataSet theTraining,
double theLearningRate)
Construct the outstar trainer.
|
Modifier and Type | Interface and Description |
---|---|
interface |
NeuralDataSet
This is an alias class for Encog 2.5 compatibility.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicNeuralDataSet
This is an alias class for Encog 2.5 compatibility.
|
Modifier and Type | Method and Description |
---|---|
double |
FlatNetwork.calculateError(MLDataSet data)
Calculate the error for this neural network.
|
Modifier and Type | Method and Description |
---|---|
double |
FreeformNetwork.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
FreeformPropagationTraining.getTraining() |
Constructor and Description |
---|
FreeformBackPropagation(FreeformNetwork theNetwork,
MLDataSet theTraining,
double theLearningRate,
double theMomentum)
Construct a back propagation trainer.
|
FreeformPropagationTraining(FreeformNetwork theNetwork,
MLDataSet theTraining)
Construct the trainer.
|
FreeformResilientPropagation(FreeformNetwork theNetwork,
MLDataSet theTraining)
Construct the RPROP trainer, Use default intiial update and max step.
|
FreeformResilientPropagation(FreeformNetwork theNetwork,
MLDataSet theTraining,
double initialUpdate,
double theMaxStep)
Construct the RPROP trainer.
|
Modifier and Type | Method and Description |
---|---|
double |
NEATPopulation.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
double |
NEATNetwork.calculateError(MLDataSet data)
Calculate the error for this neural network.
|
Modifier and Type | Method and Description |
---|---|
double |
BasicNetwork.calculateError(MLDataSet data)
Calculate the error for this neural network.
|
Constructor and Description |
---|
TrainingSetScore(MLDataSet training)
Construct a training set score calculation.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
TrainingJob.getTraining() |
Modifier and Type | Method and Description |
---|---|
void |
TrainingJob.setTraining(MLDataSet training) |
Constructor and Description |
---|
BPROPJob(BasicNetwork network,
MLDataSet training,
boolean loadToMemory,
double learningRate,
double momentum)
Construct a job definition for RPROP.
|
RPROPJob(BasicNetwork network,
MLDataSet training,
boolean loadToMemory)
Construct an RPROP job.
|
TrainingJob(BasicNetwork network,
MLDataSet training,
boolean loadToMemory)
Construct a training job.
|
Constructor and Description |
---|
LevenbergMarquardtTraining(BasicNetwork network,
MLDataSet training)
Construct the LMA object.
|
LevenbergMarquardtTraining(BasicNetwork network,
MLDataSet training,
ComputeHessian h)
Construct the LMA object.
|
Constructor and Description |
---|
NelderMeadTraining(BasicNetwork network,
MLDataSet training)
Construct a Nelder Mead trainer with a step size of 100.
|
NelderMeadTraining(BasicNetwork network,
MLDataSet training,
double stepValue)
Construct a Nelder Mead trainer with a definable step.
|
Modifier and Type | Method and Description |
---|---|
double |
TrainBasicPNN.calculateError(MLDataSet training,
boolean deriv)
Calculate the error for the entire training set.
|
Constructor and Description |
---|
TrainBasicPNN(BasicPNN network,
MLDataSet training)
Train a BasicPNN.
|
Constructor and Description |
---|
GradientWorker(FlatNetwork theNetwork,
Propagation theOwner,
MLDataSet theTraining,
int theLow,
int theHigh,
double[] flatSpot,
ErrorFunction ef)
Construct a gradient worker.
|
Propagation(ContainsFlat network,
MLDataSet training)
Construct a propagation object.
|
Constructor and Description |
---|
Backpropagation(ContainsFlat network,
MLDataSet training)
Create a class to train using backpropagation.
|
Backpropagation(ContainsFlat network,
MLDataSet training,
double theLearnRate,
double theMomentum) |
Constructor and Description |
---|
ManhattanPropagation(ContainsFlat network,
MLDataSet training,
double theLearnRate)
Construct a Manhattan propagation training object.
|
Constructor and Description |
---|
QuickPropagation(ContainsFlat network,
MLDataSet training)
Construct a QPROP trainer for flat networks.
|
QuickPropagation(ContainsFlat network,
MLDataSet training,
double theLearningRate)
Construct a QPROP trainer for flat networks.
|
Constructor and Description |
---|
ResilientPropagation(ContainsFlat network,
MLDataSet training)
Construct an RPROP trainer, allows an OpenCL device to be specified.
|
ResilientPropagation(ContainsFlat network,
MLDataSet training,
double initialUpdate,
double maxStep)
Construct a resilient training object, allow the training parameters to
be specified.
|
Constructor and Description |
---|
ScaledConjugateGradient(ContainsFlat network,
MLDataSet training)
Construct a training class.
|
Constructor and Description |
---|
NeuralPSO(BasicNetwork network,
MLDataSet trainingSet)
Construct a PSO using a training set score function, 20 particles and the
NguyenWidrowRandomizer randomizer.
|
Constructor and Description |
---|
TrainAdaline(BasicNetwork network,
MLDataSet training,
double learningRate)
Construct an ADALINE trainer.
|
Modifier and Type | Method and Description |
---|---|
double |
BasicPNN.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
PruneIncremental.getTraining() |
Constructor and Description |
---|
PruneIncremental(MLDataSet training,
NeuralNetworkPattern pattern,
int iterations,
int weightTries,
int numTopResults,
StatusReportable report)
Construct an object to determine the optimal number of hidden layers and
neurons for the specified training data and pattern.
|
Modifier and Type | Method and Description |
---|---|
double |
RBFNetwork.calculateError(MLDataSet data)
Calculate the error for this neural network.
|
Constructor and Description |
---|
SVDTraining(RBFNetwork network,
MLDataSet training)
Construct the training object.
|
Modifier and Type | Method and Description |
---|---|
double |
SOM.calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
|
Constructor and Description |
---|
BasicTrainSOM(SOM network,
double learningRate,
MLDataSet training,
NeighborhoodFunction neighborhood)
Create an instance of competitive training.
|
Constructor and Description |
---|
SOMClusterCopyTraining(SOM network,
MLDataSet training)
Construct the object.
|
Modifier and Type | Method and Description |
---|---|
static void |
TrainingDialog.trainDialog(BasicNetwork network,
MLDataSet trainingSet)
Train using SCG and display progress to a dialog box.
|
static void |
TrainingDialog.trainDialog(MLTrain train,
BasicNetwork network,
MLDataSet trainingSet)
Train, using the specified training method, display progress to a dialog
box.
|
Modifier and Type | Class and Description |
---|---|
class |
SQLNeuralDataSet
A dataset based on a SQL query.
|
Modifier and Type | Class and Description |
---|---|
class |
ImageMLDataSet
Store a collection of images for training with a neural network.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
EncogPluginService1.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
Modifier and Type | Method and Description |
---|---|
MLTrain |
SystemActivationPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
MLTrain |
SystemTrainingPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args) |
MLTrain |
SystemMethodsPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer.
|
Modifier and Type | Method and Description |
---|---|
static void |
EncogValidate.validateNetworkForTraining(ContainsFlat network,
MLDataSet training)
Validate a network for training.
|
Modifier and Type | Method and Description |
---|---|
MLDataSet |
TemporalWindowArray.process(double[] data)
Process the array.
|
MLDataSet |
TemporalWindowArray.process(double[][] data)
Processes the specified data array in an IMLDataset.
|
Modifier and Type | Method and Description |
---|---|
static MLDataSet |
EncoderTrainingFactory.generateTraining(int inputCount,
boolean compl)
Generate an encoder training set over the range [0.0,1.0].
|
static MLDataSet |
EncoderTrainingFactory.generateTraining(int inputCount,
boolean compl,
double min,
double max)
Generate an encoder over the specified range.
|
static MLDataSet |
EncoderTrainingFactory.generateTraining(int inputCount,
boolean compl,
double inputMin,
double inputMax,
double outputMin,
double outputMax) |
Modifier and Type | Method and Description |
---|---|
static int |
Evaluate.evaluateTrain(BasicNetwork network,
MLDataSet training)
Evaluate how long it takes to calculate the error for the network.
|
static void |
RandomTrainingFactory.generate(MLDataSet training,
long seed,
int count,
double min,
double max)
Generate random training into a training set.
|
Modifier and Type | Method and Description |
---|---|
static MLDataSet |
GenerationUtil.generateSingleDataRange(EncogFunction task,
double start,
double stop,
double step) |
Modifier and Type | Method and Description |
---|---|
static double |
CalculateRegressionError.calculateError(MLRegression method,
MLDataSet data) |
Modifier and Type | Method and Description |
---|---|
MLDataSet |
NormalizationStorageNeuralDataSet.getDataset() |
Constructor and Description |
---|
NormalizationStorageNeuralDataSet(MLDataSet dataset)
Construct a normalized neural storage class to hold data.
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Modifier and Type | Method and Description |
---|---|
static MLDataSet |
EncogUtility.loadCSV2Memory(String filename,
int input,
int ideal,
boolean headers,
CSVFormat format,
boolean significance)
Load CSV to memory.
|
static MLDataSet |
TrainingSetUtil.loadCSVTOMemory(CSVFormat format,
String filename,
boolean headers,
int inputSize,
int idealSize)
Load a CSV file into a memory dataset.
|
static MLDataSet |
EncogUtility.loadEGB2Memory(File filename) |
Modifier and Type | Method and Description |
---|---|
static double |
EncogUtility.calculateClassificationError(MLClassification method,
MLDataSet data)
Calculate the classification error.
|
static double |
EncogUtility.calculateRegressionError(MLRegression method,
MLDataSet data) |
static void |
EncogUtility.evaluate(MLRegression network,
MLDataSet training)
Evaluate the network and display (to the console) the output for every
value in the training set.
|
static void |
EncogUtility.saveCSV(File targetFile,
CSVFormat format,
MLDataSet set) |
static void |
EncogUtility.saveEGB(File f,
MLDataSet data)
Save a training set to an EGB file.
|
static void |
EncogUtility.trainConsole(BasicNetwork network,
MLDataSet trainingSet,
int minutes)
Train the neural network, using SCG training, and output status to the
console.
|
static void |
EncogUtility.trainConsole(MLTrain train,
BasicNetwork network,
MLDataSet trainingSet,
int minutes)
Train the network, using the specified training algorithm, and send the
output to the console.
|
static ObjectPair<double[][],double[][]> |
TrainingSetUtil.trainingToArray(MLDataSet training) |
static void |
EncogUtility.trainToError(MLMethod method,
MLDataSet dataSet,
double error)
Train the method, to a specific error, send the output to the console.
|
Modifier and Type | Method and Description |
---|---|
static void |
ValidateNetwork.validateMethodToData(MLMethod method,
MLDataSet training) |
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