Modifier and Type | Method and Description |
---|---|
void |
AnalystUtility.decode(boolean includeInput,
boolean includeOutput,
double[] rawData,
MLData encodedData)
Decode fields, using the analyst.
|
void |
AnalystUtility.encode(boolean includeInput,
boolean includeOutput,
double[] rawData,
MLData encodedData)
Encode fields, using the analyst.
|
Modifier and Type | Method and Description |
---|---|
MLData |
Ensemble.compute(MLData input)
Compute the output for a specific input
|
MLData |
GenericEnsembleML.compute(MLData input) |
MLData |
EnsembleAggregator.evaluate(ArrayList<MLData> outputs) |
Modifier and Type | Method and Description |
---|---|
int |
GenericEnsembleML.classify(MLData input) |
MLData |
Ensemble.compute(MLData input)
Compute the output for a specific input
|
MLData |
GenericEnsembleML.compute(MLData input) |
int |
GenericEnsembleML.winner(MLData output) |
Modifier and Type | Method and Description |
---|---|
MLData |
EnsembleAggregator.evaluate(ArrayList<MLData> outputs) |
Modifier and Type | Method and Description |
---|---|
MLData |
MetaClassifier.evaluate(ArrayList<MLData> outputs) |
MLData |
MajorityVoting.evaluate(ArrayList<MLData> outputs) |
MLData |
Averaging.evaluate(ArrayList<MLData> outputs) |
MLData |
MajorityVoting.evaluate(ArrayList<MLData> outputs,
double threshold,
double lowValue,
double highValue) |
Modifier and Type | Method and Description |
---|---|
MLData |
MetaClassifier.evaluate(ArrayList<MLData> outputs) |
MLData |
MajorityVoting.evaluate(ArrayList<MLData> outputs) |
MLData |
Averaging.evaluate(ArrayList<MLData> outputs) |
MLData |
MajorityVoting.evaluate(ArrayList<MLData> outputs,
double threshold,
double lowValue,
double highValue) |
Modifier and Type | Method and Description |
---|---|
void |
EnsembleDataSet.add(MLData data1) |
void |
EnsembleDataSet.add(MLData inputData,
MLData idealData) |
Modifier and Type | Method and Description |
---|---|
MLData |
MLRegression.compute(MLData input)
Compute regression.
|
MLData |
MLCluster.get(int pos)
Get the specified data item by index.
|
Modifier and Type | Method and Description |
---|---|
List<MLData> |
MLCluster.getData() |
Modifier and Type | Method and Description |
---|---|
void |
MLCluster.add(MLData pair)
Add data to this cluster.
|
int |
MLClassification.classify(MLData input)
Classify the input into a group.
|
MLData |
MLRegression.compute(MLData input)
Compute regression.
|
void |
MLCluster.remove(MLData data)
Remove the specified item.
|
Modifier and Type | Method and Description |
---|---|
int |
BayesianNetwork.classify(MLData input)
Classify the input.
|
double |
BayesianNetwork.computeProbability(MLData input) |
int[] |
BayesianNetwork.determineClasses(MLData input)
Determine the classes for the specified input.
|
Modifier and Type | Interface and Description |
---|---|
interface |
MLComplexData
This class implements a data object that can hold complex numbers.
|
Modifier and Type | Method and Description |
---|---|
MLData |
MLData.clone()
Clone this object.
|
MLData |
MLDataPair.getIdeal() |
MLData |
MLDataPair.getInput() |
Modifier and Type | Method and Description |
---|---|
void |
MLDataSet.add(MLData data1)
Add a object to the dataset.
|
void |
MLDataSet.add(MLData inputData,
MLData idealData)
Add a set of input and ideal data to the dataset.
|
Modifier and Type | Method and Description |
---|---|
void |
AutoFloatDataSet.add(MLData data1) |
void |
AutoFloatDataSet.add(MLData inputData,
MLData idealData) |
Modifier and Type | Class and Description |
---|---|
class |
BasicMLComplexData
This class implements a data object that can hold complex numbers.
|
class |
BasicMLData
Basic implementation of the MLData interface that stores the data in an
array.
|
Modifier and Type | Method and Description |
---|---|
MLData |
BasicMLData.clone()
Clone this object.
|
MLData |
BasicMLComplexData.clone()
Clone this object.
|
MLData |
BasicMLDataPair.getIdeal() |
MLData |
BasicMLDataPair.getInput() |
MLData |
BasicMLData.minus(MLData o)
Subtract one data element from another.
|
MLData |
BasicMLData.plus(MLData o)
Add one data element to another.
|
MLData |
BasicMLData.times(double d)
Multiply one data element with another.
|
Modifier and Type | Method and Description |
---|---|
Centroid<MLData> |
BasicMLData.createCentroid() |
Centroid<MLData> |
BasicMLComplexData.createCentroid()
Not supported.
|
Modifier and Type | Method and Description |
---|---|
void |
BasicMLDataCentroid.add(MLData d)
Add an element to the centroid.
|
void |
BasicMLSequenceSet.add(MLData theData)
Add a object to the dataset.
|
void |
BasicMLDataSet.add(MLData theData)
Add a object to the dataset.
|
void |
BasicMLSequenceSet.add(MLData inputData,
MLData idealData)
Add a set of input and ideal data to the dataset.
|
void |
BasicMLDataSet.add(MLData inputData,
MLData idealData)
Add a set of input and ideal data to the dataset.
|
double |
BasicMLDataCentroid.distance(MLData d)
The distance between this centroid and an element.
|
MLData |
BasicMLData.minus(MLData o)
Subtract one data element from another.
|
MLData |
BasicMLData.plus(MLData o)
Add one data element to another.
|
void |
BasicMLDataCentroid.remove(MLData d)
Remove an element from the centroid.
|
Constructor and Description |
---|
BasicMLComplexData(MLData d)
Construct a new BasicMLData object from an existing one.
|
BasicMLData(MLData d)
Construct a new BasicMLData object from an existing one.
|
BasicMLDataCentroid(MLData o)
Construct the centroid.
|
BasicMLDataPair(MLData theInput)
Construct the object with only input.
|
BasicMLDataPair(MLData theInput,
MLData theIdeal)
Construct a BasicMLDataPair class with the specified input and ideal
values.
|
Modifier and Type | Method and Description |
---|---|
void |
BufferedMLDataSet.add(MLData data1)
Add only input data, for an unsupervised dataset.
|
void |
BufferedMLDataSet.add(MLData inputData,
MLData idealData)
Add both the input and ideal data.
|
Modifier and Type | Method and Description |
---|---|
void |
FoldedDataSet.add(MLData data1)
Not supported.
|
void |
FoldedDataSet.add(MLData inputData,
MLData idealData)
Not supported.
|
Modifier and Type | Class and Description |
---|---|
class |
SparseMLData |
Modifier and Type | Method and Description |
---|---|
MLData |
SparseMLData.clone()
Clone this object.
|
Modifier and Type | Method and Description |
---|---|
Centroid<MLData> |
SparseMLData.createCentroid() |
Constructor and Description |
---|
SparseMLData(MLData d)
Construct a new BasicMLData object from an existing one.
|
Modifier and Type | Class and Description |
---|---|
class |
BiPolarNeuralData
A NeuralData implementation designed to work with bipolar data.
|
Modifier and Type | Method and Description |
---|---|
MLData |
BiPolarNeuralData.clone() |
Modifier and Type | Method and Description |
---|---|
Centroid<MLData> |
BiPolarNeuralData.createCentroid()
Not supported.
|
Modifier and Type | Method and Description |
---|---|
void |
TemporalMLDataSet.add(MLData data)
Adding directly is not supported.
|
void |
TemporalMLDataSet.add(MLData inputData,
MLData idealData)
Adding directly is not supported.
|
Modifier and Type | Method and Description |
---|---|
MLData |
NormalizationHelper.allocateInputVector()
Allocate a data item large enough to hold a single input vector.
|
MLData |
NormalizationHelper.allocateInputVector(int multiplier)
Allocate a data item large enough to hold several input vectors.
|
Modifier and Type | Method and Description |
---|---|
void |
MatrixMLDataSet.add(MLData data1)
Add a object to the dataset.
|
void |
MatrixMLDataSet.add(MLData inputData,
MLData idealData)
Add a set of input and ideal data to the dataset.
|
String[] |
NormalizationHelper.denormalizeOutputVectorToString(MLData output)
Denormalize a complete output vector to an array of strings.
|
Modifier and Type | Method and Description |
---|---|
String |
OneOfNNormalizer.denormalizeColumn(ColumnDefinition colDef,
MLData data,
int dataColumn)
Denormalize a value.
|
String |
IndexedNormalizer.denormalizeColumn(ColumnDefinition colDef,
MLData data,
int dataColumn)
Denormalize a value.
|
String |
Normalizer.denormalizeColumn(ColumnDefinition colDef,
MLData data,
int dataIndex)
Denormalize a value.
|
String |
RangeOrdinal.denormalizeColumn(ColumnDefinition colDef,
MLData data,
int dataColumn)
Denormalize a value.
|
String |
RangeNormalizer.denormalizeColumn(ColumnDefinition colDef,
MLData data,
int dataColumn)
Denormalize a value.
|
String |
PassThroughNormalizer.denormalizeColumn(ColumnDefinition colDef,
MLData data,
int dataColumn)
Denormalize a value.
|
Modifier and Type | Method and Description |
---|---|
String |
BasicNormalizationStrategy.denormalizeColumn(ColumnDefinition colDef,
boolean isInput,
MLData data,
int dataColumn)
Normalize a column, with a double input.
|
String |
NormalizationStrategy.denormalizeColumn(ColumnDefinition colDef,
boolean isInput,
MLData output,
int idx)
Normalize a column, with a double input.
|
Modifier and Type | Method and Description |
---|---|
MLData |
GaussianFitting.compute(MLData input) |
Modifier and Type | Method and Description |
---|---|
MLData |
GaussianFitting.compute(MLData input) |
Modifier and Type | Method and Description |
---|---|
MLData |
LinearRegression.compute(MLData input) |
Modifier and Type | Method and Description |
---|---|
MLData |
LinearRegression.compute(MLData input) |
Modifier and Type | Method and Description |
---|---|
MLData |
BasicCluster.get(int pos)
Get the specified data item by index.
|
Modifier and Type | Method and Description |
---|---|
List<MLData> |
BasicCluster.getData() |
Modifier and Type | Method and Description |
---|---|
void |
BasicCluster.add(MLData pair)
Add to the cluster.
|
void |
BasicCluster.remove(MLData pair)
Remove the specified item.
|
Modifier and Type | Method and Description |
---|---|
MLData |
EncogProgram.compute(MLData input)
Compute the output from the input MLData.
|
Modifier and Type | Method and Description |
---|---|
MLData |
EncogProgram.compute(MLData input)
Compute the output from the input MLData.
|
Modifier and Type | Method and Description |
---|---|
MLData |
PrgPopulation.compute(MLData input)
Compute the output from the best Genome.
|
Modifier and Type | Method and Description |
---|---|
MLData |
PrgPopulation.compute(MLData input)
Compute the output from the best Genome.
|
Modifier and Type | Method and Description |
---|---|
MLData |
SVM.compute(MLData input)
Compute the output for the given input.
|
Modifier and Type | Method and Description |
---|---|
int |
SVM.classify(MLData input)
Classify the input into a group.
|
MLData |
SVM.compute(MLData input)
Compute the output for the given input.
|
svm_node[] |
SVM.makeSparse(MLData data)
Convert regular Encog MLData into the "sparse" data needed by an SVM.
|
Modifier and Type | Method and Description |
---|---|
MLData |
ART1.compute(MLData input)
Compute the output for the BasicNetwork class.
|
Modifier and Type | Method and Description |
---|---|
int |
ART1.classify(MLData input)
Classify the input data to a class number.
|
MLData |
ART1.compute(MLData input)
Compute the output for the BasicNetwork class.
|
Modifier and Type | Method and Description |
---|---|
MLData |
BAM.compute(MLData input)
Setup the network logic, read parameters from the network.
|
Modifier and Type | Method and Description |
---|---|
void |
BAM.addPattern(MLData inputPattern,
MLData outputPattern)
Add a pattern to the neural network.
|
MLData |
BAM.compute(MLData input)
Setup the network logic, read parameters from the network.
|
Modifier and Type | Method and Description |
---|---|
MLData |
CPN.compute(MLData input)
Compute regression.
|
MLData |
CPN.computeInstar(MLData input)
Compute the instar layer.
|
MLData |
CPN.computeOutstar(MLData input)
Compute the outstar layer.
|
Modifier and Type | Method and Description |
---|---|
MLData |
CPN.compute(MLData input)
Compute regression.
|
MLData |
CPN.computeInstar(MLData input)
Compute the instar layer.
|
MLData |
CPN.computeOutstar(MLData input)
Compute the outstar layer.
|
Modifier and Type | Interface and Description |
---|---|
interface |
NeuralData
This is an alias class for Encog 2.5 compatibility.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicNeuralData
This is an alias class for Encog 2.5 compatibility.
|
Modifier and Type | Method and Description |
---|---|
MLData |
FreeformNetwork.compute(MLData input)
Compute regression.
|
Modifier and Type | Method and Description |
---|---|
int |
FreeformNetwork.classify(MLData input)
Classify the input into a group.
|
MLData |
FreeformNetwork.compute(MLData input)
Compute regression.
|
Modifier and Type | Method and Description |
---|---|
MLData |
NEATPopulation.compute(MLData input)
Compute regression.
|
MLData |
NEATNetwork.compute(MLData input)
Compute the output from this synapse.
|
Modifier and Type | Method and Description |
---|---|
MLData |
NEATPopulation.compute(MLData input)
Compute regression.
|
MLData |
NEATNetwork.compute(MLData input)
Compute the output from this synapse.
|
Modifier and Type | Method and Description |
---|---|
MLData |
BasicNetwork.compute(MLData input)
Compute the output for a given input to the neural network.
|
MLData |
NeuralDataMapping.getFrom() |
MLData |
NeuralDataMapping.getTo() |
Modifier and Type | Method and Description |
---|---|
int |
BasicNetwork.classify(MLData input)
Classify the input into a group.
|
MLData |
BasicNetwork.compute(MLData input)
Compute the output for a given input to the neural network.
|
void |
NeuralDataMapping.setFrom(MLData from)
Set the from data.
|
void |
NeuralDataMapping.setTo(MLData to)
Set the target data.
|
int |
BasicNetwork.winner(MLData input)
Determine the winner for the specified input.
|
Constructor and Description |
---|
NeuralDataMapping(MLData from,
MLData to)
Construct the neural data mapping class with the specified values.
|
Modifier and Type | Method and Description |
---|---|
MLData |
TrainBasicPNN.computeDeriv(MLData input,
MLData target)
Compute the derivative for target data.
|
Modifier and Type | Method and Description |
---|---|
MLData |
TrainBasicPNN.computeDeriv(MLData input,
MLData target)
Compute the derivative for target data.
|
Modifier and Type | Method and Description |
---|---|
MLData |
BasicPNN.compute(MLData input)
Compute the output from this network.
|
abstract MLData |
AbstractPNN.compute(MLData input)
Compute the output from the network.
|
Modifier and Type | Method and Description |
---|---|
int |
BasicPNN.classify(MLData input)
Classify the input into a group.
|
MLData |
BasicPNN.compute(MLData input)
Compute the output from this network.
|
abstract MLData |
AbstractPNN.compute(MLData input)
Compute the output from the network.
|
Modifier and Type | Method and Description |
---|---|
MLData |
RBFNetwork.compute(MLData input)
Compute regression.
|
Modifier and Type | Method and Description |
---|---|
MLData |
RBFNetwork.compute(MLData input)
Compute regression.
|
Modifier and Type | Method and Description |
---|---|
int |
SOM.classify(MLData input)
Classify the input into a group.
|
int |
SOM.winner(MLData input)
An alias for the classify method, kept for compatibility
with earlier versions of Encog.
|
Modifier and Type | Method and Description |
---|---|
int |
BestMatchingUnit.calculateBMU(MLData input)
Calculate the best matching unit (BMU).
|
double |
BestMatchingUnit.calculateEuclideanDistance(Matrix matrix,
MLData input,
int outputNeuron)
Calculate the Euclidean distance for the specified output neuron and the
input vector.
|
void |
BasicTrainSOM.trainPattern(MLData pattern)
Train the specified pattern.
|
Modifier and Type | Method and Description |
---|---|
MLData |
BoltzmannMachine.compute(MLData input)
Note: for Boltzmann networks, you will usually want to call the "run"
method to compute the output.
|
MLData |
HopfieldNetwork.compute(MLData input)
Note: for Hopfield networks, you will usually want to call the "run"
method to compute the output.
|
Modifier and Type | Method and Description |
---|---|
void |
HopfieldNetwork.addPattern(MLData pattern)
Train the neural network for the specified pattern.
|
MLData |
BoltzmannMachine.compute(MLData input)
Note: for Boltzmann networks, you will usually want to call the "run"
method to compute the output.
|
MLData |
HopfieldNetwork.compute(MLData input)
Note: for Hopfield networks, you will usually want to call the "run"
method to compute the output.
|
Modifier and Type | Class and Description |
---|---|
class |
ImageMLData
An extension of the BasicNeuralData class that is designed to hold images for
input into a neural network.
|
Modifier and Type | Method and Description |
---|---|
MLData |
DataNormalization.buildForNetworkInput(double[] data)
Build "input data for a neural network" based on the input values
provided.
|
Modifier and Type | Method and Description |
---|---|
static String |
EncogUtility.formatNeuralData(MLData data)
Format neural data as a list of numbers.
|
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