Learnable learnable
LearnableLayer learnableLayer
LearnableSynapse learnableSynapse
Monitor monitor
double beta
double timeConstant
Matrix initialState
FIRFilter[][] fir
int taps
java.util.List<E> theDeltaRuleExtenders
java.util.List<E> theGradientExtenders
UpdateWeightExtender theUpdateWeightExtender
int m_taps
double[] memory
double[] backmemory
double[] outs
double[] bouts
Matrix array
double lrate
double momentum
int LayerWidth
int LayerHeight
int LayerDepth
SpatialMap space_map
double timeConstant
int orderingPhase
double initialGaussianSize
private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, java.lang.ClassNotFoundException
java.io.IOException
java.lang.ClassNotFoundException
private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException
java.io.IOException
java.lang.String LayerName
int rows
Matrix bias
Monitor monitor
int m_batch
boolean learning
boolean learnable
getLearner
java.util.Vector<E> inputPatternListeners
java.util.Vector<E> outputPatternListeners
double[][] value
double[][] delta
boolean[][] enabled
boolean[][] fixed
int m_rows
int m_cols
WeightInitializer weightInitializer
int preLearning
boolean learning
int currentCicle
int run
int saveCurrentCicle
int saveRun
int patterns
int validationPatterns
int totCicles
double learningRate
double momentum
double globalError
int batchSize
boolean useRMSE
LearnerFactory theLearnerFactory
Monitor parent
boolean supervisioned
boolean singleThreadMode
int learningMode
java.util.List<E> learners
java.util.Hashtable<K,V> params
boolean backprop
NeuralNet nnet
java.util.Vector<E> outputs
java.lang.String name
Monitor mon
int inputDimension
boolean outputFull
boolean enabled
OutputPatternListener activeSynapse
OutputPatternListener defaultSynapse
RbfGaussianParameters[] theGaussianParameters
boolean theUseRandomSelector
RbfRandomCenterSelector theRandomSelector
RpropExtender theRpropExtender
boolean lineseek
java.util.List<E> learners
java.util.List<E> z
java.util.List<E> Z
java.util.List<E> T
java.util.List<E> U
Layer inputLayer
Layer outputLayer
double learningRate
double momentum
double[][][] p
double[][][] updateP
double[][] q
double[][] updateQ
double[] lastError
NeuralNet network
double currentSSE
double previousSSE
double stepUpScale
double stepDownScale
boolean verbose
double upperLearningRate
double lowerLearningRate
int patternCount
java.util.Random random
double shockFactor
double weightMagnitude
boolean interCycleUpdates
int minimumPatternCount
double updateProbability
java.util.List<E> weights
int cycleCount
ContextLayer layer
int index
int k
double bias
Layer layer
int index
int K
boolean inU
java.util.List<E> weights
java.util.List<E> I
java.util.List<E> initialStates
Learnable learnable
int K
int i
int j
Layer layer
Synapse synapse
double flatSpotConstant
double InitialGaussianSize
double CurrentGaussianSize
int map_width
int map_height
int map_depth
int win_x
int win_y
int win_z
int TotalEpochs
int orderingPhase
double TimeConstant
java.lang.String fieldName
double learningRate
double momentum
int inputDimension
int outputDimension
boolean inputFull
boolean outputFull
Monitor monitor
int ignoreBefore
boolean loopBack
Matrix array
int m_batch
boolean enabled
boolean learnable
getLearner
double flatSpotConstant
StreamInputSynapse desired
NeuralNet net
boolean lastErrorPatternReady
ComparisonSynapse theComparisonSynapse
LinearLayer theLinearLayer
boolean enabled
boolean outputFull
StreamInputSynapse desired
Monitor monitor
java.lang.String name
StreamInputSynapse desired
double upperBit
double lowerBit
double lowerBitPercentage
double upperBitPercentage
AbstractTeacherSynapse theTeacherSynapse
LinearLayer theLinearLayer
boolean enabled
boolean outputFull
AbstractTeacherSynapse theTeacherToUse
TeacherSynapse
will be used.
The moment the teacher (theTeacherSynapse
) is initialized is done
the first time getTheTeacherSynapse()
method is called. At that
moment we are also able to set the monitor object.StreamInputSynapse desired
Monitor monitor
java.lang.String name
boolean disableCurrentConvergence
java.util.List<E> listeners
ConvergenceListener
s.double size
ConvergenceEvent
will be generated.int cycles
int cycleCounter
size
.NeuralNet net
double percentage
ConvergenceEvent
will be generated.int cycles
int cycleCounter
percentage
.double lastError
double lowerBound
double upperBound
boolean sqrtFanIn
true
), or should
be use the normal fan-in (false
) to determine the interval to init the weights with.private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, java.lang.ClassNotFoundException
java.io.IOException
java.lang.ClassNotFoundException
private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException
java.io.IOException
java.lang.String fileName
java.io.File imageDirectory
java.lang.String theFileFilter
java.util.Vector<E> FileNameList
java.awt.Image[] MultiImages
int DesiredWidth
int DesiredHeight
boolean ColourMode
StreamInputSynapse inputSynapse
java.util.Vector<E> inputs
StreamInputSynapse activeSynapse
StreamInputSynapse defaultSynapse
java.lang.String name
Monitor mon
int outputDimension
private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, java.lang.ClassNotFoundException
java.io.IOException
- The Input Output Exceptionjava.lang.ClassNotFoundException
- The class not found exceptionprivate void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException
java.io.IOException
- The Input Output Exception if anyjava.lang.String driverName
java.lang.String dbURL
java.lang.String SQLQuery
Fifo patterns
boolean zeroPattern
int firstRow
int lastRow
int firstCol
int lastCol
java.lang.String advColumnsSel
boolean buffered
char decimalPoint
boolean StepCounter
ConverterPlugIn plugIn
int maxBufSize
java.util.List<E> plugInListeners
int myFirstRow
int myLastRow
char separator
boolean buffered
OutputConverterPlugIn nextPlugIn
private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, java.lang.ClassNotFoundException
java.io.IOException
java.lang.ClassNotFoundException
private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException
java.io.IOException
java.lang.String URL
java.net.URL cURL
private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, java.lang.ClassNotFoundException
java.io.IOException
java.lang.ClassNotFoundException
private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException
java.io.IOException
java.lang.String i_sheet_name
int i_sheet_index
boolean file_chk
java.lang.String fileName
java.lang.String FileName
int o_sheet_index
int row_no
int startCol
int startRow
java.lang.String o_sheet_name
private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, java.lang.ClassNotFoundException
java.io.IOException
- The Input Output Exceptionjava.lang.ClassNotFoundException
- The class not found exceptionprivate void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException
java.io.IOException
- The Input Output Exception if anyjava.lang.String[] months
java.lang.String[] frequency
java.lang.String[] freq_conv
java.lang.String Symbol
java.text.DateFormat date_formater
java.util.Calendar CalendarStart
java.util.Calendar CalendarEnd
java.lang.String DateStart
java.lang.String DateEnd
java.lang.String Period
java.util.Vector<E>[] StockData
java.util.Vector<E> StockDates
java.lang.String[] ColumnNames
java.lang.String sNeuralNet
NeuralNet NestedNeuralNet
LinearLayer lin
java.util.Vector<E> layers
java.lang.String NetName
Monitor mon
Layer inputLayer
Layer outputLayer
ComparingElement teacher
java.util.Vector<E> listeners
MacroInterface macroPlugin
boolean scriptingEnabled
NeuralNetAttributes descriptor
java.util.Hashtable<K,V> params
Layer[] orderedLayers
boolean stopFastRun
int patternNumber
java.util.List<E> layers
boolean isRunning
NeuralNet net
NeuralNet clone
double epsilon
java.util.List<E> listeners
java.util.List<E> outputsAfterPattern
java.util.List<E> information
double infoMax
java.util.List<E> averageOutputs
java.util.List<E> variance
double varianceMax
java.util.List<E> gamma
boolean optimized
java.util.List<E> nodeList
java.util.List<E> inputNodes
java.util.List<E> outputNodes
java.util.List<E> contextNodes
java.util.List<E> orderedNodes
boolean fixed
int patternCount
DirectSynapse inputSynapse
NetworkLayer network
javax.swing.JLabel horizontalSpacingLabel
javax.swing.JSpinner horizontalSpacingSpinner
javax.swing.JTabbedPane networkViewerTabbedPane
javax.swing.JPanel nodeViewerCommandPanel
javax.swing.JPanel nodeViewerPanel
javax.swing.JScrollPane nodeViewerScrollPane
javax.swing.JButton plotButton
javax.swing.JScrollPane treeViewerScrollPane
javax.swing.JTree treeViewerTree
javax.swing.JLabel verticalSpacingLabel
javax.swing.JSpinner verticalSpacingSpinner
AbstractConverterPlugIn nextPlugIn
java.lang.String name
boolean connected
java.util.Vector<E> pluginListeners
java.lang.String AdvancedSerieSelector
AdvancedSerieSelector
instructs this plug-in what serie/columns
it should process. The format of this specification is a common separated list of
values and ranges. E.g '1,2,5,7' will instruct the converter to convert serie 1
and 2 and 5 and 7. A range can also be used e.g '2,4,5-8,9' will instruct the
converter to process serie 2 and 4 and 5 and 6 and 7 and 8 and 9. A range is specifed
using a '-' character with the number of the serie on either side.
Note NO negative numbers can be used in the AdvancedSerieSelector
.
MacroManager macroManager
StreamInputSynapse trainingSet
StreamInputSynapse validationSet
boolean validation
int validationPatterns
int trainingPatterns
MacroManager macroManager
java.lang.String name
int rate
NeuralNet neuralNet
RbfGaussianLayer theRbfGaussianLayer
java.util.Vector<E> thePatterns
java.util.List<E> theConvertedSeries
double upperBit
double lowerBit
java.io.PrintWriter weightWriter
NodesAndWeights nodesAndWeights
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