org.joone.io

Class StreamOutputSynapse

java.lang.Object

org.joone.engine.Synapse

org.joone.io.StreamOutputSynapse

All Implemented Interfaces:

java.io.Serializable, InputPatternListener, Learnable, LearnableSynapse, NeuralElement, OutputPatternListener, Inspectable, PlugInListener

Direct Known Subclasses:

FileOutputSynapse, ImageOutputSynapse, JDBCOutputSynapse, MemoryOutputSynapse, XLSOutputSynapse

public abstract class StreamOutputSynapse
extends Synapse
implements PlugInListener

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
StreamOutputSynapse()

Method Summary

Modifier and Type	Method and Description
boolean	addPlugIn(OutputConverterPlugIn aNewPlugIn) Adds a plug in to the stream output synapse for data preprocessing.
java.util.TreeSet	check() Base for check messages.
void	dataChanged(PlugInEvent data) This method is called by plug-ins whenever data is changed.
void	fwdPut(Pattern pattern) The standard fwdPut method.
OutputConverterPlugIn	getPlugIn()
char	getSeparator() Returns the column separator creation date: (23/04/00 0.50.18)
boolean	isBuffered() Checks the buffered status of this synapse.
void	removeAllPlugIns() Removes (and disconnects) all (cascading) plug-ins.
Pattern	revGet() Returns the error pattern coming from the next layer during the training phase
void	setBuffered(boolean buf) Sets the buffered status of this synapse.
boolean	setPlugIn(OutputConverterPlugIn newPlugIn) Deprecated. use addPlugIn(OutputConverterPlugIn)
void	setSeparator(char newSeparator) Inserire qui la descrizione del metodo.
abstract void	write(Pattern pattern) Custom xxxOutputSynapses need to implement at least this method.

Methods inherited from class org.joone.engine.Synapse

addNoise, canCountSteps, fwdGet, getIgnoreBefore, getInputDimension, getLearner, getLearningRate, getMomentum, getMonitor, getName, getOutputDimension, getWeights, init, initLearner, InspectableTitle, Inspections, isEnabled, isInputFull, isLoopBack, isOutputFull, randomize, reset, revPut, setEnabled, setIgnoreBefore, setInputDimension, setInputFull, setLearningRate, setLoopBack, setMomentum, setMonitor, setName, setOutputDimension, setOutputFull, setWeights

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

StreamOutputSynapse

public StreamOutputSynapse()

Method Detail

fwdPut

public void fwdPut(Pattern pattern)

The standard fwdPut method. This method performs buffering of pattern data if required according to the buffered flag. If buffered mode is set then the default multiple writing method write(Pattern [] patterns) method is called to loop through all the patterns calling the single pattern writing abstract method write(Pattern pattern) to write the pattern to the appropriate media. If the synapse is not in buffered mode then the pattern is simply passed to the single pattern writing abstract method write(Pattern pattern) to write the pattern to the appropriate media. Custom xxxOutputSynapse classes can over ride this method if they would like to perform a custom buffering implementation. In this case it will be the responsiblity of the custom class to ensure all patterns are output correctly and any conversion is done prior to writing data.

Specified by:

fwdPut in interface OutputPatternListener

Overrides:

fwdPut in class Synapse

Parameters:

pattern - neural.engine.Pattern

write

public abstract void write(Pattern pattern)

Custom xxxOutputSynapses need to implement at least this method. The custom synapse should write the pattern out to the appropriate media. All patterns including the ending pattern with pattern.getCount()=-1 will be passed to this method so that custom output synapses can perform final processing.

getSeparator

public char getSeparator()

Returns the column separator creation date: (23/04/00 0.50.18)

Returns:

char

revGet

public Pattern revGet()

Description copied from interface: OutputPatternListener

Returns the error pattern coming from the next layer during the training phase

Specified by:

revGet in interface OutputPatternListener

Overrides:

revGet in class Synapse

Returns:

neural.engine.Pattern

setSeparator

public void setSeparator(char newSeparator)

Inserire qui la descrizione del metodo. Data di creazione: (23/04/00 0.50.18)

Parameters:

newSeparator - char

setBuffered

public void setBuffered(boolean buf)

Sets the buffered status of this synapse.

isBuffered

public boolean isBuffered()

Checks the buffered status of this synapse.

check

public java.util.TreeSet check()

Description copied from class: Synapse

Base for check messages. Subclasses should call this method from thier own check method.

Specified by:

check in interface NeuralElement

Overrides:

check in class Synapse

Returns:

validation errors.

See Also:

InputPaternListener, OutputPaternListener

getPlugIn

public OutputConverterPlugIn getPlugIn()

Returns:

neural.engine.ConverterPlugIn

setPlugIn

public boolean setPlugIn(OutputConverterPlugIn newPlugIn)

Deprecated. use addPlugIn(OutputConverterPlugIn)

Sets the plugin for the data postprocessing

Parameters:

newPlugIn - neural.engine.OutputConverterPlugIn

addPlugIn

public boolean addPlugIn(OutputConverterPlugIn aNewPlugIn)

Adds a plug in to the stream output synapse for data preprocessing. If one or more plug ins are already added to the this synapse, the plug in will be added at the end of the list of plug ins.

Parameters:

aNewPlugIn - The new converter plug in to add (at the end of the list).

Returns:

true when the plug in is added, false when the plug in is not added, e.g. in case the plug in is already added / connected to another synapse.

removeAllPlugIns

public void removeAllPlugIns()

Removes (and disconnects) all (cascading) plug-ins.

dataChanged

public void dataChanged(PlugInEvent data)

Description copied from interface: PlugInListener

This method is called by plug-ins whenever data is changed.

Specified by:

dataChanged in interface PlugInListener

Parameters:

data - the event that is send, i.e. the event indicating that the data is changed.