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java.lang.Object | +--com.huguesjohnson.jail.neuralNetwork.NeuralNetwork | +--com.huguesjohnson.jail.neuralNetwork.BackPropagationNetwork
BackPropagationNetwork
Constructor Summary | |
BackPropagationNetwork(int inputLayerSize,
int hiddenLayerSize,
int outputLayerSize,
int inputSize,
double punishRate,
double rewardRate,
double errorThreshold,
double minWeight,
double maxWeight)
constructor |
Method Summary | |
double[] |
getDesiredOutput()
returns the desired output of the network |
double |
getError()
returns the error of the network |
com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer |
getHiddenLayer()
returns the hidden layer |
com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer |
getInputLayer()
returns the input layer |
double[] |
getOutput()
returns the output of the network |
com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer |
getOutputLayer()
returns the output layer |
void |
networkRunComplete()
method invoked when the network run is complete |
void |
run()
run the network |
void |
setDesiredOutput(double[] desiredOutput)
sets the desired output of the network |
void |
setHiddenLayer(com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer hiddenLayer)
sets the hidden layer, automatically adjusts links between input layer and hidden layer and hidden layer and output layer |
void |
setInput(double[] input)
sets the input of the network |
void |
setInputLayer(com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer inputLayer)
sets the input layer, automatically adjusts links between input layer and hidden layer |
void |
setOutputLayer(com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer outputLayer)
sets the output layer, automatically adjusts links between hidden layer and output layer |
void |
train(int iterations)
train the network |
Methods inherited from class com.huguesjohnson.jail.neuralNetwork.NeuralNetwork |
finalize, log, start, stop, toString |
Methods inherited from class java.lang.Object |
clone, equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
public BackPropagationNetwork(int inputLayerSize, int hiddenLayerSize, int outputLayerSize, int inputSize, double punishRate, double rewardRate, double errorThreshold, double minWeight, double maxWeight)
inputLayerSize
- the number of neurons in the input layerhiddenLayerSize
- the number of neurons in the hidden layeroutputLayerSize
- the number of neurons in the output layerinputSize
- the number of inputs to the layerpunishRate
- the punish rate for every neuron in the layerrewardRate
- the reward rate for every neuron in the layererrorThreshold
- the error threshold for every neuron in the layerminWeight
- minimum weight on a neuron linkmaxWeight
- maximum weight on a neuron linkMethod Detail |
public double[] getOutput()
getOutput
in class NeuralNetwork
public double getError()
getError
in class NeuralNetwork
public void setDesiredOutput(double[] desiredOutput)
setDesiredOutput
in class NeuralNetwork
desiredOutput
- the desired output of the networkpublic void setInput(double[] input)
setInput
in class NeuralNetwork
input
- the input of the networkpublic final com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer getInputLayer()
public final com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer getHiddenLayer()
public final com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer getOutputLayer()
public final void setInputLayer(com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer inputLayer)
inputLayer
- new input layerpublic final void setHiddenLayer(com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer hiddenLayer)
hiddenLayer
- new hidden layerpublic final void setOutputLayer(com.huguesjohnson.jail.neuralNetwork.BackPropagationLayer outputLayer)
outputLayer
- new output layerpublic void train(int iterations)
train
in class NeuralNetwork
iterations
- number of training iterations to runpublic void run()
run
in interface java.lang.Runnable
run
in class NeuralNetwork
public void networkRunComplete()
public double[] getDesiredOutput()
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