package aima.core.learning.neural;
import aima.core.util.math.Matrix;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
* @author Mike Stampone
*/
public class Perceptron implements FunctionApproximator {
private final Layer layer;
private Vector lastInput;
public Perceptron(int numberOfNeurons, int numberOfInputs) {
this.layer = new Layer(numberOfNeurons, numberOfInputs, 2.0, -2.0,
new HardLimitActivationFunction());
}
public Vector processInput(Vector input) {
lastInput = input;
return layer.feedForward(input);
}
public void processError(Vector error) {
Matrix weightUpdate = error.times(lastInput.transpose());
layer.acceptNewWeightUpdate(weightUpdate);
Vector biasUpdate = layer.getBiasVector().plus(error);
layer.acceptNewBiasUpdate(biasUpdate);
}
/**
* Induces the layer of this perceptron from the specified set of examples
*
* @param innds
* a set of training examples for constructing the layer of this
* perceptron.
* @param numberofEpochs
* the number of training epochs to be used.
*/
public void trainOn(NNDataSet innds, int numberofEpochs) {
for (int i = 0; i < numberofEpochs; i++) {
innds.refreshDataset();
while (innds.hasMoreExamples()) {
NNExample nne = innds.getExampleAtRandom();
processInput(nne.getInput());
Vector error = layer.errorVectorFrom(nne.getTarget());
processError(error);
}
}
}
/**
* Returns the outcome predicted for the specified example
*
* @param nne
* an example
*
* @return the outcome predicted for the specified example
*/
public Vector predict(NNExample nne) {
return processInput(nne.getInput());
}
/**
* Returns the accuracy of the hypothesis on the specified set of examples
*
* @param nnds
* the neural network data set to be tested on.
*
* @return the accuracy of the hypothesis on the specified set of examples
*/
public int[] testOnDataSet(NNDataSet nnds) {
int[] result = new int[] { 0, 0 };
nnds.refreshDataset();
while (nnds.hasMoreExamples()) {
NNExample nne = nnds.getExampleAtRandom();
Vector prediction = predict(nne);
if (nne.isCorrect(prediction)) {
result[0] = result[0] + 1;
} else {
result[1] = result[1] + 1;
}
}
return result;
}
}