checking in perceptron

Author: magicindian

src/aima/learning/neural/Perceptron.java

@@ -0,0 +1,62 @@
+package aima.learning.neural;
+
+import aima.util.Matrix;
+
+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);
+
+	}
+
+	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);
+			}
+		}
+	}
+
+	public Vector predict(NNExample nne) {
+		return processInput(nne.getInput());
+	}
+
+	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;
+	}
+
+}