Perceptron Implemented

Author: magicindian

…rning/statistics/ActivationFunction.java …/learning/neural/ActivationFunction.javasrc/aima/learning/statistics/ActivationFunction.java renamed to src/aima/learning/neural/ActivationFunction.java src/aima/learning/statistics/ActivationFunction.java renamed to src/aima/learning/neural/ActivationFunction.java

@@ -2,7 +2,7 @@
  * Created on Aug 3, 2005
  *
  */
-package aima.learning.statistics;
+package aima.learning.neural;
 
 /**
  * @author Ravi Mohan

src/aima/learning/neural/BackPropLearning.java

@@ -18,8 +18,8 @@ public BackPropLearning(double learningRate, double momentum) {
 
 	}
 
-	public void setNeuralNetwork(FeedForwardNeuralNetwork ffnn) {
-
+	public void setNeuralNetwork(FunctionApproximator fapp) {
+		FeedForwardNeuralNetwork ffnn = (FeedForwardNeuralNetwork) fapp;
 		this.hiddenLayer = ffnn.getHiddenLayer();
 		this.outputLayer = ffnn.getOutputLayer();
 		this.hiddenSensitivity = new LayerSensitivity(hiddenLayer);

src/aima/learning/neural/HardLimitActivationFunction.java

@@ -0,0 +1,20 @@
+package aima.learning.neural;
+
+
+public class HardLimitActivationFunction implements ActivationFunction {
+
+	public double activation(double parameter) {
+
+		if (parameter < 0.0) {
+			return 0.0;
+		} else {
+			return 1.0;
+		}
+	}
+
+	public double deriv(double parameter) {
+
+		return 0.0;
+	}
+
+}

src/aima/learning/neural/Layer.java

@@ -1,6 +1,5 @@
 package aima.learning.neural;
 
-import aima.learning.statistics.ActivationFunction;
 import aima.util.Matrix;
 import aima.util.Util;
 

src/aima/learning/neural/LogSigActivationFunction.java

@@ -1,6 +1,5 @@
 package aima.learning.neural;
 
-import aima.learning.statistics.ActivationFunction;
 
 public class LogSigActivationFunction implements ActivationFunction {
 

src/aima/learning/neural/NNDataSet.java

@@ -8,7 +8,6 @@
 
 import aima.learning.framework.DataSet;
 import aima.learning.framework.Example;
-import aima.learning.statistics.Numerizer;
 import aima.util.Pair;
 import aima.util.Util;
 

src/aima/learning/neural/NNTrainingScheme.java

@@ -5,5 +5,5 @@ public interface NNTrainingScheme {
 
 	void processError(FeedForwardNeuralNetwork network, Vector error);
 
-	void setNeuralNetwork(FeedForwardNeuralNetwork ffnn);
+	void setNeuralNetwork(FunctionApproximator ffnn);
 }

…/aima/learning/statistics/Numerizer.java src/aima/learning/neural/Numerizer.javasrc/aima/learning/statistics/Numerizer.java renamed to src/aima/learning/neural/Numerizer.java src/aima/learning/statistics/Numerizer.java renamed to src/aima/learning/neural/Numerizer.java

@@ -2,7 +2,7 @@
  * Created on Aug 6, 2005
  *
  */
-package aima.learning.statistics;
+package aima.learning.neural;
 
 import java.util.List;
 

src/aima/learning/neural/PureLinearActivationFunction.java

@@ -1,6 +1,5 @@
 package aima.learning.neural;
 
-import aima.learning.statistics.ActivationFunction;
 
 public class PureLinearActivationFunction implements ActivationFunction {
 

src/aima/learning/neural/Vector.java

@@ -46,6 +46,14 @@ public Vector minus(Vector v) {
 		return result;
 	}
 
+	public Vector plus(Vector v) {
+		Vector result = new Vector(size());
+		for (int i = 0; i < size(); i++) {
+			result.setValue(i, getValue(i) + v.getValue(i));
+		}
+		return result;
+	}
+
 	public int indexHavingMaxValue() {
 		if (size() <= 0) {
 			throw new RuntimeException("can't perform this op on empty vector");