nguyenwidrow.java

利用Java实现的神经网络工具箱

/*
 * Created on 23/10/2004
 *
 * TODO To change the template for this generated file go to
 * Window - Preferences - Java - Code Generation - Code and Comments
 */
package neuralnetworktoolkit.neuralnetwork.weightinitialization;

import neuralnetworktoolkit.neuralnetwork.INeuralNetwork;

/**
 * @author iver
 *
 * TODO To change the template for this generated type comment go to
 * Window - Preferences - Java - Code Generation - Code and Comments
 */
public class NguyenWidrow extends WeightInitialization {
	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.neuralnetwork.WeightInitialization#initialize()
	 */
	public void initialize(INeuralNetwork nn) {
		int numberOfHiddenNeurons = 0;
		int numberOfInputs = nn.getInputSize();
		double range = 2;
		double factor;
		double weight, bias;
		
		for (int i = 0; i < nn.getNetworkSize() - 1; i++) {
			numberOfHiddenNeurons = numberOfHiddenNeurons
					+ nn.getLayer(i).getLayerSize();
		}
		
		System.out.println("nn: " + numberOfHiddenNeurons);
		//((0.7h^(1/i))/R)
		
		factor = (.7 * Math.pow(numberOfHiddenNeurons, 1 / numberOfInputs))/range;
				
		for (int i = (nn.getNetworkSize() - 1); i >= 0; i--) {
			for (int j = 0; j < nn.getLayer(i).getLayerSize(); j++) {
				
				bias = randomNumber(-factor, factor);
				nn.getLayer(i).setBias(bias, j);
				
				for (int k = 0; k < nn.getLayer(i).getWeightSize(j); k++) {
					
					weight = randomNumber(0, factor);
					nn.getLayer(i).setWeight(weight, k, j);
					
				}
				
			}
		}
	}
}