edu.stevens.cpe.reservior.readout.GradientDecent.java Source code

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Here is the source code for edu.stevens.cpe.reservior.readout.GradientDecent.java

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/*******************************************************************************
 *  Copyright 2013 William Koch
 * 
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 * 
 *      http://www.apache.org/licenses/LICENSE-2.0
 * 
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 ******************************************************************************/
package edu.stevens.cpe.reservior.readout;

import java.util.Arrays;

import org.apache.commons.math3.linear.Array2DRowFieldMatrix;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;

import cern.colt.matrix.DoubleMatrix2D;

import edu.stevens.cpe.reservior.ReserviorException;
import edu.stevens.cpe.reservior.Reservoir;
import edu.stevens.cpe.reservior.ReservoirNetwork;
import edu.stevens.cpe.reservior.layers.Input;
import edu.stevens.cpe.reservior.layers.ReadoutFunction;

public class GradientDecent {
    private ReservoirNetwork reservoir;
    private MLDataSet trainingSet;
    private double learningRate;

    DoubleMatrix2D X;
    DoubleMatrix2D Y;

    /**
     * The number of weights = outputs * reservoir nodes
     */
    private double[] deltaWeights;

    public GradientDecent(ReservoirNetwork reservoir, MLDataSet trainingSet, double learningRate) {
        this.reservoir = reservoir;
        this.trainingSet = trainingSet;
        this.learningRate = learningRate;
        //this.deltaWeights = new double [reservoir.getReservior().getNeuronCount() * reservoir.getReadout().getNumberOutputs()];
        Arrays.fill(deltaWeights, 0.0);
    }

    public GradientDecent(DoubleMatrix2D X, DoubleMatrix2D Y, double learningRate) {
        this.learningRate = learningRate;
        this.X = X;
        this.Y = Y;
    }

    public void iteration() {
        for (int i = 0; i < X.rows(); i++) {
            /*
            try {
                   
               //Output
               double output = X.viewRow(i).zDotProduct(W.viewDice().viewRow(0));
               double target = Y.get(i, 0);
               double error += ErrorUtility.computeLinearRegressionError(new double [] {target}, new double [] { output});
                
               input.scalarMultiply(learningRate*error);
                   
            //   deltaWeights = deltaWeights + 
            } catch (ReserviorException e) {
               e.printStackTrace();
            }
                
                
               */

        }
    }

}