net.sourceforge.jabm.examples.elfarolbar.AutoregressivePredictionStrategy.java Source code

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Here is the source code for net.sourceforge.jabm.examples.elfarolbar.AutoregressivePredictionStrategy.java

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/*
 * JABM - Java Agent-Based Modeling Toolkit
 * Copyright (C) 2013 Steve Phelps
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation; either version 3 of
 * the License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 * See the GNU General Public License for more details.
 */
package net.sourceforge.jabm.examples.elfarolbar;

import org.springframework.beans.factory.InitializingBean;
import org.springframework.beans.factory.annotation.Required;

import cern.jet.random.AbstractContinousDistribution;

public class AutoregressivePredictionStrategy extends AbstractPredictionStrategy implements InitializingBean {

    protected double[] coefficients;

    protected double offset;

    protected int windowSize;

    protected AbstractContinousDistribution coefficientDistribution;

    protected AbstractContinousDistribution offsetDistribution;

    public AutoregressivePredictionStrategy(int memorySize, AbstractContinousDistribution coefficientDistribution) {
        super();
        this.coefficientDistribution = coefficientDistribution;
        afterPropertiesSet();
    }

    public AutoregressivePredictionStrategy() {
        super();
    }

    public void randomlyInitialise() {
        for (int i = 0; i < windowSize; i++) {
            coefficients[i] = coefficientDistribution.nextDouble();
        }
        offset = offsetDistribution.nextDouble();
    }

    @Override
    public void makePrediction() {
        currentPrediction = 0.0;
        for (int i = 0; i < windowSize; i++) {
            currentPrediction += barTender.getAttendanceAtLag(i + 1) * coefficients[i];
        }
        currentPrediction += offset * 100;
    }

    public AbstractContinousDistribution getCoefficientDistribution() {
        return coefficientDistribution;
    }

    /**
     * The probability distribution used to draw coefficient values.
     * @param coefficientDistribution
     */
    @Required
    public void setCoefficientDistribution(AbstractContinousDistribution coefficientDistribution) {
        this.coefficientDistribution = coefficientDistribution;
    }

    /**
     * The probability distribution used to draw the offset value.
     * @return
     */
    @Required
    public AbstractContinousDistribution getOffsetDistribution() {
        return offsetDistribution;
    }

    public void setOffsetDistribution(AbstractContinousDistribution offsetDistribution) {
        this.offsetDistribution = offsetDistribution;
    }

    @Override
    public void afterPropertiesSet() {
        coefficients = new double[windowSize];
        randomlyInitialise();
    }

    public int getWindowSize() {
        return windowSize;
    }

    public void setWindowSize(int windowSize) {
        this.windowSize = windowSize;
    }

    @Override
    public String toString() {
        return "AutoregressivePredictionStrategy [offset=" + offset + ", windowSize=" + windowSize
                + ", coefficientDistribution=" + coefficientDistribution + ", offsetDistribution="
                + offsetDistribution + "]";
    }

}