eu.crisis_economics.abm.model.configuration.UniformDistributionModelParameterConfiguration.java Source code

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Here is the source code for eu.crisis_economics.abm.model.configuration.UniformDistributionModelParameterConfiguration.java

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/*
 * This file is part of CRISIS, an economics simulator.
 * 
 * Copyright (C) 2015 John Kieran Phillips
 *
 * CRISIS 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.
 *
 * CRISIS 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.
 *
 * You should have received a copy of the GNU General Public License
 * along with CRISIS.  If not, see <http://www.gnu.org/licenses/>.
 */
package eu.crisis_economics.abm.model.configuration;

import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.junit.Assert;

import com.google.common.base.Preconditions;
import com.google.inject.Inject;
import com.google.inject.Key;
import com.google.inject.TypeLiteral;
import com.google.inject.name.Named;

import eu.crisis_economics.abm.model.ConfigurationComponent;
import eu.crisis_economics.abm.model.Layout;
import eu.crisis_economics.abm.model.Parameter;
import eu.crisis_economics.abm.model.parameters.ModelParameter;
import eu.crisis_economics.abm.simulation.injection.factories.InjectionFactoryUtils;

/**
  * A {@link ComponentConfiguration} for a {@link RealDistribution} implemented
  * by the uniform distribution (on a customizable interval).<br><br>
  * 
  * This {@link ComponentConfiguration} provides a {@link Parameter}{@code <Double>}
  * implemented by a uniform distribution. The profile of the distribution
  * is customizable.<br><br>
  * 
  * @author phillips
  */
@ConfigurationComponent(DisplayName = "Uniform Distribution", Description = "A model parameter whose values are drawn at random uniformly from a customizable "
        + "contiguous interval.")
public final class UniformDistributionModelParameterConfiguration
        extends AbstractDistributionModelParameterConfiguration
        implements SampledDoubleModelParameterConfiguration, TimeseriesDoubleModelParameterConfiguration {

    private static final long serialVersionUID = -1281219507220139834L;

    private static final double DEFAULT_MINIMUM = 0., DEFAULT_MAXIMUM = Double.MAX_VALUE;

    @Layout(FieldName = "Minimum", Order = 1.0)
    @Parameter(ID = "UNIFORM_REAL_DISTRIBUTION_MINIMUM")
    private double minimum;
    @Layout(FieldName = "Maximum", Order = 1.1)
    @Parameter(ID = "UNIFORM_REAL_DISTRIBUTION_MAXIMUM")
    private double maximum;

    public double getMinimum() {
        return minimum;
    }

    public void setMinimum(final double minimum) {
        this.minimum = minimum;
    }

    public double getMaximum() {
        return maximum;
    }

    public void setMaximum(final double maximum) {
        this.maximum = maximum;
    }

    /**
      * Create a {@link UniformDistributionModelParameterConfiguration} object with default
      * parameters.
      */
    public UniformDistributionModelParameterConfiguration() {
        this(DEFAULT_MINIMUM, DEFAULT_MAXIMUM);
    }

    /**
      * Create a {@link UniformDistributionModelParameterConfiguration} object with custom
      * parameters.<br><br>
      * 
      * See also {@link UniformDistributionModelParameterConfiguration}.
      * 
      * @param minimum
      *        The minimum in the range of the distribution.
      * @param maximum
      *        The maximum in the range of the distribution. This argument must not be 
      *        less than {@code minimum}.
      */
    public UniformDistributionModelParameterConfiguration(final double minimum, final double maximum) {
        Preconditions.checkArgument(maximum >= minimum);
        this.minimum = minimum;
        this.maximum = maximum;
    }

    @Override
    public void assertParameterValidity() {
        Preconditions.checkArgument(maximum >= minimum,
                toParameterValidityErrMsg("Domain maximum must not be less than domain minimum."));
    }

    /**
      * A lightweight injection wrapper for the {@link UniformRealDistribution} class.
      * 
      * @author phillips
      */
    private static final class UniformRealDistributionImpl extends UniformRealDistribution {

        @Inject
        UniformRealDistributionImpl(@Named("UNIFORM_REAL_DISTRIBUTION_MINIMUM") final double minimum,
                @Named("UNIFORM_REAL_DISTRIBUTION_MAXIMUM") final double maximum) {
            super(minimum, maximum);
        }

        private static final long serialVersionUID = -2276076222570551094L;
    }

    @Override
    protected void addBindings() {
        super.addBindings();
        install(InjectionFactoryUtils.asPrimitiveParameterModule("UNIFORM_REAL_DISTRIBUTION_MINIMUM", minimum,
                "UNIFORM_REAL_DISTRIBUTION_MAXIMUM", maximum));
        bind(RealDistribution.class).to(UniformRealDistributionImpl.class);
    }

    /**
      * A lightweight test for this {@link ConfigurationComponent}. This snippet
      * creates a {@link Parameter}{@code <Double>} using an instance of
      * {@link UniformDistributionModelParameterConfiguration} and then tests
      * whether {@link Double} values drawn from this {@link Parameter} are 
      * strictly positive and have the expected mean.
      * 
      */
    public static void main(String[] args) {
        final UniformDistributionModelParameterConfiguration configuration = new UniformDistributionModelParameterConfiguration();
        configuration.setMinimum(.5);
        configuration.setMaximum(1.5);
        final ModelParameter<Double> distribution = configuration.createInjector()
                .getInstance(Key.get(new TypeLiteral<ModelParameter<Double>>() {
                }));
        double mean = 0.;
        final int numSamples = 10000000;
        for (int i = 0; i < numSamples; ++i) {
            final double value = distribution.get();
            mean += value;
            Assert.assertTrue(value >= .5 && value <= 1.5);
        }
        mean /= numSamples;
        Assert.assertTrue(Math.abs(mean - 1.) < 1.e-3);
        System.out.println(mean);
    }
}