path: root/src/main/java/org/apache/commons/math3/random/ValueServer.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 org.apache.commons.math3.random;

import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.ZeroException;
import org.apache.commons.math3.exception.util.LocalizedFormats;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.MalformedURLException;
import java.net.URL;

/**
 * Generates values for use in simulation applications.
 *
 * <p>How values are generated is determined by the <code>mode</code> property.
 *
 * <p>Supported <code>mode</code> values are:
 *
 * <ul>
 *   <li>DIGEST_MODE -- uses an empirical distribution
 *   <li>REPLAY_MODE -- replays data from <code>valuesFileURL</code>
 *   <li>UNIFORM_MODE -- generates uniformly distributed random values with mean = <code>mu</code>
 *   <li>EXPONENTIAL_MODE -- generates exponentially distributed random values with mean = <code>mu
 *       </code>
 *   <li>GAUSSIAN_MODE -- generates Gaussian distributed random values with mean = <code>mu</code>
 *       and standard deviation = <code>sigma</code>
 *   <li>CONSTANT_MODE -- returns <code>mu</code> every time.
 * </ul>
 */
public class ValueServer {

    /** Use empirical distribution. */
    public static final int DIGEST_MODE = 0;

    /** Replay data from valuesFilePath. */
    public static final int REPLAY_MODE = 1;

    /** Uniform random deviates with mean = &mu;. */
    public static final int UNIFORM_MODE = 2;

    /** Exponential random deviates with mean = &mu;. */
    public static final int EXPONENTIAL_MODE = 3;

    /** Gaussian random deviates with mean = &mu;, std dev = &sigma;. */
    public static final int GAUSSIAN_MODE = 4;

    /** Always return mu */
    public static final int CONSTANT_MODE = 5;

    /** mode determines how values are generated. */
    private int mode = 5;

    /** URI to raw data values. */
    private URL valuesFileURL = null;

    /** Mean for use with non-data-driven modes. */
    private double mu = 0.0;

    /** Standard deviation for use with GAUSSIAN_MODE. */
    private double sigma = 0.0;

    /** Empirical probability distribution for use with DIGEST_MODE. */
    private EmpiricalDistribution empiricalDistribution = null;

    /** File pointer for REPLAY_MODE. */
    private BufferedReader filePointer = null;

    /** RandomDataImpl to use for random data generation. */
    private final RandomDataGenerator randomData;

    // Data generation modes ======================================

    /** Creates new ValueServer */
    public ValueServer() {
        randomData = new RandomDataGenerator();
    }

    /**
     * Construct a ValueServer instance using a RandomDataImpl as its source of random data.
     *
     * @param randomData the RandomDataImpl instance used to source random data
     * @since 3.0
     * @deprecated use {@link #ValueServer(RandomGenerator)}
     */
    @Deprecated
    public ValueServer(RandomDataImpl randomData) {
        this.randomData = randomData.getDelegate();
    }

    /**
     * Construct a ValueServer instance using a RandomGenerator as its source of random data.
     *
     * @since 3.1
     * @param generator source of random data
     */
    public ValueServer(RandomGenerator generator) {
        this.randomData = new RandomDataGenerator(generator);
    }

    /**
     * Returns the next generated value, generated according to the mode value (see MODE constants).
     *
     * @return generated value
     * @throws IOException in REPLAY_MODE if a file I/O error occurs
     * @throws MathIllegalStateException if mode is not recognized
     * @throws MathIllegalArgumentException if the underlying random generator thwrows one
     */
    public double getNext()
            throws IOException, MathIllegalStateException, MathIllegalArgumentException {
        switch (mode) {
            case DIGEST_MODE:
                return getNextDigest();
            case REPLAY_MODE:
                return getNextReplay();
            case UNIFORM_MODE:
                return getNextUniform();
            case EXPONENTIAL_MODE:
                return getNextExponential();
            case GAUSSIAN_MODE:
                return getNextGaussian();
            case CONSTANT_MODE:
                return mu;
            default:
                throw new MathIllegalStateException(
                        LocalizedFormats.UNKNOWN_MODE,
                        mode,
                        "DIGEST_MODE",
                        DIGEST_MODE,
                        "REPLAY_MODE",
                        REPLAY_MODE,
                        "UNIFORM_MODE",
                        UNIFORM_MODE,
                        "EXPONENTIAL_MODE",
                        EXPONENTIAL_MODE,
                        "GAUSSIAN_MODE",
                        GAUSSIAN_MODE,
                        "CONSTANT_MODE",
                        CONSTANT_MODE);
        }
    }

    /**
     * Fills the input array with values generated using getNext() repeatedly.
     *
     * @param values array to be filled
     * @throws IOException in REPLAY_MODE if a file I/O error occurs
     * @throws MathIllegalStateException if mode is not recognized
     * @throws MathIllegalArgumentException if the underlying random generator thwrows one
     */
    public void fill(double[] values)
            throws IOException, MathIllegalStateException, MathIllegalArgumentException {
        for (int i = 0; i < values.length; i++) {
            values[i] = getNext();
        }
    }

    /**
     * Returns an array of length <code>length</code> with values generated using getNext()
     * repeatedly.
     *
     * @param length length of output array
     * @return array of generated values
     * @throws IOException in REPLAY_MODE if a file I/O error occurs
     * @throws MathIllegalStateException if mode is not recognized
     * @throws MathIllegalArgumentException if the underlying random generator thwrows one
     */
    public double[] fill(int length)
            throws IOException, MathIllegalStateException, MathIllegalArgumentException {
        double[] out = new double[length];
        for (int i = 0; i < length; i++) {
            out[i] = getNext();
        }
        return out;
    }

    /**
     * Computes the empirical distribution using values from the file in <code>valuesFileURL</code>,
     * using the default number of bins.
     *
     * <p><code>valuesFileURL</code> must exist and be readable by *this at runtime.
     *
     * <p>This method must be called before using <code>getNext()</code> with <code>
     * mode = DIGEST_MODE</code>
     *
     * @throws IOException if an I/O error occurs reading the input file
     * @throws NullArgumentException if the {@code valuesFileURL} has not been set
     * @throws ZeroException if URL contains no data
     */
    public void computeDistribution() throws IOException, ZeroException, NullArgumentException {
        computeDistribution(EmpiricalDistribution.DEFAULT_BIN_COUNT);
    }

    /**
     * Computes the empirical distribution using values from the file in <code>valuesFileURL</code>
     * and <code>binCount</code> bins.
     *
     * <p><code>valuesFileURL</code> must exist and be readable by this process at runtime.
     *
     * <p>This method must be called before using <code>getNext()</code> with <code>
     * mode = DIGEST_MODE</code>
     *
     * @param binCount the number of bins used in computing the empirical distribution
     * @throws NullArgumentException if the {@code valuesFileURL} has not been set
     * @throws IOException if an error occurs reading the input file
     * @throws ZeroException if URL contains no data
     */
    public void computeDistribution(int binCount)
            throws NullArgumentException, IOException, ZeroException {
        empiricalDistribution =
                new EmpiricalDistribution(binCount, randomData.getRandomGenerator());
        empiricalDistribution.load(valuesFileURL);
        mu = empiricalDistribution.getSampleStats().getMean();
        sigma = empiricalDistribution.getSampleStats().getStandardDeviation();
    }

    /**
     * Returns the data generation mode. See {@link ValueServer the class javadoc} for description
     * of the valid values of this property.
     *
     * @return Value of property mode.
     */
    public int getMode() {
        return mode;
    }

    /**
     * Sets the data generation mode.
     *
     * @param mode New value of the data generation mode.
     */
    public void setMode(int mode) {
        this.mode = mode;
    }

    /**
     * Returns the URL for the file used to build the empirical distribution when using {@link
     * #DIGEST_MODE}.
     *
     * @return Values file URL.
     */
    public URL getValuesFileURL() {
        return valuesFileURL;
    }

    /**
     * Sets the {@link #getValuesFileURL() values file URL} using a string URL representation.
     *
     * @param url String representation for new valuesFileURL.
     * @throws MalformedURLException if url is not well formed
     */
    public void setValuesFileURL(String url) throws MalformedURLException {
        this.valuesFileURL = new URL(url);
    }

    /**
     * Sets the the {@link #getValuesFileURL() values file URL}.
     *
     * <p>The values file <i>must</i> be an ASCII text file containing one valid numeric entry per
     * line.
     *
     * @param url URL of the values file.
     */
    public void setValuesFileURL(URL url) {
        this.valuesFileURL = url;
    }

    /**
     * Returns the {@link EmpiricalDistribution} used when operating in {@value #DIGEST_MODE}.
     *
     * @return EmpircalDistribution built by {@link #computeDistribution()}
     */
    public EmpiricalDistribution getEmpiricalDistribution() {
        return empiricalDistribution;
    }

    /**
     * Resets REPLAY_MODE file pointer to the beginning of the <code>valuesFileURL</code>.
     *
     * @throws IOException if an error occurs opening the file
     * @throws NullPointerException if the {@code valuesFileURL} has not been set.
     */
    public void resetReplayFile() throws IOException {
        if (filePointer != null) {
            try {
                filePointer.close();
                filePointer = null;
            } catch (IOException ex) { // NOPMD
                // ignore
            }
        }
        filePointer =
                new BufferedReader(new InputStreamReader(valuesFileURL.openStream(), "UTF-8"));
    }

    /**
     * Closes {@code valuesFileURL} after use in REPLAY_MODE.
     *
     * @throws IOException if an error occurs closing the file
     */
    public void closeReplayFile() throws IOException {
        if (filePointer != null) {
            filePointer.close();
            filePointer = null;
        }
    }

    /**
     * Returns the mean used when operating in {@link #GAUSSIAN_MODE}, {@link #EXPONENTIAL_MODE} or
     * {@link #UNIFORM_MODE}. When operating in {@link #CONSTANT_MODE}, this is the constant value
     * always returned. Calling {@link #computeDistribution()} sets this value to the overall mean
     * of the values in the {@link #getValuesFileURL() values file}.
     *
     * @return Mean used in data generation.
     */
    public double getMu() {
        return mu;
    }

    /**
     * Sets the {@link #getMu() mean} used in data generation. Note that calling this method after
     * {@link #computeDistribution()} has been called will have no effect on data generated in
     * {@link #DIGEST_MODE}.
     *
     * @param mu new Mean value.
     */
    public void setMu(double mu) {
        this.mu = mu;
    }

    /**
     * Returns the standard deviation used when operating in {@link #GAUSSIAN_MODE}. Calling {@link
     * #computeDistribution()} sets this value to the overall standard deviation of the values in
     * the {@link #getValuesFileURL() values file}. This property has no effect when the data
     * generation mode is not {@link #GAUSSIAN_MODE}.
     *
     * @return Standard deviation used when operating in {@link #GAUSSIAN_MODE}.
     */
    public double getSigma() {
        return sigma;
    }

    /**
     * Sets the {@link #getSigma() standard deviation} used in {@link #GAUSSIAN_MODE}.
     *
     * @param sigma New standard deviation.
     */
    public void setSigma(double sigma) {
        this.sigma = sigma;
    }

    /**
     * Reseeds the random data generator.
     *
     * @param seed Value with which to reseed the {@link RandomDataImpl} used to generate random
     *     data.
     */
    public void reSeed(long seed) {
        randomData.reSeed(seed);
    }

    // ------------- private methods ---------------------------------

    /**
     * Gets a random value in DIGEST_MODE.
     *
     * <p><strong>Preconditions</strong>:
     *
     * <ul>
     *   <li>Before this method is called, <code>computeDistribution()</code> must have completed
     *       successfully; otherwise an <code>IllegalStateException</code> will be thrown
     * </ul>
     *
     * @return next random value from the empirical distribution digest
     * @throws MathIllegalStateException if digest has not been initialized
     */
    private double getNextDigest() throws MathIllegalStateException {
        if ((empiricalDistribution == null) || (empiricalDistribution.getBinStats().size() == 0)) {
            throw new MathIllegalStateException(LocalizedFormats.DIGEST_NOT_INITIALIZED);
        }
        return empiricalDistribution.getNextValue();
    }

    /**
     * Gets next sequential value from the <code>valuesFileURL</code>.
     *
     * <p>Throws an IOException if the read fails.
     *
     * <p>This method will open the <code>valuesFileURL</code> if there is no replay file open.
     *
     * <p>The <code>valuesFileURL</code> will be closed and reopened to wrap around from EOF to BOF
     * if EOF is encountered. EOFException (which is a kind of IOException) may still be thrown if
     * the <code>valuesFileURL</code> is empty.
     *
     * @return next value from the replay file
     * @throws IOException if there is a problem reading from the file
     * @throws MathIllegalStateException if URL contains no data
     * @throws NumberFormatException if an invalid numeric string is encountered in the file
     */
    private double getNextReplay() throws IOException, MathIllegalStateException {
        String str = null;
        if (filePointer == null) {
            resetReplayFile();
        }
        if ((str = filePointer.readLine()) == null) {
            // we have probably reached end of file, wrap around from EOF to BOF
            closeReplayFile();
            resetReplayFile();
            if ((str = filePointer.readLine()) == null) {
                throw new MathIllegalStateException(
                        LocalizedFormats.URL_CONTAINS_NO_DATA, valuesFileURL);
            }
        }
        return Double.parseDouble(str);
    }

    /**
     * Gets a uniformly distributed random value with mean = mu.
     *
     * @return random uniform value
     * @throws MathIllegalArgumentException if the underlying random generator thwrows one
     */
    private double getNextUniform() throws MathIllegalArgumentException {
        return randomData.nextUniform(0, 2 * mu);
    }

    /**
     * Gets an exponentially distributed random value with mean = mu.
     *
     * @return random exponential value
     * @throws MathIllegalArgumentException if the underlying random generator thwrows one
     */
    private double getNextExponential() throws MathIllegalArgumentException {
        return randomData.nextExponential(mu);
    }

    /**
     * Gets a Gaussian distributed random value with mean = mu and standard deviation = sigma.
     *
     * @return random Gaussian value
     * @throws MathIllegalArgumentException if the underlying random generator thwrows one
     */
    private double getNextGaussian() throws MathIllegalArgumentException {
        return randomData.nextGaussian(mu, sigma);
    }
}