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Java example source code file (Logistic.java)

This example Java source code file (Logistic.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

deprecated, derivativestructure, differentiableunivariatefunction, dimensionmismatchexception, logistic, notstrictlypositiveexception, nullargumentexception, parametricunivariatefunction, univariatedifferentiablefunction

The Logistic.java Java example source code

/*
 * 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.analysis.function;

import org.apache.commons.math3.analysis.FunctionUtils;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.util.FastMath;

/**
 * <a href="http://en.wikipedia.org/wiki/Generalised_logistic_function">
 *  Generalised logistic</a> function.
 *
 * @since 3.0
 */
public class Logistic implements UnivariateDifferentiableFunction, DifferentiableUnivariateFunction {
    /** Lower asymptote. */
    private final double a;
    /** Upper asymptote. */
    private final double k;
    /** Growth rate. */
    private final double b;
    /** Parameter that affects near which asymptote maximum growth occurs. */
    private final double oneOverN;
    /** Parameter that affects the position of the curve along the ordinate axis. */
    private final double q;
    /** Abscissa of maximum growth. */
    private final double m;

    /**
     * @param k If {@code b > 0}, value of the function for x going towards +∞.
     * If {@code b < 0}, value of the function for x going towards -∞.
     * @param m Abscissa of maximum growth.
     * @param b Growth rate.
     * @param q Parameter that affects the position of the curve along the
     * ordinate axis.
     * @param a If {@code b > 0}, value of the function for x going towards -∞.
     * If {@code b < 0}, value of the function for x going towards +∞.
     * @param n Parameter that affects near which asymptote the maximum
     * growth occurs.
     * @throws NotStrictlyPositiveException if {@code n <= 0}.
     */
    public Logistic(double k,
                    double m,
                    double b,
                    double q,
                    double a,
                    double n)
        throws NotStrictlyPositiveException {
        if (n <= 0) {
            throw new NotStrictlyPositiveException(n);
        }

        this.k = k;
        this.m = m;
        this.b = b;
        this.q = q;
        this.a = a;
        oneOverN = 1 / n;
    }

    /** {@inheritDoc} */
    public double value(double x) {
        return value(m - x, k, b, q, a, oneOverN);
    }

    /** {@inheritDoc}
     * @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)}
     */
    @Deprecated
    public UnivariateFunction derivative() {
        return FunctionUtils.toDifferentiableUnivariateFunction(this).derivative();
    }

    /**
     * Parametric function where the input array contains the parameters of
     * the {@link Logistic#Logistic(double,double,double,double,double,double)
     * logistic function}, ordered as follows:
     * <ul>
     *  <li>k
     *  <li>m
     *  <li>b
     *  <li>q
     *  <li>a
     *  <li>n
     * </ul>
     */
    public static class Parametric implements ParametricUnivariateFunction {
        /**
         * Computes the value of the sigmoid at {@code x}.
         *
         * @param x Value for which the function must be computed.
         * @param param Values for {@code k}, {@code m}, {@code b}, {@code q},
         * {@code a} and  {@code n}.
         * @return the value of the function.
         * @throws NullArgumentException if {@code param} is {@code null}.
         * @throws DimensionMismatchException if the size of {@code param} is
         * not 6.
         * @throws NotStrictlyPositiveException if {@code param[5] <= 0}.
         */
        public double value(double x, double ... param)
            throws NullArgumentException,
                   DimensionMismatchException,
                   NotStrictlyPositiveException {
            validateParameters(param);
            return Logistic.value(param[1] - x, param[0],
                                  param[2], param[3],
                                  param[4], 1 / param[5]);
        }

        /**
         * Computes the value of the gradient at {@code x}.
         * The components of the gradient vector are the partial
         * derivatives of the function with respect to each of the
         * <em>parameters.
         *
         * @param x Value at which the gradient must be computed.
         * @param param Values for {@code k}, {@code m}, {@code b}, {@code q},
         * {@code a} and  {@code n}.
         * @return the gradient vector at {@code x}.
         * @throws NullArgumentException if {@code param} is {@code null}.
         * @throws DimensionMismatchException if the size of {@code param} is
         * not 6.
         * @throws NotStrictlyPositiveException if {@code param[5] <= 0}.
         */
        public double[] gradient(double x, double ... param)
            throws NullArgumentException,
                   DimensionMismatchException,
                   NotStrictlyPositiveException {
            validateParameters(param);

            final double b = param[2];
            final double q = param[3];

            final double mMinusX = param[1] - x;
            final double oneOverN = 1 / param[5];
            final double exp = FastMath.exp(b * mMinusX);
            final double qExp = q * exp;
            final double qExp1 = qExp + 1;
            final double factor1 = (param[0] - param[4]) * oneOverN / FastMath.pow(qExp1, oneOverN);
            final double factor2 = -factor1 / qExp1;

            // Components of the gradient.
            final double gk = Logistic.value(mMinusX, 1, b, q, 0, oneOverN);
            final double gm = factor2 * b * qExp;
            final double gb = factor2 * mMinusX * qExp;
            final double gq = factor2 * exp;
            final double ga = Logistic.value(mMinusX, 0, b, q, 1, oneOverN);
            final double gn = factor1 * FastMath.log(qExp1) * oneOverN;

            return new double[] { gk, gm, gb, gq, ga, gn };
        }

        /**
         * Validates parameters to ensure they are appropriate for the evaluation of
         * the {@link #value(double,double[])} and {@link #gradient(double,double[])}
         * methods.
         *
         * @param param Values for {@code k}, {@code m}, {@code b}, {@code q},
         * {@code a} and {@code n}.
         * @throws NullArgumentException if {@code param} is {@code null}.
         * @throws DimensionMismatchException if the size of {@code param} is
         * not 6.
         * @throws NotStrictlyPositiveException if {@code param[5] <= 0}.
         */
        private void validateParameters(double[] param)
            throws NullArgumentException,
                   DimensionMismatchException,
                   NotStrictlyPositiveException {
            if (param == null) {
                throw new NullArgumentException();
            }
            if (param.length != 6) {
                throw new DimensionMismatchException(param.length, 6);
            }
            if (param[5] <= 0) {
                throw new NotStrictlyPositiveException(param[5]);
            }
        }
    }

    /**
     * @param mMinusX {@code m - x}.
     * @param k {@code k}.
     * @param b {@code b}.
     * @param q {@code q}.
     * @param a {@code a}.
     * @param oneOverN {@code 1 / n}.
     * @return the value of the function.
     */
    private static double value(double mMinusX,
                                double k,
                                double b,
                                double q,
                                double a,
                                double oneOverN) {
        return a + (k - a) / FastMath.pow(1 + q * FastMath.exp(b * mMinusX), oneOverN);
    }

    /** {@inheritDoc}
     * @since 3.1
     */
    public DerivativeStructure value(final DerivativeStructure t) {
        return t.negate().add(m).multiply(b).exp().multiply(q).add(1).pow(oneOverN).reciprocal().multiply(k - a).add(a);
    }

}

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