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Java example source code file (LogisticTest.java)

This example Java source code file (LogisticTest.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

derivativestructure, eps, logistic, logistictest, sigmoid, test, univariatefunction

The LogisticTest.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.UnivariateFunction;
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
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;

import org.junit.Assert;
import org.junit.Test;

/**
 * Test for class {@link Logistic}.
 */
public class LogisticTest {
    private final double EPS = Math.ulp(1d);

    @Test(expected=NotStrictlyPositiveException.class)
    public void testPreconditions1() {
        new Logistic(1, 0, 1, 1, 0, -1);
    }

    @Test(expected=NotStrictlyPositiveException.class)
    public void testPreconditions2() {
        new Logistic(1, 0, 1, 1, 0, 0);
    }

    @Test
    public void testCompareSigmoid() {
        final UnivariateFunction sig = new Sigmoid();
        final UnivariateFunction sigL = new Logistic(1, 0, 1, 1, 0, 1);

        final double min = -2;
        final double max = 2;
        final int n = 100;
        final double delta = (max - min) / n;
        for (int i = 0; i < n; i++) {
            final double x = min + i * delta;
            Assert.assertEquals("x=" + x, sig.value(x), sigL.value(x), EPS);
        }
    }

    @Test
    public void testSomeValues() {
        final double k = 4;
        final double m = 5;
        final double b = 2;
        final double q = 3;
        final double a = -1;
        final double n = 2;

        final UnivariateFunction f = new Logistic(k, m, b, q, a, n);

        double x;
        x = m;
        Assert.assertEquals("x=" + x, a + (k - a) / FastMath.sqrt(1 + q), f.value(x), EPS);

        x = Double.NEGATIVE_INFINITY;
        Assert.assertEquals("x=" + x, a, f.value(x), EPS);

        x = Double.POSITIVE_INFINITY;
        Assert.assertEquals("x=" + x, k, f.value(x), EPS);
    }

    @Test
    public void testCompareDerivativeSigmoid() {
        final double k = 3;
        final double a = 2;

        final Logistic f = new Logistic(k, 0, 1, 1, a, 1);
        final Sigmoid g = new Sigmoid(a, k);

        final double min = -10;
        final double max = 10;
        final double n = 20;
        final double delta = (max - min) / n;
        for (int i = 0; i < n; i++) {
            final DerivativeStructure x = new DerivativeStructure(1, 5, 0, min + i * delta);
            for (int order = 0; order <= x.getOrder(); ++order) {
                Assert.assertEquals("x=" + x.getValue(),
                                    g.value(x).getPartialDerivative(order),
                                    f.value(x).getPartialDerivative(order),
                                    3.0e-15);
            }
        }
    }

    @Test(expected=NullArgumentException.class)
    public void testParametricUsage1() {
        final Logistic.Parametric g = new Logistic.Parametric();
        g.value(0, null);
    }

    @Test(expected=DimensionMismatchException.class)
    public void testParametricUsage2() {
        final Logistic.Parametric g = new Logistic.Parametric();
        g.value(0, new double[] {0});
    }

    @Test(expected=NullArgumentException.class)
    public void testParametricUsage3() {
        final Logistic.Parametric g = new Logistic.Parametric();
        g.gradient(0, null);
    }

    @Test(expected=DimensionMismatchException.class)
    public void testParametricUsage4() {
        final Logistic.Parametric g = new Logistic.Parametric();
        g.gradient(0, new double[] {0});
    }

    @Test(expected=NotStrictlyPositiveException.class)
    public void testParametricUsage5() {
        final Logistic.Parametric g = new Logistic.Parametric();
        g.value(0, new double[] {1, 0, 1, 1, 0 ,0});
    }

    @Test(expected=NotStrictlyPositiveException.class)
    public void testParametricUsage6() {
        final Logistic.Parametric g = new Logistic.Parametric();
        g.gradient(0, new double[] {1, 0, 1, 1, 0 ,0});
    }

    @Test
    public void testGradientComponent0Component4() {
        final double k = 3;
        final double a = 2;

        final Logistic.Parametric f = new Logistic.Parametric();
        // Compare using the "Sigmoid" function.
        final Sigmoid.Parametric g = new Sigmoid.Parametric();

        final double x = 0.12345;
        final double[] gf = f.gradient(x, new double[] {k, 0, 1, 1, a, 1});
        final double[] gg = g.gradient(x, new double[] {a, k});

        Assert.assertEquals(gg[0], gf[4], EPS);
        Assert.assertEquals(gg[1], gf[0], EPS);
    }

    @Test
    public void testGradientComponent5() {
        final double m = 1.2;
        final double k = 3.4;
        final double a = 2.3;
        final double q = 0.567;
        final double b = -FastMath.log(q);
        final double n = 3.4;

        final Logistic.Parametric f = new Logistic.Parametric();

        final double x = m - 1;
        final double qExp1 = 2;

        final double[] gf = f.gradient(x, new double[] {k, m, b, q, a, n});

        Assert.assertEquals((k - a) * FastMath.log(qExp1) / (n * n * FastMath.pow(qExp1, 1 / n)),
                            gf[5], EPS);
    }

    @Test
    public void testGradientComponent1Component2Component3() {
        final double m = 1.2;
        final double k = 3.4;
        final double a = 2.3;
        final double b = 0.567;
        final double q = 1 / FastMath.exp(b * m);
        final double n = 3.4;

        final Logistic.Parametric f = new Logistic.Parametric();

        final double x = 0;
        final double qExp1 = 2;

        final double[] gf = f.gradient(x, new double[] {k, m, b, q, a, n});

        final double factor = (a - k) / (n * FastMath.pow(qExp1, 1 / n + 1));
        Assert.assertEquals(factor * b, gf[1], EPS);
        Assert.assertEquals(factor * m, gf[2], EPS);
        Assert.assertEquals(factor / q, gf[3], EPS);
    }
}

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