/*
* 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.math4.analysis.function;
import org.apache.commons.math4.analysis.UnivariateFunction;
import org.apache.commons.math4.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math4.analysis.function.Logistic;
import org.apache.commons.math4.analysis.function.Sigmoid;
import org.apache.commons.math4.exception.DimensionMismatchException;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.exception.NullArgumentException;
import org.apache.commons.math4.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);
}
}