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/*
* 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.ml.neuralnet.sofm.util;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.exception.NumberIsTooLargeException;
import org.junit.Test;
import org.junit.Assert;
/**
* Tests for {@link ExponentialDecayFunction} class
*/
public class ExponentialDecayFunctionTest {
@Test(expected=NotStrictlyPositiveException.class)
public void testPrecondition1() {
new ExponentialDecayFunction(0d, 0d, 2);
}
@Test(expected=NotStrictlyPositiveException.class)
public void testPrecondition2() {
new ExponentialDecayFunction(1d, 0d, 2);
}
@Test(expected=NumberIsTooLargeException.class)
public void testPrecondition3() {
new ExponentialDecayFunction(1d, 1d, 100);
}
@Test(expected=NotStrictlyPositiveException.class)
public void testPrecondition4() {
new ExponentialDecayFunction(1d, 0.2, 0);
}
@Test
public void testTrivial() {
final int n = 65;
final double init = 4;
final double valueAtN = 3;
final ExponentialDecayFunction f = new ExponentialDecayFunction(init, valueAtN, n);
Assert.assertEquals(init, f.value(0), 0d);
Assert.assertEquals(valueAtN, f.value(n), 0d);
Assert.assertEquals(0, f.value(Long.MAX_VALUE), 0d);
}
}