/*
* 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.ml.neuralnet.sofm.util;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.util.FastMath;
/**
* Exponential decay function: a e-x / b
,
* where {@code x} is the (integer) independent variable.
*
* Class is immutable.
*
* @since 3.3
*/
public class ExponentialDecayFunction {
/** Factor {@code a}. */
private final double a;
/** Factor {@code 1 / b}. */
private final double oneOverB;
/**
* Creates an instance. It will be such that
*
a e-numCall / b
.
*
* @param numCall Current step of the training task.
* @return the value of the function at {@code numCall}.
*/
public double value(long numCall) {
return a * FastMath.exp(-numCall * oneOverB);
}
}