/*
* 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.analysis.function.Logistic;
/**
* Decay function whose shape is similar to a sigmoid.
* <br/>
* Class is immutable.
*
* @since 3.3
*/
public class QuasiSigmoidDecayFunction {
/** Sigmoid. */
private final Logistic sigmoid;
/** See {@link #value(long)}. */
private final double scale;
/**
* Creates an instance.
* The function {@code f} will have the following properties:
* <ul>
* <li>{@code f(0) = initValue}</li>
* <li>{@code numCall} is the inflexion point</li>
* <li>{@code slope = f'(numCall)}</li>
* </ul>
*
* @param initValue Initial value, i.e. {@link #value(long) value(0)}.
* @param slope Value of the function derivative at {@code numCall}.
* @param numCall Inflexion point.
* @throws NotStrictlyPositiveException if {@code initValue <= 0}.
* @throws NumberIsTooLargeException if {@code slope >= 0}.
* @throws NotStrictlyPositiveException if {@code numCall <= 0}.
*/
public QuasiSigmoidDecayFunction(double initValue,
double slope,
long numCall) {
if (initValue <= 0) {
throw new NotStrictlyPositiveException(initValue);
}
if (slope >= 0) {
throw new NumberIsTooLargeException(slope, 0, false);
}
if (numCall <= 1) {
throw new NotStrictlyPositiveException(numCall);
}
final double k = initValue;
final double m = numCall;
final double b = 4 * slope / initValue;
final double q = 1;
final double a = 0;
final double n = 1;
sigmoid = new Logistic(k, m, b, q, a, n);
final double y0 = sigmoid.value(0);
scale = k / y0;
}
/**
* Computes the value of the learning factor.
*
* @param numCall Current step of the training task.
* @return the value of the function at {@code numCall}.
*/
public double value(long numCall) {
return scale * sigmoid.value(numCall);
}
}