/* * 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); } }