001/*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements.  See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License.  You may obtain a copy of the License at
008 *
009 *      http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017
018package org.apache.commons.math3.ml.neuralnet;
019
020import org.apache.commons.math3.distribution.RealDistribution;
021import org.apache.commons.math3.distribution.UniformRealDistribution;
022import org.apache.commons.math3.analysis.UnivariateFunction;
023import org.apache.commons.math3.analysis.function.Constant;
024import org.apache.commons.math3.random.RandomGenerator;
025
026/**
027 * Creates functions that will select the initial values of a neuron's
028 * features.
029 *
030 * @since 3.3
031 */
032public class FeatureInitializerFactory {
033    /** Class contains only static methods. */
034    private FeatureInitializerFactory() {}
035
036    /**
037     * Uniform sampling of the given range.
038     *
039     * @param min Lower bound of the range.
040     * @param max Upper bound of the range.
041     * @param rng Random number generator used to draw samples from a
042     * uniform distribution.
043     * @return an initializer such that the features will be initialized with
044     * values within the given range.
045     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
046     * if {@code min >= max}.
047     */
048    public static FeatureInitializer uniform(final RandomGenerator rng,
049                                             final double min,
050                                             final double max) {
051        return randomize(new UniformRealDistribution(rng, min, max),
052                         function(new Constant(0), 0, 0));
053    }
054
055    /**
056     * Uniform sampling of the given range.
057     *
058     * @param min Lower bound of the range.
059     * @param max Upper bound of the range.
060     * @return an initializer such that the features will be initialized with
061     * values within the given range.
062     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
063     * if {@code min >= max}.
064     */
065    public static FeatureInitializer uniform(final double min,
066                                             final double max) {
067        return randomize(new UniformRealDistribution(min, max),
068                         function(new Constant(0), 0, 0));
069    }
070
071    /**
072     * Creates an initializer from a univariate function {@code f(x)}.
073     * The argument {@code x} is set to {@code init} at the first call
074     * and will be incremented at each call.
075     *
076     * @param f Function.
077     * @param init Initial value.
078     * @param inc Increment
079     * @return the initializer.
080     */
081    public static FeatureInitializer function(final UnivariateFunction f,
082                                              final double init,
083                                              final double inc) {
084        return new FeatureInitializer() {
085            /** Argument. */
086            private double arg = init;
087
088            /** {@inheritDoc} */
089            public double value() {
090                final double result = f.value(arg);
091                arg += inc;
092                return result;
093            }
094        };
095    }
096
097    /**
098     * Adds some amount of random data to the given initializer.
099     *
100     * @param random Random variable distribution.
101     * @param orig Original initializer.
102     * @return an initializer whose {@link FeatureInitializer#value() value}
103     * method will return {@code orig.value() + random.sample()}.
104     */
105    public static FeatureInitializer randomize(final RealDistribution random,
106                                               final FeatureInitializer orig) {
107        return new FeatureInitializer() {
108            /** {@inheritDoc} */
109            public double value() {
110                return orig.value() + random.sample();
111            }
112        };
113    }
114}