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Java example source code file (FeatureInitializerFactory.java)

This example Java source code file (FeatureInitializerFactory.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

constant, featureinitializer, featureinitializerfactory, randomgenerator, uniformrealdistribution, univariatefunction

The FeatureInitializerFactory.java Java example source code

/*
 * 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;

import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.function.Constant;
import org.apache.commons.math3.random.RandomGenerator;

/**
 * Creates functions that will select the initial values of a neuron's
 * features.
 *
 * @since 3.3
 */
public class FeatureInitializerFactory {
    /** Class contains only static methods. */
    private FeatureInitializerFactory() {}

    /**
     * Uniform sampling of the given range.
     *
     * @param min Lower bound of the range.
     * @param max Upper bound of the range.
     * @param rng Random number generator used to draw samples from a
     * uniform distribution.
     * @return an initializer such that the features will be initialized with
     * values within the given range.
     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
     * if {@code min >= max}.
     */
    public static FeatureInitializer uniform(final RandomGenerator rng,
                                             final double min,
                                             final double max) {
        return randomize(new UniformRealDistribution(rng, min, max),
                         function(new Constant(0), 0, 0));
    }

    /**
     * Uniform sampling of the given range.
     *
     * @param min Lower bound of the range.
     * @param max Upper bound of the range.
     * @return an initializer such that the features will be initialized with
     * values within the given range.
     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
     * if {@code min >= max}.
     */
    public static FeatureInitializer uniform(final double min,
                                             final double max) {
        return randomize(new UniformRealDistribution(min, max),
                         function(new Constant(0), 0, 0));
    }

    /**
     * Creates an initializer from a univariate function {@code f(x)}.
     * The argument {@code x} is set to {@code init} at the first call
     * and will be incremented at each call.
     *
     * @param f Function.
     * @param init Initial value.
     * @param inc Increment
     * @return the initializer.
     */
    public static FeatureInitializer function(final UnivariateFunction f,
                                              final double init,
                                              final double inc) {
        return new FeatureInitializer() {
            /** Argument. */
            private double arg = init;

            /** {@inheritDoc} */
            public double value() {
                final double result = f.value(arg);
                arg += inc;
                return result;
            }
        };
    }

    /**
     * Adds some amount of random data to the given initializer.
     *
     * @param random Random variable distribution.
     * @param orig Original initializer.
     * @return an initializer whose {@link FeatureInitializer#value() value}
     * method will return {@code orig.value() + random.sample()}.
     */
    public static FeatureInitializer randomize(final RealDistribution random,
                                               final FeatureInitializer orig) {
        return new FeatureInitializer() {
            /** {@inheritDoc} */
            public double value() {
                return orig.value() + random.sample();
            }
        };
    }
}

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