Package com.heatonresearch.aifh.examples.learning

Source Code of com.heatonresearch.aifh.examples.learning.LearnPolynomial

/*
* Artificial Intelligence for Humans
* Volume 1: Fundamental Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh

* Copyright 2013 by Jeff Heaton
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/

package com.heatonresearch.aifh.examples.learning;

import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.learning.TrainGreedyRandom;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
import com.heatonresearch.aifh.learning.score.ScoreRegressionData;

import java.util.ArrayList;
import java.util.List;

/**
* Learn a simple polynomial with the Greedy Random algorithm.
*/
public class LearnPolynomial extends SimpleLearn {

    public List<BasicData> generateTrainingData() {
        final List<BasicData> result = new ArrayList<BasicData>();

        for (double x = -50; x < 50; x++) {
            final double y = (2 * Math.pow(x, 2)) + (4 * x) + 6;
            final BasicData pair = new BasicData(1, 1);
            pair.getInput()[0] = x;
            pair.getIdeal()[0] = y;
            result.add(pair);
        }

        return result;
    }


    /**
     * Run the example.
     */
    public void process() {
        final List<BasicData> trainingData = generateTrainingData();
        final PolynomialFn poly = new PolynomialFn(3);
        final ScoreFunction score = new ScoreRegressionData(trainingData);
        final TrainGreedyRandom train = new TrainGreedyRandom(true, poly, score);
        performIterations(train, 1000000, 0.01, true);
        System.out.println(poly.toString());
    }

    /**
     * The main method.
     *
     * @param args Not used.
     */
    public static void main(final String[] args) {
        final LearnPolynomial prg = new LearnPolynomial();
        prg.process();
    }
}
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