/*
 * Encog(tm) Core v3.2 - Java Version
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-core

 * Copyright 2008-2013 Heaton Research, Inc.
 *
 * 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 org.encog.ensemble;

import org.encog.ensemble.data.EnsembleDataSet;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLData;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.util.EngineArray;

public class GenericEnsembleML implements EnsembleML {

    private EnsembleDataSet trainingSet;
    private BasicNetwork ml;
    private MLTrain trainer;
    private String label;

    public GenericEnsembleML(MLMethod fromML, String description) {
        setMl(fromML);
        this.label = description;
    }

    @Override
    public void setTrainingSet(EnsembleDataSet dataSet) {
        this.trainingSet = dataSet;
    }

    @Override
    public EnsembleDataSet getTrainingSet() {
        return trainingSet;
    }

    @Override
    public void train(double targetError, boolean verbose) {
        double error = 0;
        double previouserror = 0;
        double errordelta = 0;
        int iteration = 0;
        do {
            trainer.iteration();
            iteration++;
            if (iteration > 1) {
                previouserror = error;
            }
            error = trainer.getError();
            if (iteration > 1) {
                errordelta = previouserror - error;
            }
            if (verbose) {
                System.out.println(iteration + " " + error);
            }
        } while ((error > targetError) &&
                trainer.canContinue() &&
                errordelta > -0.1 &&
                //make this a parameter
                iteration < 2000);
        trainer.finishTraining();
    }

    @Override
    public void setMl(MLMethod newMl) {
        ml = (BasicNetwork) newMl;
    }

    @Override
    public MLMethod getMl() {
        return ml;
    }

    @Override
    public int classify(MLData input) {
        return ml.classify(input);
    }

    @Override
    public MLData compute(MLData input) {
        return ml.compute(input);
    }

    @Override
    public int getInputCount() {
        return ml.getInputCount();
    }

    @Override
    public int getOutputCount() {
        return ml.getOutputCount();
    }

    @Override
    public void train(double targetError) {
        train(targetError, false);
    }

    public int winner(MLData output) {
        return EngineArray.maxIndex(output.getData());
    }

    @Override
    public void setTraining(MLTrain train) {
        trainer = train;
    }

    @Override
    public MLTrain getTraining() {
        return trainer;
    }

    @Override
    public void trainStep() {
        trainer.iteration();
    }

    @Override
    public String getLabel() {
        return label;
    }

    @Override
    public double getError(EnsembleDataSet testset) {
        return ml.calculateError(testset);
    }
}
