/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.features.weighting;

import java.util.List;

import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorCreationException;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.features.transformation.PCA;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.OperatorService;

/**
 * Uses the factors of one of the principal components (default is the first) as
 * feature weights. Please note that the PCA weighting operator is currently the only one
 * which also works on data sets without a label, i.e. for unsupervised learning.
 * 
 * @author Ingo Mierswa
 *
 */
public class PCAWeighting extends AbstractWeighting {

    public PCAWeighting(OperatorDescription description) {
        super(description);
    }

    @Override
    protected AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {
        PCA pcaOperator = null;
        try {
            pcaOperator = OperatorService.createOperator(PCA.class);
        } catch (OperatorCreationException e) {
            throw new UserError(this, 904, "inner pca operator", e.getMessage());
        }
        pcaOperator.setParameter(PCA.PARAMETER_REDUCTION_TYPE, PCA.REDUCTION_NONE + "");

        ComponentWeights weightOperator = null;
        try {
            weightOperator = OperatorService.createOperator(ComponentWeights.class);
        } catch (OperatorCreationException e) {
            throw new UserError(this, 904, "inner weight operator", e.getMessage());
        }
        weightOperator.setParameter(ComponentWeights.PARAMETER_COMPONENT_NUMBER, getParameterAsInt(ComponentWeights.PARAMETER_COMPONENT_NUMBER) + "");
        weightOperator.setParameter(ComponentWeights.PARAMETER_NORMALIZE_WEIGHTS, false + "");
        weightOperator.setParameter(ComponentWeights.PARAMETER_SORT_WEIGHTS, false + "");

        Model pcaModel = pcaOperator.doWork(exampleSet);
        AttributeWeights result = weightOperator.doWork(pcaModel, exampleSet);

        result.setSource(this.getName());
        return result;
    }

    @Override
    public List<ParameterType> getParameterTypes() {
        List<ParameterType> types = super.getParameterTypes();
        types.add(new ParameterTypeInt(ComponentWeights.PARAMETER_COMPONENT_NUMBER,
                "Indicates the number of the component from which the weights should be calculated.",
                1, Integer.MAX_VALUE, 1));
        return types;
    }

    @Override
    public boolean supportsCapability(OperatorCapability capability) {
        switch (capability) {
        case NUMERICAL_ATTRIBUTES:
        case NO_LABEL:
            return true;
        default:
            return false;
        }
    }
}