/*
 *  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.preprocessing.normalization;

import java.util.HashMap;
import java.util.Iterator;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeRole;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.SimpleAttributes;
import com.rapidminer.example.table.ViewAttribute;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;
import com.rapidminer.tools.container.Tupel;


/** This model performs a z-Transformation on the given example set.
 * 
 *  @author Ingo Mierswa, Sebastian Land
 */
public class ZTransformationModel extends AbstractNormalizationModel {

    private static final long serialVersionUID = 7739929307307501706L;

    private HashMap<String, Tupel<Double, Double>> attributeMeanVarianceMap;

    public ZTransformationModel(ExampleSet exampleSet, HashMap<String, Tupel<Double, Double>> attributeMeanVarianceMap) {
        super(exampleSet);
        this.attributeMeanVarianceMap = attributeMeanVarianceMap;
    }

    @Override
    public Attributes getTargetAttributes(ExampleSet viewParent) {
        SimpleAttributes attributes = new SimpleAttributes();
        // add special attributes to new attributes
        Iterator<AttributeRole> roleIterator = viewParent.getAttributes().allAttributeRoles();
        while (roleIterator.hasNext()) {
            AttributeRole role = roleIterator.next();
            if (role.isSpecial()) {
                attributes.add(role);
            }
        }
        // add regular attributes
        for (Attribute attribute: viewParent.getAttributes()) {
            if (!attribute.isNumerical() || !attributeMeanVarianceMap.containsKey(attribute.getName())) {
                attributes.addRegular(attribute);
            } else {
                // giving new attributes old name: connection to rangesMap
                attributes.addRegular(new ViewAttribute(this, attribute, attribute.getName(), Ontology.NUMERICAL, null));
            }
        }
        return attributes;
    }

    @Override
    public double getValue(Attribute targetAttribute, double value) {
        Tupel<Double, Double> meanVarianceTupel = attributeMeanVarianceMap.get(targetAttribute.getName());
        if (meanVarianceTupel != null)
            if (meanVarianceTupel.getSecond().doubleValue() <= 0) {
                return(0);
            } else {
                return(value - meanVarianceTupel.getFirst().doubleValue()) / (Math.sqrt(meanVarianceTupel.getSecond().doubleValue()));
            }
        return value;
    }

    @Override
    public String getName() {
        return "Z-Transformation";
    }

    /** Returns a string representation of this model. */
    @Override
    public String toResultString() {
        StringBuffer result = new StringBuffer();
        result.append("Normalize " + attributeMeanVarianceMap.size() + " attributes to mean 0 and variance 1." + Tools.getLineSeparator() + "Using");
        int counter = 0;
        for(String name: attributeMeanVarianceMap.keySet()) {
            if (counter > 4) {
                result.append(Tools.getLineSeparator() + "... " + (attributeMeanVarianceMap.size() - 5) + " more attributes ...");
                break;
            }
            Tupel<Double, Double> meanVariance = attributeMeanVarianceMap.get(name);
            result.append(Tools.getLineSeparator() + name + " --> mean: " + meanVariance.getFirst().doubleValue() + ", variance: " + meanVariance.getSecond().doubleValue());
            counter++;
        }
        return result.toString();
    }
}