/*
 *  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.learner.functions;

import Jama.Matrix;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;


/**
 * The model for vector linear regression.
 * 
 * @author Tobias Malbrecht, Sebastian Land
 */
public class VectorRegressionModel extends PredictionModel {

	private static final long serialVersionUID = 8381268071090932037L;

	private String[] labelNames;
	
	private String[] attributeNames;

	Matrix coefficients;
	
	private boolean useIntercept = true;
	
	public VectorRegressionModel(ExampleSet exampleSet, String[] labelNames, Matrix coefficients, boolean useIntercept) {
		super(exampleSet);
		this.labelNames = labelNames;
		this.attributeNames = com.rapidminer.example.Tools.getRegularAttributeNames(exampleSet);
		this.coefficients = coefficients;
		this.useIntercept = useIntercept;
	}
	
	@Override
	public ExampleSet apply(ExampleSet exampleSet) {
		// creating labels
		Attribute[] predictedLabels = new Attribute[labelNames.length];
		for (int i = 0; i < labelNames.length; i++) {
			predictedLabels[i] = AttributeFactory.createAttribute("prediction(" + labelNames[i] + ")", Ontology.NUMERICAL);
			exampleSet.getExampleTable().addAttribute(predictedLabels[i]);
			exampleSet.getAttributes().addRegular(predictedLabels[i]);
			exampleSet.getAttributes().setSpecialAttribute(predictedLabels[i], "prediction_" + labelNames[i]);
		}

		// retrieving attributes
		Attributes attributes = exampleSet.getAttributes();
		Attribute[] usedAttributes = new Attribute[attributeNames.length];
		for (int i = 0; i < attributeNames.length; i++) {
			usedAttributes[i] = attributes.get(attributeNames[i]);
		}
		
		// now calculate predicted value
		for (Example example : exampleSet) {
			for (int i = 0; i < predictedLabels.length; i++) {
				double predictedLabel = useIntercept ? coefficients.get(0, i) : 0;
				if (useIntercept) {
					for (int j = 1; j <= attributeNames.length; j++)
						predictedLabel += example.getValue(usedAttributes[j - 1]) * coefficients.get(j, i);
				} else {
					for (int j = 0; j < attributeNames.length; j++)
						predictedLabel += example.getValue(usedAttributes[j]) * coefficients.get(j, i);
				}
				example.setValue(predictedLabels[i], predictedLabel);
			}
		}
		return exampleSet;
	}
	
	public String[] getLabelNames() {
		return labelNames;
	}
	
	public String[] getAttributeNames() {
		return attributeNames;
	}
	
	public double[] getCoefficients(String labelName) {
		double[] coefficients = new double[this.coefficients.getRowDimension()];
		int i = 0;
		for (String label : labelNames) {
			if (label.equals(labelName)) {
				for (int j = 0; j < coefficients.length - 1; j++) {
					coefficients[j] = this.coefficients.get(j + 1, i);
				}
				coefficients[coefficients.length - 1] = this.coefficients.get(0, i);
				return coefficients;
			}
			i++;
		}
		return null;
	}
	
	@Override
	public String toString() {
		StringBuffer result = new StringBuffer();

		for (int i = 0; i < labelNames.length; i++) {
			result.append(labelNames[i] + " = ");
			boolean first = true;
			for (int j = (useIntercept ? 1 : 0); j < attributeNames.length + (useIntercept ? 1 : 0); j++) {
				result.append(getCoefficientString(coefficients.get(j, i), first) + " * " + attributeNames[j - (useIntercept ? 1 : 0)] + "  ");
				first = false;
			}
			if (useIntercept) {
				result.append(getCoefficientString(coefficients.get(0, i), false));
			}
			result.append("\n");
		}
		
		return result.toString();
	}
	
	private String getCoefficientString(double coefficient, boolean first) {
		if (!first) {
			if (coefficient >= 0)
				return "+ " + Tools.formatNumber(Math.abs(coefficient));
			else
				return "- " + Tools.formatNumber(Math.abs(coefficient));
		} else {
			if (coefficient >= 0)
				return "  " + Tools.formatNumber(Math.abs(coefficient));
			else
				return "- " + Tools.formatNumber(Math.abs(coefficient));
		}
	}

	@Override
	/**
	 * This method won't be called at all, because we overwrite the calling method.
	 */
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		return null;
	}
}