/*
 *  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 java.util.ArrayList;
import java.util.List;

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;

/**
 * This is the model of a SUR regression. It 
 * contains the used attributes, the names of the target 
 * labels and the coefficients. 
 * @author Sebastian Land
 *
 */
public class SeeminglyUnrelatedRegressionModel extends PredictionModel {

	private static final long serialVersionUID = 4843759046775802520L;

	private ArrayList<String[]> usedAttributeNames;
	private ArrayList<String> labelNames;
	private double[] coefficients; 
	
	protected SeeminglyUnrelatedRegressionModel(ExampleSet trainingExampleSet, ArrayList<String[]> usedAttributeNames, ArrayList<String> labelNames, double[] coefficients) {
		super(trainingExampleSet);
		this.usedAttributeNames = usedAttributeNames;
		this.labelNames = labelNames;
		this.coefficients = coefficients;
	}

	@Override
	public ExampleSet apply(ExampleSet exampleSet) {
		// creating labels
		Attribute[] predictedLabels = new Attribute[labelNames.size()];
		for (int i = 0; i < labelNames.size(); i++) {
			String labelName = labelNames.get(i);
			predictedLabels[i] = AttributeFactory.createAttribute("prediction(" + labelName + ")", Ontology.REAL);
			exampleSet.getExampleTable().addAttribute(predictedLabels[i]);
			exampleSet.getAttributes().addRegular(predictedLabels[i]);
			exampleSet.getAttributes().setSpecialAttribute(predictedLabels[i], "prediction_" + labelName);
		}

		// retrieving used attributes
		Attribute[][] usedAttributes = new Attribute[usedAttributeNames.size()][];
		Attributes attributes = exampleSet.getAttributes();
		for (int i = 0; i < usedAttributeNames.size(); i++) {
			String[] currentAttributeNames = usedAttributeNames.get(i);
			Attribute[] regressionAttributes = new Attribute[currentAttributeNames.length];
			for (int j = 0; j < currentAttributeNames.length; j++) {
				regressionAttributes[j] = attributes.get(currentAttributeNames[j]);
			}
			usedAttributes[i] = regressionAttributes;
		}
		
		// perform prediction
		for (Example example : exampleSet) {
			int coefficientOffset = 0;
			for (int i = 0; i < predictedLabels.length; i++) {
				// adding bias
				double predictedValue = coefficients[coefficientOffset];  
				coefficientOffset++;
				
				// calculating regression
				for (int j = 0; j < usedAttributes[i].length; j++) {
					predictedValue += example.getValue(usedAttributes[i][j]) * coefficients[coefficientOffset + j];
				}
				coefficientOffset += usedAttributes[i].length;
				example.setValue(predictedLabels[i], predictedValue);
			}
		}
		return exampleSet;
	}
	
	@Override
	/**
	 * This method isn't called at all, since we have overridden the calling method.
	 */
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		return null;
	}
	
	public double[] getCoefficients(String labelName) {
		int offset = 0;
		int length = 0;
		int i = 0;
		for (String label : labelNames) {
			String[] selectedAttributes = usedAttributeNames.get(i);
			length = selectedAttributes.length;
			if (label.equals(labelName)) {
				break;
			}
			offset += 1 + length;
			i++;
		}
		if (offset < this.coefficients.length) {
			double[] coefficients = new double[length + 1];
			for (int j = 0; j < coefficients.length - 1; j++) {
				coefficients[j] = this.coefficients[offset + j + 1];
			}
			coefficients[coefficients.length - 1] = this.coefficients[offset];
			return coefficients;
		}
		return null;
	}
	
	public String[] getSelectedAttributeNames(String labelName) {
		int i = 0;
		for (String label : labelNames) {
			if (label.equals(labelName)) {
				return usedAttributeNames.get(i);
			}
			i++;
		}
		return null;
	}
	
	public List<String> getLabelNames() {
		return labelNames;
	}

	
	@Override
	public String toString() {
		StringBuffer result = new StringBuffer();
		int j = 0;
		int coeffIndex = 0;
		for (String labelName: labelNames) {
			result.append(Tools.getLineSeparators(2) + labelName + Tools.getLineSeparators(2));
			String[] selectedAttributes = usedAttributeNames.get(j); 
			
			//bias
			result.append(getCoefficientString(coefficients[coeffIndex], true)+ Tools.getLineSeparator());
			coeffIndex++;
			
			//coefficients
			for (int i = 0; i < selectedAttributes.length; i++) {
					result.append(getCoefficientString(coefficients[coeffIndex], false) + " * " + selectedAttributes[i] + Tools.getLineSeparator());
					coeffIndex++;
			}
			j++;
		}
		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));
		}
	}
}