/*
 *  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.meta;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map.Entry;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;


/**
 */
@Deprecated
public class HierarchicalModel extends PredictionModel implements MetaModel {

	public static class Node implements Serializable {
	
		private static final long serialVersionUID = 1L;

		private final String className;
		
		private final List<Node> children = new ArrayList<Node>();
		
		private Node parent = null;
		
		private Model model = null;
		
		public Node(String className) {
			this.className = className;
		}
		
		public List<Node> getChildren() {
			return children;
		}
		
		public void addChild(Node child) {
			children.add(child);
			child.setParent(this);
		}
		
		public void setParent(Node parent) {
			this.parent = parent;
		}
		
		public Node getParent() {
			return this.parent;
		}
		
		public String getClassName() {
			return this.className;
		}
		
		public List<String> getChildrenClasses() {
			List<String> childrenClasses = new ArrayList<String>();
			for (Node child : children) {
				childrenClasses.add(child.getClassName());
				childrenClasses.addAll(child.getChildrenClasses());
			}
			return childrenClasses;
		}
		
		public List<String> getLeaveClasses() {
			List<String> leaveClasses = new ArrayList<String>();
			for (Node child : children) {
				leaveClasses.addAll(child.getLeaveClasses());
			}
			if (children.size() == 0) {
				leaveClasses.add(className);
			}
			return leaveClasses;
		}
		
		public void setModel(Model model) {
			this.model = model;
		}
		
		public Model getModel() {
			return this.model;
		}
		
	}

	private static final long serialVersionUID = -5792943818860734082L;
	
	private final Node root;

	public HierarchicalModel(ExampleSet exampleSet, Node root) {
		super(exampleSet);
		this.root = root;
	}

	@Override
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		ExampleSet eSet = (ExampleSet) exampleSet.clone();
		int numberOfClasses = getLabel().getMapping().getValues().size();

		HashMap<String, Integer>  classIndexMap = new HashMap<String, Integer>(numberOfClasses);		
		for (String currentClass : getLabel().getMapping().getValues()) {
			classIndexMap.put(currentClass, getLabel().getMapping().mapString(currentClass));							
		}			

		double[][] confidenceMatrix = new double[eSet.size()][numberOfClasses];
		for (int i = 0; i < confidenceMatrix.length; i++) {
			for (int j = 0; j < confidenceMatrix[i].length; j++) {
				confidenceMatrix[i][j] = 1;
			}
		}
		performPrediction(eSet, predictedLabel, root, confidenceMatrix, classIndexMap);
		
		int counter = 0;
		for (Example example : exampleSet) {
			double predictedValue = 0;
			double maxConfidence = 0;
//			double sumConfidence = 0;
//			for (int i = 0; i < confidenceMatrix[counter].length; i++) {
//				sumConfidence += confidenceMatrix[counter][i];
//			}
			for (Entry<String, Integer> entry : classIndexMap.entrySet()) {
//				confidenceMatrix[counter][entry.getValue()] /= sumConfidence;
				example.setConfidence(entry.getKey(), confidenceMatrix[counter][entry.getValue()]);
				if (confidenceMatrix[counter][entry.getValue()] > maxConfidence) {
					maxConfidence = confidenceMatrix[counter][entry.getValue()];
					predictedValue = entry.getValue();
				}
			}
			example.setPredictedLabel(predictedValue);
			counter++;
		}
		return exampleSet;
	}
	
	public void performPrediction(ExampleSet eSet, Attribute predictedLabel, Node node, double[][] confidenceMatrix, HashMap<String, Integer> classIndexMap) throws OperatorException {
		if (node.getModel() != null && node.getChildren().size() > 0) {
			System.err.println("Predicting " + node.getClassName());
			eSet = node.getModel().apply(eSet);

			int counter = 0;
			for (Example example : eSet) {
				for (Node child : node.getChildren()) {
					double confidence = example.getConfidence(child.getClassName());
					for (String className : child.getLeaveClasses()) {
						confidenceMatrix[counter][classIndexMap.get(className)] *= confidence;
					}
				}
				counter++;
			}
			PredictionModel.removePredictedLabel(eSet);
		}
		for (Node child : node.getChildren()) {
			performPrediction(eSet, predictedLabel, child, confidenceMatrix, classIndexMap);
		}
	}

	@Override
	public List<String> getModelNames() {
		List<String> names = new LinkedList<String>();
		return names;
	}

	@Override
	public List<Model> getModels() {
		return Arrays.asList();
	}
}