/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  Redistributions in binary form must reproduce the above copyright notice,
 *      this list of conditions and the following disclaimer in the documentation
 *      and/or other materials provided with the distribution.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.ml.clustering.rforest;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.LinkedList;
import java.util.List;
import java.util.Random;
import java.util.Scanner;

/**
 * A tree of {@link RandomDecision} nodes used for constructing a string of bits which represent a cluster
 * point for a single data point
 * 
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 *
 */
public class RandomDecisionTree {
        List<RandomDecision> decisions;
        private Random random = new Random();
        /**
         * Construct a new RandomDecisionTree setting the number of decisions and the values needed
         * to choose a random index and min/max values for each feature vector index.
         * 
         * @param nDecisions
         * @param featureLength
         * @param minVal
         * @param maxVal
         */
        public RandomDecisionTree(int nDecisions, int featureLength, int[] minVal, int[] maxVal) {
                initDecisions(nDecisions,featureLength,minVal,maxVal);
        }
        
        /**
         * Construct a new RandomDecisionTree setting the number of decisions and the values needed
         * to choose a random index and min/max values for each feature vector index.
         * 
         * @param nDecisions
         * @param featureLength
         * @param minVal
         * @param maxVal
         * @param r
         */
        public RandomDecisionTree(int nDecisions, int featureLength, int[] minVal, int[] maxVal,Random r) {
                this.random = r;
                initDecisions(nDecisions,featureLength,minVal,maxVal);
        }
        
        private void initDecisions(int nDecisions, int featureLength, int[] minVal,int[] maxVal) {
                decisions = new LinkedList<RandomDecision>();
                for(int i = 0; i < nDecisions; i++){
                        RandomDecision dec = new RandomDecision(featureLength,minVal,maxVal,this.random);
                        decisions.add(dec);
                }
        }

        /**
         * A convenience function allowing the RandomDecisionTree to be written and read.
         */
        public RandomDecisionTree() {
                decisions = new LinkedList<RandomDecision>();
        }

        /**
         * The function which finds the path down this random tree for a given feature. Tests each
         * required feature vector index against the threshold and assigns booleans.
         * 
         * @param feature
         * @return return the letter as a string of bytes
         */
        public boolean[] getLetter(int[] feature){
                boolean[] out = new boolean[decisions.size()];
                int i = 0;
                for(RandomDecision r : decisions){
                        if(feature[r.feature] > r.threshold)
                                out[i] = true;
                        else
                                out[i] = false;
                        i++;
                }
                return out;
        }

        /**
         * Read/Write RandomDecisionTree (including decision nodes)
         * @param o
         * @throws IOException
         */
        public void write(DataOutput o) throws IOException {
                o.writeInt(this.decisions.size());
                for(RandomDecision r : this.decisions){
                        r.write(o);
                }
        }

        /**
         * Read/Write RandomDecisionTree (including decision nodes)
         * @param writer
         */
        public void writeASCII(PrintWriter writer) {
                for(RandomDecision r : this.decisions){
                        r.writeASCII(writer);
                        writer.print(" ");
                }
        }

        /**
         * Read/Write RandomDecisionTree (including decision nodes)
         * @param dis
         * @throws IOException
         * @return this
         */
        public RandomDecisionTree readBinary(DataInput dis) throws IOException {
                int nDecisions = dis.readInt();
                if(this.decisions.size() != nDecisions){
                        this.decisions = new LinkedList<RandomDecision>();
                        for(int i = 0 ; i < nDecisions; i ++){
                                RandomDecision r = new RandomDecision().readBinary(dis);
                                this.decisions.add(r);
                        }
                }
                else{
                        for(RandomDecision rd : this.decisions){
                                rd.readBinary(dis);
                        }
                }
                return this;
        }

        /**
         * Read/Write RandomDecisionTree (including decision nodes)
         * @param br
         * @throws IOException
         * @return this
         */
        public RandomDecisionTree readASCII(Scanner br) throws IOException {
                String[] lines = br.nextLine().split(" ");
                if(this.decisions.size() != lines.length){
                        this.decisions = new LinkedList<RandomDecision>();
                        for(String line : lines){
                                this.decisions.add(new RandomDecision().readString(line));
                        }
                }
                else{
                        int index = 0;
                        for(RandomDecision rd : this.decisions){
                                rd.readString(lines[index++]);
                        }
                }
                
                return this;
        }
        
        @Override
        public String toString(){
                String s = "{";
                for (RandomDecision r : this.decisions){
                        s += r.toString() + ",";
                }
                s+="}";
                return s;
        }
        
        @Override
        public boolean equals(Object o)
        {
                if(!(o instanceof RandomDecisionTree)) return false;
                RandomDecisionTree rdt = (RandomDecisionTree) o;
                for(int i = 0; i < this.decisions.size(); i++){
                        RandomDecision d1 = rdt.decisions.get(i);
                        RandomDecision d2 = this.decisions.get(i);
                        
                        if(d1.feature != d2.feature || d1.threshold != d2.threshold)
                                return false;
                }
                return true;
        }
}