001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.ml.clustering.rforest;
031
032import java.io.DataInput;
033import java.io.DataOutput;
034import java.io.IOException;
035import java.io.PrintWriter;
036import java.util.Random;
037
038/**
039 * A single decision node of a RandomForest tree. This decision holds the feature index and the
040 * threshold for that index.  
041 * 
042 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
043 *
044 */
045public class RandomDecision {
046
047        /**
048         * Feature threshold
049         */
050        public int threshold;
051        /**
052         * Feature index
053         */
054        public int feature;
055        private int randomSeed = -1;
056        private Random random = new Random();
057
058        /**
059         * @param featureLength The number of entries in this featurevector
060         * @param minVal the min values of each featurevector entry
061         * @param maxVal the max values of each featurevector entry
062         */
063        public RandomDecision(int featureLength, int[] minVal, int[] maxVal) {
064                setFeatureDecision(featureLength,minVal,maxVal);
065        }
066        
067        private void setFeatureDecision(int featureLength, int[] minVal,int[] maxVal) {
068                this.feature = this.random.nextInt(featureLength);
069                if(maxVal[this.feature]-minVal[this.feature] == 0)
070                        this.threshold = minVal[this.feature];
071                else
072                        this.threshold = this.random.nextInt(maxVal[this.feature]-minVal[this.feature]) + minVal[this.feature];
073        }
074
075        /**
076         * @param featureLength The number of entries in this featurevector
077         * @param minVal the min values of each featurevector entry
078         * @param maxVal the max values of each featurevector entry
079         * @param r random seed to set before construction
080         */
081        public RandomDecision(int featureLength, int[] minVal, int[] maxVal, Random r) {
082                this.random = r;
083                setFeatureDecision(featureLength,minVal, maxVal);
084        }
085
086        /**
087         * Emtpy contructor provided to allow reading of the decision
088         */
089        public RandomDecision() {
090        }
091
092        
093        
094        /**
095         * Write decision to a binary stream, threshold followed by feature.
096         * @param o
097         * @throws IOException
098         */
099        public void write(DataOutput o) throws IOException {
100                o.writeInt(threshold);
101                o.writeInt(feature);
102        }
103
104        /**
105         * write decision in a human readable form
106         * @param writer
107         */
108        public void writeASCII(PrintWriter writer) {
109                writer.print(threshold + "," + feature);
110        }
111
112        /**
113         * Read decision
114         * @param dis
115         * @return A decision
116         * @throws IOException
117         */
118        public RandomDecision readBinary(DataInput dis) throws IOException {
119                threshold = dis.readInt();
120                feature = dis.readInt();
121                return this;
122        }
123
124        /**
125         * Read decision from a string
126         * @param line
127         * @return a decision
128         */
129        public RandomDecision readString(String line) {
130                String[] bits = line.split(",");
131                threshold = Integer.parseInt(bits[0]);
132                feature = Integer.parseInt(bits[1]);
133                return this;
134        }
135        
136        @Override
137        public String toString()
138        {
139                String s = "(" + this.feature + "," + this.threshold + ")";
140                return s;
141        }
142
143        /**
144         * Random seed upon which a java {@link Random} object is seeded and used to choose
145         * random indecies and thresholds.
146         * @param randomSeed
147         */
148        public void setRandomSeed(int randomSeed) {
149                this.randomSeed = randomSeed;
150                this.random = new Random(this.randomSeed);
151        }
152}