/**
 * 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.math.matrix.similarity;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.Arrays;
import java.util.Scanner;

import org.jgrapht.UndirectedGraph;
import org.jgrapht.graph.DefaultEdge;
import org.jgrapht.graph.SimpleGraph;
import org.openimaj.io.ReadWriteable;
import org.openimaj.math.matrix.ReadWriteableMatrix;
import org.openimaj.math.matrix.similarity.processor.SimilarityMatrixProcessor;

import Jama.Matrix;

/**
 * A similarity matrix is a square matrix with an associated index.
 * It can be used to store all the similarities across a set
 * of objects.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 */
public class SimilarityMatrix extends ReadWriteableMatrix implements ReadWriteable {
        private static final long serialVersionUID = 1L;

        protected String[] index;

        /**
         * Construct an empty similarity matrix. Only for IOUtils use.
         */
        protected SimilarityMatrix() {
                super();
        }

        /**
         * Construct a similarity matrix with the given size
         * and allocate the index accordingly.
         * @param size the size of the matrix
         */
        public SimilarityMatrix(int size) {
                super(size, size);
                index = new String[size];
        }

        /**
         * Construct a similarity matrix with the given index
         * and set the matrix size based on the index length.
         * @param index the index.
         */
        public SimilarityMatrix(String [] index) {
                super(index.length, index.length);
                this.index = index;
        }

        /**
         * Construct a similarity matrix based on the given index
         * and matrix. The matrix must be square and its dimensions
         * must be the same as the index length.
         * 
         * @param index the index
         * @param data the matrix
         */
        public SimilarityMatrix(String [] index, Matrix data) {
                super(data);

                if (data.getColumnDimension() != data.getRowDimension())
                        throw new IllegalArgumentException("matrix must be square");

                if (index.length != data.getRowDimension())
                        throw new IllegalArgumentException("index must have same length as matrix sides");

                this.index = index;
        }
        
        /**
         * Construct a similarity matrix based on the given index
         * and matrix data. The matrix data must be square and its dimensions
         * must be the same as the index length.
         * 
         * @param index the index
         * @param data the matrix data
         */
        public SimilarityMatrix(String [] index, double[][] data) {
                super(data);

                if (index.length != this.getRowDimension())
                        throw new IllegalArgumentException("index must have same length as matrix sides");

                this.index = index;
        }

        /**
         * Get the offset in the index for a given value 
         * @param value the value
         * @return the index
         */
        public int indexOf(String value) {
                return Arrays.binarySearch(index, value);
        }

        /**
         * Set the value of the index at a given offset
         * @param i the offset
         * @param value the value
         */
        public void setIndexValue(int i, String value) {
                index[i] = value;
        }

        /**
         * Get a value from the index
         * @param i the offset into the index
         * @return the value
         */
        public String getIndexValue(int i) {
                return index[i];
        }

        /**
         * Get the index
         * @return the index
         */
        public String [] getIndex() {
                return index;
        }

        @Override
        public void readASCII(Scanner in) throws IOException {
                super.readASCII(in);

                index = new String[this.getRowDimension()];

                for (int i=0; i<index.length; i++)
                        index[i] = in.nextLine();
        }

        @Override
        public String asciiHeader() {
                return this.getClass().getName() + " ";
        }

        @Override
        public void readBinary(DataInput in) throws IOException {
                super.readBinary(in);

                index = new String[this.getRowDimension()];

                for (int i=0; i<index.length; i++)
                        index[i] = in.readUTF();
        }

        @Override
        public byte[] binaryHeader() {
                return "SimMat".getBytes();
        }

        @Override
        public void writeASCII(PrintWriter out) throws IOException {
                super.writeASCII(out);

                for (String s : index)
                        out.println(s);
        }

        @Override
        public void writeBinary(DataOutput out) throws IOException {
                super.writeBinary(out);

                for (String s : index)
                        out.writeUTF(s);
        }

        /**
         * Convert the similarity matrix to an unweighted, undirected
         * graph representation. A threshold is used to determine
         * if edges should be created. If the value at [r][c] is bigger
         * than the threshold, then an edge will be created between the
         * vertices represented by index[r] and index[c].
         * 
         * @param threshold the threshold
         * @return the graph
         */
        public UndirectedGraph<String, DefaultEdge> toUndirectedUnweightedGraph(double threshold) {
                UndirectedGraph<String, DefaultEdge> graph = new SimpleGraph<String, DefaultEdge>(DefaultEdge.class);

                final int rows = this.getRowDimension();
                final int cols = this.getColumnDimension();
                final double[][] data = this.getArray(); 

                for (String s : index) {
                        graph.addVertex(s);
                }

                for (int r=0; r<rows; r++) {
                        for (int c=0; c<cols; c++) {
                                if (r != c && data[r][c] > threshold)
                                        graph.addEdge(index[r], index[c]);
                        }
                }

                return graph;
        }

        @Override   
        public SimilarityMatrix copy() {
                double[][] C = this.getArrayCopy();
                String[] i = Arrays.copyOf(index, index.length);
                
                return new SimilarityMatrix(i, C);
        }
        
        @Override   
        public SimilarityMatrix clone() {
                return copy();
        }

        /**
         * Process a copy of this similarity matrix with the
         * given processor and return the copy.
         * 
         * @param proc the processor
         * @return a processed copy of this matrix
         */
        public SimilarityMatrix process(SimilarityMatrixProcessor proc) {
                SimilarityMatrix mat = this.clone();
                proc.process(mat);
                return mat;
        }

        /**
         * Process this matrix with the given processor.
         * @param proc the processor
         * @return this.
         */
        public SimilarityMatrix processInplace(SimilarityMatrixProcessor proc) {
                proc.process(this);
                return this;
        }

        @Override
        public String toString() {
                StringBuilder sb = new StringBuilder();

                int maxIndexLength = 0;
                for (String s : index) 
                        if (s.length() > maxIndexLength) 
                                maxIndexLength = s.length();

                final int maxIndexCountLength = (index.length + "").length();
                final String indexFormatString = "%"+(maxIndexCountLength+2)+"s %" + maxIndexLength + "s ";  

                final int rows = this.getRowDimension();
                final int cols = this.getColumnDimension();
                final double[][] data = this.getArray(); 

                sb.append(String.format("%"+(maxIndexLength+maxIndexCountLength+3)+"s", ""));
                for (int r=0; r<rows; r++) {
                        sb.append(String.format("%9s", String.format("(%d)", r)));
                }
                sb.append("\n");

                for (int r=0; r<rows; r++) {
                        sb.append(String.format(indexFormatString, String.format("(%d)", r), index[r]));

                        for (int c=0; c<cols; c++) {
                                sb.append(String.format("%8.3f ", data[r][c]));
                        }
                        sb.append("\n");
                }

                return sb.toString();
        }
}