/*  Cocluster.cc    the main file for co-clustering programs         Copyright (c) 2005, 2006              by Hyuk Cho    Copyright (c) 2003, 2004    	      by Hyuk Cho, Yuqiang Guan, and Suvrit Sra                {hyukcho, yguan, suvrit}@cs.utexas.edu*/#include <fstream>#include <cmath>#include <iomanip>#include <time.h>#include <stdio.h>#include <stdlib.h>#include <string.h>#include <assert.h>#include "MatrixVector.h"#include "TimerUtility.h"#include "Tools.h"#include "Itcc.h"#include "MssrIcc.h"#include "MssrIIcc.h"using namespace std;long memoryUsed = 0l;int main(int argc, char **argv){  commandLineArgument myCLA;  //------------------------------------------------------------------------------  setCommandLine(myCLA);		// set default command line arguments    getCommandLine(argc, argv, myCLA);	// get command line arguments//------------------------------------------------------------------------------  if (!myCLA.havingArgument)    printHelp();  if (myCLA.numInvalidCLA != 0)    exit(EXIT_FAILURE);  if (myCLA.algorithmType == INFORMATION_THEORETIC_CC && myCLA.takingReverse){    cout << "  Taking reverse is invalid for ITCC..." << endl << endl;    exit(EXIT_FAILURE);  }  if (myCLA.takingReverse && myCLA.coclusterOffsetType == START_FROM_0){    cout << "  Taking reverse and output labels starting from 0 are not valid..." << endl << endl;    exit(EXIT_FAILURE);  }//  strcat(myCLA.dumpFilename, DUMP_FILENAME_SUFFIX);//  strcat(myCLA.objectiveFilename, OBJECTIVE_FILENAME_SUFFIX);//  strcat(myCLA.coclusterFilename, COCLUSTER_FILENAME_SUFFIX);//  makeFilename(myCLA.objectiveFilename, OBJECTIVE_FILENAME_SUFFIX, myCLA);//  makeFilename(myCLA.coclusterFilename, COCLUSTER_FILENAME_SUFFIX, myCLA);//------------------------------------------------------------------------------  sparseStruct sparseCCS;  sparseStruct sparseCRS;  denseStruct denseMat;  TimerUtil runTime;  switch (myCLA.inputMatrixType){    case DENSE_MATRIX:    case DENSE_MATRIX_TRANS:	// not used...      myCLA.emptyColId = readMatrix(myCLA.inputFilename, &denseMat, myCLA.numEmptyCol, myCLA.inputFormatType, myCLA.inputMatrixType);      assert(((myCLA.numRowCluster <= denseMat.numRow) && (myCLA.numColCluster <= denseMat.numCol)) && (myCLA.numRowCluster != denseMat.numRow || myCLA.numColCluster != denseMat.numCol));      assert((myCLA.rowLocalSearchLength < denseMat.numRow) && (myCLA.colLocalSearchLength < denseMat.numCol));      memoryUsed += denseMat.numRow * denseMat.numCol * sizeof(double);      break;    case SPARSE_MATRIX:      myCLA.emptyColId = readMatrix(myCLA.inputFilename, &sparseCCS, myCLA.numEmptyCol, myCLA.inputFormatType, myCLA.scalingType);      assert(((myCLA.numRowCluster <= sparseCCS.numRow) && (myCLA.numColCluster <= sparseCCS.numCol)) && (myCLA.numRowCluster != sparseCCS.numRow || myCLA.numColCluster != sparseCCS.numCol));      assert((myCLA.rowLocalSearchLength < sparseCCS.numRow) && (myCLA.colLocalSearchLength < sparseCCS.numCol));      sparseCRS.colPtr = new int[sparseCCS.numRow+1];      sparseCRS.rowIdx = new int[sparseCCS.numVal];      sparseCRS.value = new double[sparseCCS.numVal];      sparseCRS.numCol = sparseCCS.numRow;      sparseCRS.numRow = sparseCCS.numCol;      sparseCRS.numVal = sparseCCS.numVal;      convertSparse2Sparse(sparseCCS.numCol, sparseCCS.numRow, sparseCCS.numVal, sparseCCS.colPtr, sparseCCS.rowIdx, sparseCCS.value, sparseCRS.colPtr, sparseCRS.rowIdx, sparseCRS.value);  //     checkConvertSparse2Sparse(sparseCCS.numCol, sparseCCS.numRow, sparseCCS.numVal, sparseCCS.colPtr, sparseCCS.rowIdx, sparseCCS.value, sparseCRS.colPtr, sparseCRS.rowIdx, sparseCRS.value);       memoryUsed += (sparseCCS.numCol + sparseCCS.numRow + 2 * sparseCCS.numVal) * sizeof(int) + 2 * sparseCCS.numVal * sizeof(double);      break;    default:      break;  }//------------------------------------------------------------------------------//------------------------------------------------------------------------------  Matrix *myCCS = NULL;  Matrix *myCRS = NULL;  Coclustering *myCC = NULL;    switch (myCLA.inputMatrixType){    case DENSE_MATRIX:      myCCS = new DenseMatrix(denseMat.numRow, denseMat.numCol, denseMat.value);       myCRS = myCCS;      if (myCLA.algorithmType == INFORMATION_THEORETIC_CC)        myCCS->preprocess();      break;    case SPARSE_MATRIX:      myCCS = new SparseMatrix(sparseCCS.numRow, sparseCCS.numCol, sparseCCS.numVal, sparseCCS.value, sparseCCS.rowIdx, sparseCCS.colPtr);      if (myCLA.algorithmType == INFORMATION_THEORETIC_CC)        myCCS->preprocess();      myCRS = new SparseMatrix(sparseCRS.numRow, sparseCRS.numCol, sparseCRS.numVal, sparseCRS.value, sparseCRS.rowIdx, sparseCRS.colPtr);       if (myCLA.algorithmType == INFORMATION_THEORETIC_CC)        myCRS->preprocess();      break;    default:      break;  }  //------------------------------------------------------------------------------  //------------------------------------------------------------------------------    switch (myCLA.algorithmType){    case INFORMATION_THEORETIC_CC:      myCC = new Itcc(myCCS, myCRS, myCLA);      myCC->classPrefix = ITCC_CLASS;      break;    case MINIMUM_SUM_SQUARE_RESIDUE_I_CC:      myCC = new MssrIcc(myCCS, myCRS, myCLA);      myCC->classPrefix = MSSRICC_CLASS;      break;    case MINIMUM_SUM_SQUARE_RESIDUE_II_CC:      myCC = new MssrIIcc(myCCS, myCRS, myCLA);      myCC->classPrefix = MSSRIICC_CLASS;      break;    default:      myCC = new Itcc(myCCS, myCRS, myCLA);      myCC->classPrefix = ITCC_CLASS;      break;  }//------------------------------------------------------------------------------  //------------------------------------------------------------------------------    if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY){    myCLA.rowClassLabel = new int[myCCS->getNumRow()];    myCLA.numRowClass = readLabel(myCLA.rowClassFilename, myCCS->getNumRow(), myCLA.rowClassLabel, myCLA.classOffsetType);  }  if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY){     myCLA.colClassLabel = new int[myCCS->getNumCol()];    myCLA.numColClass = readLabel(myCLA.colClassFilename, myCCS->getNumCol(), myCLA.colClassLabel, myCLA.classOffsetType);  }  if (myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){    myCLA.rowClassLabel = new int[myCCS->getNumRow()];    myCLA.colClassLabel = new int[myCCS->getNumRow()];    readLabel(myCLA.bothClassFilename, myCCS->getNumRow(), myCCS->getNumCol(), myCLA.rowClassLabel, myCLA.colClassLabel, myCLA.numRowClass, myCLA.numColClass, myCLA.classOffsetType);     }//------------------------------------------------------------------------------  //------------------------------------------------------------------------------      outputConstructor(myCLA, myCC->classPrefix, myCC->dumpFile, myCC->statisticsFile);  if (myCLA.numRun >= 1){    int *numPingPong = new int[myCLA.numRun];    int *numReversedRow = NULL;    double *initialObjectVal4RC = NULL, *initialObjectVal4CC = NULL;    double *finalObjectVal4RC = NULL, *finalObjectVal4CC = NULL;    double *numEmptyRC = new double[myCLA.numRun];