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| /*
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];
double *numEmptyCC = new double[myCLA.numRun];
double *numSingletonRC = new double[myCLA.numRun];
double *numSingletonCC = new double[myCLA.numRun];
double *initialObjectVal = new double[myCLA.numRun];
double *finalObjectVal = new double[myCLA.numRun];
double *initialRowPrecision = NULL, *initialColPrecision = NULL; // micro-averaged precision
double *finalRowPrecision = NULL, *finalColPrecision = NULL; // micro-averaged precision
double *initialRowAccuracy = NULL, *initialColAccuracy = NULL;
double *finalRowAccuracy = NULL, *finalColAccuracy = NULL;
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
initialRowPrecision = new double[myCLA.numRun];
finalRowPrecision = new double[myCLA.numRun];
initialRowAccuracy = new double[myCLA.numRun];
finalRowAccuracy = new double[myCLA.numRun];
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
initialColPrecision = new double[myCLA.numRun];
finalColPrecision = new double[myCLA.numRun];
initialColAccuracy = new double[myCLA.numRun];
finalColAccuracy = new double[myCLA.numRun];
}
if (myCLA.takingReverse)
numReversedRow = new int[myCLA.numRun];
if (myCLA.computingOneWayObjective){
initialObjectVal4RC = new double[myCLA.numRun];
initialObjectVal4CC = new double[myCLA.numRun];
finalObjectVal4RC = new double[myCLA.numRun];
finalObjectVal4CC = new double[myCLA.numRun];
}
for (int i = 0; i < myCLA.numRun; i++){
cout << endl << myCC->classPrefix << "beginRandomRun(): numRun(" << i+1 << ")" << endl << endl;;
if (myCLA.dumpLevel == MAXIMUM_DUMP_LEVEL)
myCC->dumpFile << endl << myCC->classPrefix << "beginRandomRun(): numRun(" << i+1 << ")" << endl << endl;
if (myCLA.statisticsAccessMode != NO_OPEN_MODE)
myCC->statisticsFile << endl << myCC->classPrefix << "beginRandomRun(): numRun(" << i+1 << ")" << endl << endl;
myCC->setRowSmoothingFactor(myCLA.rowSmoothingFactor);
myCC->setColSmoothingFactor(myCLA.colSmoothingFactor);
myCC->doInitialization();
initialObjectVal[i] = myCC->getObjValue();
if (myCLA.computingOneWayObjective){
initialObjectVal4RC[i] = myCC->getObjValue4RowCluster();
initialObjectVal4CC[i] = myCC->getObjValue4ColCluster();
}
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
myCC->validateRowCluster(myCLA.numRowClass, myCLA.rowClassLabel);
initialRowPrecision[i] = myCC->getRowPrecision();
initialRowAccuracy[i] = myCC->getRowAccuracy();
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
myCC->validateColCluster(myCLA.numColClass, myCLA.colClassLabel);
initialColPrecision[i] = myCC->getColPrecision();
initialColAccuracy[i] = myCC->getColAccuracy();
}
myCC->doPingPong();
numEmptyRC[i] = myCC->getEmptyRC();
numEmptyCC[i] = myCC->getEmptyCC();
numSingletonRC[i] = myCC->getSingletonRC();
numSingletonCC[i] = myCC->getSingletonCC();
finalObjectVal[i] = myCC->getObjValue();
if (myCLA.computingOneWayObjective){
finalObjectVal4RC[i] = myCC->getObjValue4RowCluster();
finalObjectVal4CC[i] = myCC->getObjValue4ColCluster();
}
numPingPong[i] = myCC->getNumIteration();
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
myCC->validateRowCluster(myCLA.numRowClass, myCLA.rowClassLabel);
finalRowPrecision[i] = myCC->getRowPrecision();
finalRowAccuracy[i] = myCC->getRowAccuracy();
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
myCC->validateColCluster(myCLA.numColClass, myCLA.colClassLabel);
finalColPrecision[i] = myCC->getColPrecision();
finalColAccuracy[i] = myCC->getColAccuracy();
}
if (myCLA.takingReverse)
numReversedRow[i] = myCC->getNumReversedRow();
cout << myCC->classPrefix << "endRandomRun(): numRun(" << i+1 << ")" << endl << endl;
if (myCLA.dumpLevel == MAXIMUM_DUMP_LEVEL)
myCC->dumpFile << myCC->classPrefix << "endRandomRun(): numRun(" << i+1 << ")" << endl << endl;
if (myCLA.statisticsAccessMode != NO_OPEN_MODE)
myCC->statisticsFile << myCC->classPrefix << "endRandomRun(): numRun(" << i+1 << ")" << endl << endl;
}
// cout << endl << " ### " << myCLA.numRun << " random run(s) done ###" << endl << endl;
// if (myCLA.dumpLevel == MAXIMUM_DUMP_LEVEL)
// myCC->dumpFile << endl << " ### " << myCLA.numRun << " random run(s) done ###" << endl << endl;
// if (myCLA.statisticsAccessMode != NO_OPEN_MODE)
// myCC->statisticsFile << endl << " ### " << myCLA.numRun << " random run(s) done ###" << endl << endl;
outputStatistics(myCLA, myCC->classPrefix, numPingPong, "Average # of PingPong Iteration = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, numEmptyRC, "Average # of Empty Row Clusters = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, numEmptyCC, "Average # of Empty Col Clusters = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, numSingletonRC, "Average # of Singleton Row Clusters = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, numSingletonCC, "Average # of Singleton Col Clusters = ", myCC->dumpFile, myCC->statisticsFile);
if (myCLA.takingReverse){
outputStatistics(myCLA, myCC->classPrefix, numReversedRow,"Average # of Reversed Row(s) = ", myCC->dumpFile, myCC->statisticsFile);
delete [] numReversedRow;
}
outputStatistics(myCLA, myCC->classPrefix, initialObjectVal, "Average Initial Objective Value = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, finalObjectVal, "Average Final Objective Value = ", myCC->dumpFile, myCC->statisticsFile);
if (myCLA.computingOneWayObjective){
outputStatistics(myCLA, myCC->classPrefix, initialObjectVal4RC, "Average Initial Obj. of Row Cluster = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, finalObjectVal4RC, "Average Final Obj. of Row Cluster = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, initialObjectVal4CC, "Average Initial Obj. of Col Cluster = ", myCC->dumpFile, myCC->statisticsFile);
outputStatistics(myCLA, myCC->classPrefix, finalObjectVal4CC, "Average Final Obj. of Col Cluster = ", myCC->dumpFile, myCC->statisticsFile);
delete [] initialObjectVal4RC;
delete [] initialObjectVal4CC;
delete [] finalObjectVal4RC;
delete [] finalObjectVal4CC;
}
delete [] numPingPong;
delete [] numEmptyRC;
delete [] numEmptyCC;
delete [] numSingletonRC;
delete [] numSingletonCC;
delete [] initialObjectVal;
delete [] finalObjectVal;
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, initialRowPrecision, "Average Initial Row Precision Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] initialRowPrecision;
}
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, finalRowPrecision, "Average Final Row Precision Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] finalRowPrecision;
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, initialColPrecision, "Average Initial Col Precision Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] initialColPrecision;
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, finalColPrecision, "Average Final Col Precision Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] finalColPrecision;
}
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, initialRowAccuracy, "Average Initial Row Accuracy Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] initialRowAccuracy;
}
if (myCLA.externalValidityType == ROW_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, finalRowAccuracy, "Average Final Row Accuracy Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] finalRowAccuracy;
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, initialColAccuracy, "Average Initial Col Accuracy Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] initialColAccuracy;
}
if (myCLA.externalValidityType == COL_EXTERNAL_VALIDITY || myCLA.externalValidityType == BOTH_EXTERNAL_VALIDITY){
outputStatistics(myCLA, myCC->classPrefix, finalColAccuracy, "Average Final Col Accuracy Value = ", myCC->dumpFile, myCC->statisticsFile);
delete [] finalColAccuracy;
}
//------------------------------------------------------------------------------
if (myCLA.coclusterAccessMode != NO_OPEN_MODE)
if (myCLA.numRun == 1 || (myCLA.numRun > 1 && myCLA.coclusterAccessMode == APPEND_MODE))
myCC->writeCocluster();
//------------------------------------------------------------------------------
}
outputRunTime(runTime, myCLA, myCC->classPrefix, myCC->dumpFile, myCC->statisticsFile);
outputDeconstructor(myCLA, myCC->classPrefix, myCC->dumpFile, myCC->statisticsFile);
outputCommandLineArgument(argc, argv, cout);
outputCommandLineArgument(argc, argv, myCC->dumpFile);
outputCommandLineArgument(argc, argv, myCC->statisticsFile);
} |
Probleme erreurs deprecated conversion et extra qualification
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