//------------------------------------------------------------------------
void undo_wave_temp(fstream &in, fstream &out, string file_name)
{
if(in.is_open())
in.close();
if(out.is_open())
out.close();
remove(file_name.c_str());
rename( (file_name + ".tmp").c_str()
, file_name.c_str()
);
return;
}
/*
* Given a file stream, opens that filestream to log.txt
* Throws log_fail_ex on failure
*/
void open_log_file(fstream &log)
{
if (log.is_open())
{
throw log_fail_ex;
}
log.open("log.txt", ios::out | ios::app);
if (!log.is_open())
{
throw log_fail_ex;
}
}
bool RBMQuantizer::saveModelToFile(fstream &file) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header
file << "RBM_QUANTIZER_FILE_V1.0" << endl;
//Save the base feature extraction settings to the file
if( !saveBaseSettingsToFile( file ) ){
errorLog << "saveModelToFile(fstream &file) - Failed to save base feature extraction settings to file!" << endl;
return false;
}
file << "QuantizerTrained: " << trained << endl;
file << "NumClusters: " << numClusters << endl;
if( trained ){
if( !rbm.saveModelToFile( file ) ){
errorLog << "saveModelToFile(fstream &file) - Failed to save RBM settings to file!" << endl;
return false;
}
}
return true;
}
bool TimeseriesBuffer::loadSettingsFromFile(fstream &file){
if( !file.is_open() ){
errorLog << "loadSettingsFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
string word;
//Load the header
file >> word;
if( word != "GRT_TIMESERIES_BUFFER_FILE_V1.0" ){
errorLog << "loadSettingsFromFile(fstream &file) - Invalid file format!" << endl;
return false;
}
if( !loadBaseSettingsFromFile( file ) ){
errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to load base feature extraction settings from file!" << endl;
return false;
}
file >> word;
if( word != "BufferSize:" ){
errorLog << "loadSettingsFromFile(fstream &file) - Failed to read BufferSize header!" << endl;
return false;
}
file >> bufferSize;
//Init the TimeseriesBuffer module to ensure everything is initialized correctly
return init(bufferSize,numInputDimensions);
}
void open_file_update(fstream& fp, const char* name, const char* folder_name) {
char file_name[BUFLEN];
if (folder_name == NULL)
snprintf(file_name, BUFLEN-1, "%s/%s", FOLDER_STATE, name);
else
snprintf(file_name, BUFLEN-1, "%s/%s", folder_name, name);
fp.open(file_name, ios_base::in | ios_base::out);
if (!fp.is_open()) {
fp.open(file_name, ios_base::out);
if (!fp.is_open()) {
cout <<"Warning: could not operate file "<<file_name << endl;
exit(1);
}
}
}
bool LogisticRegression::saveModelToFile(fstream &file) const{
if(!file.is_open())
{
errorLog << "loadModelFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header info
file<<"GRT_LOGISTIC_REGRESSION_MODEL_FILE_V2.0\n";
//Write the regressifier settings to the file
if( !Regressifier::saveBaseSettingsToFile(file) ){
errorLog <<"saveModelToFile(fstream &file) - Failed to save Regressifier base settings to file!" << endl;
return false;
}
if( trained ){
file << "Weights: ";
file << w0;
for(UINT j=0; j<numInputDimensions; j++){
file << " " << w[j];
}
file << endl;
}
return true;
}
bool Softmax::saveModelToFile(fstream &file) const{
if(!file.is_open())
{
errorLog <<"loadModelFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header info
file<<"GRT_SOFTMAX_MODEL_FILE_V1.0\n";
file<<"NumFeatures: "<<numFeatures<<endl;
file<<"NumClasses: "<<numClasses<<endl;
file <<"UseScaling: " << useScaling << endl;
file<<"UseNullRejection: " << useNullRejection << endl;
///Write the ranges if needed
if( useScaling ){
file << "Ranges: \n";
for(UINT n=0; n<ranges.size(); n++){
file << ranges[n].minValue << "\t" << ranges[n].maxValue << endl;
}
}
file << "Models:\n";
for(UINT k=0; k<numClasses; k++){
file << "ClassLabel: " << models[k].classLabel << endl;
file << "Weights: " << models[k].w0;
for(UINT n=0; n<numFeatures; n++){
file << " " << models[k].w[n];
}
file << endl;
}
return true;
}
bool FeatureExtraction::loadBaseSettingsFromFile(fstream &file){
featureVector.clear();
if( !file.is_open() ){
errorLog << "loadBaseSettingsFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
string word;
//Load the NumInputDimensions
file >> word;
if( word != "NumInputDimensions:" ){
errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read NumInputDimensions header!" << endl;
return false;
}
file >> numInputDimensions;
//Load the NumOutputDimensions
file >> word;
if( word != "NumOutputDimensions:" ){
errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to read NumOutputDimensions header!" << endl;
return false;
}
file >> numOutputDimensions;
//Resize the feature vector
featureVector.resize(numOutputDimensions,0);
return true;
}
int printFile(){
if(fs.is_open())
{
// get the starting position
streampos start = fs.tellg();
// go to the end
fs.seekg(0, std::ios::end);
// get the ending position
streampos end = fs.tellg();
// go back to the start
fs.seekg(0, std::ios::beg);
// create a vector to hold the data that
// is resized to the total size of the file
std::vector<char> contents;
contents.resize(static_cast<size_t>(end - start));
// read it in
fs.read(&contents[0], contents.size());
// print it out (for clarity)
cout <<"\n Full File:\n";
for(int i=0; i < contents.size(); i++){
cout << i;
};
cout<<"\n File Read Done!!\n";
}
}
bool LeakyIntegrator::loadModelFromFile(fstream &file){
if( !file.is_open() ){
errorLog << "loadModelFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
string word;
//Load the header
file >> word;
if( word != "GRT_LEAKY_INTEGRATOR_FILE_V1.0" ){
errorLog << "loadModelFromFile(fstream &file) - Invalid file format!" << endl;
return false;
}
if( !PreProcessing::loadPreProcessingSettingsFromFile( file ) ){
errorLog << "loadPreProcessingSettingsFromFile(fstream &file) - Failed to load preprocessing settings from file!" << endl;
return false;
}
//Load the LeakRate
file >> word;
if( word != "LeakRate:" ){
errorLog << "loadModelFromFile(fstream &file) - Failed to read LeakRate header!" << endl;
return false;
}
file >> leakRate;
//Init the LeakyIntegrator module to ensure everything is initialized correctly
return init(leakRate,numInputDimensions);
}
bool KMeans::saveModelToFile(fstream &file) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - Failed to save model, file is not open!" << endl;
return false;
}
file << "GRT_KMEANS_MODEL_FILE_V1.0\n";
if( !saveClustererSettingsToFile( file ) ){
errorLog << "saveModelToFile(fstream &file) - Failed to save clusterer settings to file!" << endl;
return false;
}
if( trained ){
file << "Clusters:\n";
for(UINT k=0; k<numClusters; k++){
for(UINT n=0; n<numInputDimensions; n++){
file << clusters[k][n] << "\t";
}file << endl;
}
}
return true;
}
bool RegressionTree::saveModelToFile(fstream &file) const{
if(!file.is_open())
{
Regressifier::errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header info
file << "GRT_REGRESSION_TREE_MODEL_FILE_V1.0\n";
//Write the classifier settings to the file
if( !Regressifier::saveBaseSettingsToFile(file) ){
Regressifier::errorLog <<"saveModelToFile(fstream &file) - Failed to save classifier base settings to file!" << endl;
return false;
}
file << "NumSplittingSteps: " << numSplittingSteps << endl;
file << "MinNumSamplesPerNode: " << minNumSamplesPerNode << endl;
file << "MaxDepth: " << maxDepth << endl;
file << "RemoveFeaturesAtEachSpilt: " << removeFeaturesAtEachSpilt << endl;
file << "TrainingMode: " << trainingMode << endl;
file << "TreeBuilt: " << (tree != NULL ? 1 : 0) << endl;
if( tree != NULL ){
file << "Tree:\n";
if( !tree->saveToFile( file ) ){
Regressifier::errorLog << "saveModelToFile(fstream &file) - Failed to save tree to file!" << endl;
return false;
}
}
return true;
}
bool MovementTrajectoryFeatures::saveSettingsToFile(fstream &file) const{
if( !file.is_open() ){
errorLog << "saveSettingsToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the file header
file << "GRT_MOVEMENT_TRAJECTORY_FEATURES_FILE_V1.0" << endl;
//Save the base settings to the file
if( !saveBaseSettingsToFile( file ) ){
errorLog << "saveSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << endl;
return false;
}
//Write the movement trajectory settings to the file
file << "TrajectoryLength: " << trajectoryLength << endl;
file << "NumCentroids: " << numCentroids << endl;
file << "FeatureMode: " << featureMode << endl;
file << "NumHistogramBins: " << numHistogramBins << endl;
file << "UseTrajStartAndEndValues: " << useTrajStartAndEndValues << endl;
file << "UseWeightedMagnitudeValues: " << useWeightedMagnitudeValues << endl;
return true;
}
bool is_terminated() { // check if one of the programs terminates
//read the existing files
file.open(filename.c_str(), ios::in);
if (file.is_open()) //if there is a program end first
return true;
return false;
}
bool FFT::loadSettingsFromFile(fstream &file){
if( !file.is_open() ){
errorLog << "loadSettingsFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
string word;
//Load the header
file >> word;
if( word != "GRT_FFT_FILE_V1.0" ){
errorLog << "loadSettingsFromFile(fstream &file) - Invalid file format!" << endl;
return false;
}
if( !loadBaseSettingsFromFile( file ) ){
errorLog << "loadBaseSettingsFromFile(fstream &file) - Failed to load base feature extraction settings from file!" << endl;
return false;
}
file >> word;
if( word != "HopSize:" ){
errorLog << "loadSettingsFromFile(fstream &file) - Failed to read HopSize header!" << endl;
return false;
}
file >> hopSize;
file >> word;
if( word != "FftWindowSize:" ){
errorLog << "loadSettingsFromFile(fstream &file) - Failed to read FftWindowSize header!" << endl;
return false;
}
file >> fftWindowSize;
file >> word;
if( word != "FftWindowFunction:" ){
errorLog << "loadSettingsFromFile(fstream &file) - Failed to read FftWindowFunction header!" << endl;
return false;
}
file >> fftWindowFunction;
file >> word;
if( word != "ComputeMagnitude:" ){
errorLog << "loadSettingsFromFile(fstream &file) - Failed to read ComputeMagnitude header!" << endl;
return false;
}
file >> computeMagnitude;
file >> word;
if( word != "ComputePhase:" ){
errorLog << "loadSettingsFromFile(fstream &file) - Failed to read ComputePhase header!" << endl;
return false;
}
file >> computePhase;
//Init the FFT module to ensure everything is initialized correctly
return init(fftWindowSize,hopSize,numInputDimensions,fftWindowFunction,computeMagnitude,computePhase);
}
MStatus
XmlCacheFormat::readHeader()
{
bool rtn = false;
if (kWrite != fMode) {
if( fFile.is_open() ) {
string tag;
readXmlTag( tag );
if( tag == XMLSTARTTAG(cacheTag) )
{
MStringArray value;
readXmlTagValue( versionTag, value );
readXmlTagValue( startTimeTag, value );
readXmlTagValue( endTimeTag, value );
readXmlTag( tag ); // Should be header close tag, check
if( tag != XMLENDTAG(cacheTag) )
{
// TODO - handle this error case
}
rtn = true;
}
}
}
return rtn ? MS::kSuccess : MS::kFailure ;
}
bool FFT::saveSettingsToFile(fstream &file){
if( !file.is_open() ){
errorLog << "saveSettingsToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the file header
file << "GRT_FFT_FILE_V1.0" << endl;
//Save the base settings to the file
if( !saveBaseSettingsToFile( file ) ){
errorLog << "saveSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << endl;
return false;
}
//Write the FFT settings
file << "HopSize: " << hopSize << endl;
file << "FftWindowSize: " << fftWindowSize << endl;
file << "FftWindowFunction: " << fftWindowFunction << endl;
file << "ComputeMagnitude: " << computeMagnitude << endl;
file << "ComputePhase: " << computePhase << endl;
return true;
}
bool AdaBoost::saveModelToFile(fstream &file) const{
if(!file.is_open())
{
errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header info
file<<"GRT_ADABOOST_MODEL_FILE_V2.0\n";
//Write the classifier settings to the file
if( !Classifier::saveBaseSettingsToFile(file) ){
errorLog <<"saveModelToFile(fstream &file) - Failed to save classifier base settings to file!" << endl;
return false;
}
//Write the AdaBoost settings to the file
file << "PredictionMethod: " << predictionMethod << endl;
//If the model has been trained then write the model
if( trained ){
file << "Models: " << endl;
for(UINT i=0; i<models.size(); i++){
if( !models[i].saveModelToFile( file ) ){
errorLog <<"saveModelToFile(fstream &file) - Failed to write model " << i << " to file!" << endl;
file.close();
return false;
}
}
}
return true;
}
MStatus
XmlCacheFormat::open( const MString& fileName, FileAccessMode mode )
{
bool rtn = true;
assert((fileName.length() > 0));
fFileName = fileName;
if( mode == kWrite ) {
fFile.open(fFileName.asChar(), ios::out);
}
else if (mode == kReadWrite) {
fFile.open(fFileName.asChar(), ios::app);
} else {
fFile.open(fFileName.asChar(), ios::in);
}
if (!fFile.is_open()) {
rtn = false;
} else {
if (mode == kRead) {
rtn = readHeader();
}
}
return rtn ? MS::kSuccess : MS::kFailure ;
}
bool KMeansQuantizer::saveSettingsToFile(fstream &file) const{
if( !file.is_open() ){
errorLog << "saveSettingsToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//First, you should add a header (with no spaces) e.g.
file << "KMEANS_QUANTIZER_FILE_V1.0" << endl;
//Second, you should save the base feature extraction settings to the file
if( !saveBaseSettingsToFile( file ) ){
errorLog << "saveSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << endl;
return false;
}
file << "QuantizerTrained: " << quantizerTrained << endl;
file << "NumClusters: " << numClusters << endl;
if( quantizerTrained ){
file << "Clusters: \n";
for(UINT k=0; k<numClusters; k++){
for(UINT j=0; j<numInputDimensions; j++){
file << clusters[k][j];
if( j != numInputDimensions-1 ) file << "\t";
else file << endl;
}
}
}
return true;
}
bool TimeDomainFeatures::saveModelToFile(fstream &file) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the file header
file << "GRT_TIME_DOMAIN_FEATURES_FILE_V1.0" << endl;
//Save the base settings to the file
if( !saveFeatureExtractionSettingsToFile( file ) ){
errorLog << "saveFeatureExtractionSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << endl;
return false;
}
//Write the time domain settings to the file
file << "BufferLength: " << bufferLength << endl;
file << "NumFrames: " << numFrames << endl;
file << "OffsetInput: " << offsetInput << endl;
file << "UseMean: " << useMean << endl;
file << "UseStdDev: " << useStdDev << endl;
file << "UseEuclideanNorm: " << useEuclideanNorm << endl;
file << "UseRMS: " << useRMS << endl;
return true;
}
bool MovementIndex::loadModelFromFile(fstream &file) {
if( !file.is_open() ) {
errorLog << "loadModelFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
string word;
//Load the header
file >> word;
if( word != "GRT_MOVEMENT_INDEX_FILE_V1.0" ) {
errorLog << "loadModelFromFile(fstream &file) - Invalid file format!" << endl;
return false;
}
if( !loadFeatureExtractionSettingsFromFile( file ) ) {
errorLog << "loadFeatureExtractionSettingsFromFile(fstream &file) - Failed to load base feature extraction settings from file!" << endl;
return false;
}
//Load the BufferLength
file >> word;
if( word != "BufferLength:" ) {
errorLog << "loadModelFromFile(fstream &file) - Failed to read BufferLength header!" << endl;
return false;
}
file >> bufferLength;
//Init the MovementIndex module to ensure everything is initialized correctly
return init(bufferLength,numInputDimensions);
}
ElectronPhononExchangeHertel::ElectronPhononExchangeHertel(fstream &fileId,
map<string,double> & parameters,
Material * material)
: ElectronPhononExchange(),
exchangeCoef_(0),
debeyeTemperature_(1),
massEnhancement_(0),
material_(material)
{
if (!fileId.is_open()) throw ATC_Error("cannot open material file");
vector<string> line;
while(fileId.good()) {
command_line(fileId, line);
if (line.size() == 0) continue;
if (line[0] == "end") break;
else if (line[0] == "debeye_temperature") {
debeyeTemperature_ = str2dbl(line[1]);
parameters["debeye_temperature"] = debeyeTemperature_;
}
else if (line[0] == "mass_enhancement") {
massEnhancement_ = str2dbl(line[1]);
parameters["mass_enhancement"] = massEnhancement_;
}
else {
throw ATC_Error( "unrecognized material function "+line[0]);
}
}
// coupling coefficient, eqn. 15 of Hertel 2002
double kb = LammpsInterface::instance()->kBoltzmann();
double hbar = LammpsInterface::instance()->hbar();
double PI = 3.141592653589793238;
exchangeCoef_ = 144.*1.0369*kb/(PI*hbar);
exchangeCoef_ *= massEnhancement_/pow(debeyeTemperature_,2);
}
bool Softmax::saveModelToFile(fstream &file) const{
if(!file.is_open())
{
errorLog <<"loadModelFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header info
file<<"GRT_SOFTMAX_MODEL_FILE_V2.0\n";
//Write the classifier settings to the file
if( !Classifier::saveBaseSettingsToFile(file) ){
errorLog <<"saveModelToFile(fstream &file) - Failed to save classifier base settings to file!" << endl;
return false;
}
if( trained ){
file << "Models:\n";
for(UINT k=0; k<numClasses; k++){
file << "ClassLabel: " << models[k].classLabel << endl;
file << "Weights: " << models[k].w0;
for(UINT n=0; n<numInputDimensions; n++){
file << " " << models[k].w[n];
}
file << endl;
}
}
return true;
}
bool PreProcessing::loadPreProcessingSettingsFromFile(fstream &file){
if( !file.is_open() ){
errorLog << "loadPreProcessingSettingsFromFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Try and load the base settings from the file
if( !MLBase::loadBaseSettingsFromFile( file ) ){
errorLog << "loadPreProcessingSettingsFromFile(fstream &file) - Failed to load base settings from file!" << endl;
return false;
}
string word;
//Load if the filter has been initialized
file >> word;
if( word != "Initialized:" ){
errorLog << "loadPreProcessingSettingsFromFile(fstream &file) - Failed to read Initialized header!" << endl;
clear();
return false;
}
file >> initialized;
//If the module has been initalized then call the init function to setup the processed data vector
if( initialized ){
return init();
}
return true;
}
/*
* Given a string containing an action that occurred and the result of that action, appends the string and the result to the log file
* Places a timestamp at the start of the string
* Places a newline at the end of the string
* Throws log_fail_ex if the log is not open
*/
void append_action_to_log(fstream& log, string s, bool result)
{
if (!log.is_open())
{
throw log_fail_ex;
}
time_t current_time;
time(¤t_time);
//FIXME remove newline char at the end of ctime
log << ctime(¤t_time) << ": " << s;
if (result)
{
log << " (SUCCESS)";
}else
{
log << " (FAILURE)";
}
log << endl;
if (log.fail())
{
throw log_fail_ex;
}
}
/*
* Given a (pre-formatted) string and file stream, appends the string to the log file
* Places a timestamp at the start of the string
* Places a newline at the end of the string if one is not at the end of the string
* Throws log_fail_ex if the log is not open
*/
void append_raw_to_log(fstream& log, string s)
{
if (!log.is_open())
{
throw log_fail_ex;
}
time_t current_time;
time(¤t_time);
log << ctime(¤t_time) << s;
if (s[s.size() - 1] != '\n')
{
log << endl;
}else
{
flush(log);
}
if (log.fail())
{
throw log_fail_ex;
}
}
bool BernoulliRBM::saveModelToFile(fstream &file) const{
if(!file.is_open())
{
errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << endl;
return false;
}
//Write the header info
file<<"GRT_BERNOULLI_RBM_MODEL_FILE_V1.1\n";
if( !saveBaseSettingsToFile( file ) ){
errorLog <<"saveModelToFile(fstream &file) - Failed to save base settings to file!" << endl;
return false;
}
file << "NumVisibleUnits: " << numVisibleUnits << endl;
file << "NumHiddenUnits: " << numHiddenUnits << endl;
file << "BatchSize: " << batchSize << endl;
file << "BatchStepSize: " << batchStepSize << endl;
file << "LearningRate: " << learningRate << endl;
file << "LearningRateUpdate: " << learningRateUpdate << endl;
file << "Momentum: " << momentum << endl;
file << "RandomizeWeightsForTraining: " << randomizeWeightsForTraining << endl;
file << "Ranges: \n";
for(UINT n=0; n<ranges.size(); n++){
file << ranges[n].minValue << "\t" << ranges[n].maxValue << endl;
}
//If the model has been trained then write the model
if( trained ){
file << "WeightsMatrix: " << endl;
for(UINT i=0; i<weightsMatrix.getNumRows(); i++){
for(UINT j=0; j<weightsMatrix.getNumCols(); j++){
file << weightsMatrix[i][j];
if( j < weightsMatrix.getNumCols()-1 ) file << " ";
}
file << endl;
}
file << "VisibleLayerBias: ";
for(unsigned int i=0; i<visibleLayerBias.size(); i++){
file << visibleLayerBias[i];
if( i < visibleLayerBias.size()-1 ) file << " ";
}
file << endl;
file << "HiddenLayerBias: ";
for(unsigned int i=0; i<hiddenLayerBias.size(); i++){
file << hiddenLayerBias[i];
if( i < hiddenLayerBias.size()-1 ) file << " ";
}
file << endl;
}
return true;
}
//自分が入力したファイル名のファイルを開けるようになった！
bool DumpFile::Open(){
string filename;
cout << "ファイル名 > " << flush;
getline(cin, filename);
m_file.open(filename.c_str(),ios::in|ios::binary);
m_page = 0;
return m_file.is_open();
}
/*
* Given a file stream, cleanly closes the file
*/
void close_log_file(fstream& log)
{
if (log.is_open())
{
log.close();
}
//If the log is already closed, we have nothing to worry about
}
