bool XorEncryptor::encryptData(std::fstream &original, std::fstream &result)
{
if (!original.is_open() || !result.is_open())
{
return false;
}
original.seekg(0, std::ios::beg);
result.seekp(0, std::ios::beg);
char c = 0;
unsigned i = 0;
while (original.good())
{
original.read(&c, 1);
c ^= password[i];
if(original.gcount() > 0)
{
result.write(&c, 1);
}
if (++i == passSize)
{
i = 0;
}
}
original.seekg(0, std::ios::beg);
result.seekg(0, std::ios::beg);
result.flush();
return true;
}
NMPRKC_API nmprk_status_t NMPRK_StartDebugLogging(
const char *filename)
{
char dateStr[MAX_DATE_STR_LEN];
char timeStr[MAX_DATE_STR_LEN];
if(si_fsDebugLog.is_open() == true)
return NMPRK_FAILURE;
try
{
si_debugModule = SI_DEBUG_MODULE_ALL;
si_debugLevel = SI_DEBUG_LEVEL_ALL;
si_fsDebugLog.open(filename, std::fstream::out | std::fstream::app);
if(si_fsDebugLog.is_open() != true)
return NMPRK_FAILURE;
}
catch (...)
{
return NMPRK_FAILURE;
}
#if defined WIN32
_strdate_s(dateStr, MAX_DATE_STR_LEN);
_strtime_s(timeStr, MAX_DATE_STR_LEN);
#else
time_t mytime = time(NULL);
strftime(dateStr, 9, "%D", localtime(&mytime));
strftime(timeStr, 9, "%T", localtime(&mytime));
#endif
SI_DEBUG_INFO(SI_THIS_MODULE, "Debug Logging Started: %s %s", dateStr, timeStr);
return NMPRK_SUCCESS;
}
// Ensures the file is opened/closed properly and retries 5 times.
// if choice is false, the file is closed and if it is 1, the file is opened.
bool verifiedOC ( std::fstream& file, std::string fileDir, bool choice, std::ios::openmode io ) {
unsigned int i = 0; // Declaring a counter variable.
// Choice determines if we are opening or closing the file. (True to open, False to close)
if ( choice ) {
do {
file.open ( fileDir.c_str(), io ); // Open file as user selection.
if ( file.is_open() ) {
return true;
} else {
// Prints that the attempt to change the file state has failed.
std::cout << "The file " << fileDir.c_str() << " failed to open... Retrying " << ++i << "\n";
}
// Will exit the loop after the the number of attempts FILE_OPEN_RETRIES specifies.
if ( i >= FILE_OPEN_RETRIES ) {
std::cout << "The file " << fileDir.c_str() << " failed to change open." << std::endl;
return false;
}
} while ( !file.is_open() );
} else {
file.close();
}
return true;
}
bool SnappyFile::rawOpen(const std::string &filename, File::Mode mode)
{
std::ios_base::openmode fmode = std::fstream::binary;
if (mode == File::Write) {
fmode |= (std::fstream::out | std::fstream::trunc);
createCache(SNAPPY_CHUNK_SIZE);
} else if (mode == File::Read) {
fmode |= std::fstream::in;
}
m_stream.open(filename.c_str(), fmode);
//read in the initial buffer if we're reading
if (m_stream.is_open() && mode == File::Read) {
m_stream.seekg(0, std::ios::end);
m_endPos = m_stream.tellg();
m_stream.seekg(0, std::ios::beg);
// read the snappy file identifier
unsigned char byte1, byte2;
m_stream >> byte1;
m_stream >> byte2;
assert(byte1 == SNAPPY_BYTE1 && byte2 == SNAPPY_BYTE2);
flushReadCache();
} else if (m_stream.is_open() && mode == File::Write) {
bool ClassLabelChangeFilter::load( std::fstream &file ){
if( !file.is_open() ){
errorLog << "load(fstream &file) - The file is not open!" << std::endl;
return false;
}
std::string word;
//Load the header
file >> word;
if( word != "GRT_CLASS_LABEL_CHANGE_FILTER_FILE_V1.0" ){
errorLog << "load(fstream &file) - Invalid file format!" << std::endl;
return false;
}
file >> word;
if( word != "NumInputDimensions:" ){
errorLog << "load(fstream &file) - Failed to read NumInputDimensions header!" << std::endl;
return false;
}
file >> numInputDimensions;
//Load the number of output dimensions
file >> word;
if( word != "NumOutputDimensions:" ){
errorLog << "load(fstream &file) - Failed to read NumOutputDimensions header!" << std::endl;
return false;
}
file >> numOutputDimensions;
//Init the classLabelTimeoutFilter module to ensure everything is initialized correctly
return init();
}
bool TimeDomainFeatures::saveModelToFile( std::fstream &file ) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
//Write the file header
file << "GRT_TIME_DOMAIN_FEATURES_FILE_V1.0" << std::endl;
//Save the base settings to the file
if( !saveFeatureExtractionSettingsToFile( file ) ){
errorLog << "saveFeatureExtractionSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << std::endl;
return false;
}
//Write the time domain settings to the file
file << "BufferLength: " << bufferLength << std::endl;
file << "NumFrames: " << numFrames << std::endl;
file << "OffsetInput: " << offsetInput << std::endl;
file << "UseMean: " << useMean << std::endl;
file << "UseStdDev: " << useStdDev << std::endl;
file << "UseEuclideanNorm: " << useEuclideanNorm << std::endl;
file << "UseRMS: " << useRMS << std::endl;
return true;
}
bool RBMQuantizer::save( std::fstream &file ) const{
if( !file.is_open() ){
errorLog << "save(fstream &file) - The file is not open!" << std::endl;
return false;
}
//Write the header
file << "RBM_QUANTIZER_FILE_V1.0" << std::endl;
//Save the base feature extraction settings to the file
if( !saveFeatureExtractionSettingsToFile( file ) ){
errorLog << "saveFeatureExtractionSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << std::endl;
return false;
}
file << "QuantizerTrained: " << trained << std::endl;
file << "NumClusters: " << numClusters << std::endl;
if( trained ){
if( !rbm.save( file ) ){
errorLog << "save(fstream &file) - Failed to save RBM settings to file!" << std::endl;
return false;
}
}
return true;
}
bool PostProcessing::loadPostProcessingSettingsFromFile(std::fstream &file){
if( !file.is_open() ){
errorLog << "loadPostProcessingSettingsFromFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
//Try and load the base settings from the file
if( !MLBase::loadBaseSettingsFromFile( file ) ){
return false;
}
std::string word;
//Load if the filter has been initialized
file >> word;
if( word != "Initialized:" ){
errorLog << "loadPostProcessingSettingsFromFile(fstream &file) - Failed to read Initialized header!" << std::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;
}
bool LinearRegression::saveModelToFile( std::fstream &file ) const{
if(!file.is_open())
{
errorLog << "loadModelFromFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
//Write the header info
file<<"GRT_LINEAR_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!" << std::endl;
return false;
}
if( trained ){
file << "Weights: ";
file << w0;
for(UINT j=0; j<numInputDimensions; j++){
file << " " << w[j];
}
file << std::endl;
}
return true;
}
void read_matrix_size(std::fstream& f, std::size_t & sz1, std::size_t & sz2)
{
if(!f.is_open())
throw std::invalid_argument("File is not opened");
f >> sz1 >> sz2;
}
int main(int argc, char *argv[]) {
//Initialize symbol table code. Pin does not read symbols unless this is called
PIN_InitSymbols();
//initialize Pin system
if(PIN_Init(argc,argv)) {
return Usage();
}
string filename = KnobOutputFile.Value();
//file to record all instructions
#ifdef LOG_ASSEM
//TraceFile.open(filename, ios::out);
AxOpenFile(TraceFile, filename);
if (TraceFile.is_open()) {
PRINT_SCN(filename << " : Start to make trace at instruction #" << InsCount);
} else {
PRINT_SCN("cannot open");
return -1;
}
#endif
//file to record all memory accesses
MemFile.open("mem.txt", ios::out);
//add a function used to instrument at instruction granularity
INS_AddInstrumentFunction(Instruction, 0);
//call 'Fini' immediately before the application exits
PIN_AddFiniFunction(Fini, 0);
//starts executing the application
PIN_StartProgram();
return 0;
}
bool TimeseriesBuffer::load( std::fstream &file ){
if( !file.is_open() ){
errorLog << "load(fstream &file) - The file is not open!" << std::endl;
return false;
}
std::string word;
//Load the header
file >> word;
if( word != "GRT_TIMESERIES_BUFFER_FILE_V1.0" ){
errorLog << "load(fstream &file) - Invalid file format!" << std::endl;
return false;
}
if( !loadFeatureExtractionSettingsFromFile( file ) ){
errorLog << "loadFeatureExtractionSettingsFromFile(fstream &file) - Failed to load base feature extraction settings from file!" << std::endl;
return false;
}
file >> word;
if( word != "BufferSize:" ){
errorLog << "load(fstream &file) - Failed to read BufferSize header!" << std::endl;
return false;
}
file >> bufferSize;
//Init the TimeseriesBuffer module to ensure everything is initialized correctly
return init(bufferSize,numInputDimensions);
}
bool FFT::loadModelFromFile( std::fstream &file ){
if( !file.is_open() ){
errorLog << "loadModelFromFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
std::string word;
//Load the header
file >> word;
if( word != "GRT_FFT_FILE_V1.0" ){
errorLog << "loadModelFromFile(fstream &file) - Invalid file format!" << std::endl;
return false;
}
if( !loadFeatureExtractionSettingsFromFile( file ) ){
errorLog << "loadFeatureExtractionSettingsFromFile(fstream &file) - Failed to load base feature extraction settings from file!" << std::endl;
return false;
}
file >> word;
if( word != "HopSize:" ){
errorLog << "loadModelFromFile(fstream &file) - Failed to read HopSize header!" << std::endl;
return false;
}
file >> hopSize;
file >> word;
if( word != "FftWindowSize:" ){
errorLog << "loadModelFromFile(fstream &file) - Failed to read FftWindowSize header!" << std::endl;
return false;
}
file >> fftWindowSize;
file >> word;
if( word != "FftWindowFunction:" ){
errorLog << "loadModelFromFile(fstream &file) - Failed to read FftWindowFunction header!" << std::endl;
return false;
}
file >> fftWindowFunction;
file >> word;
if( word != "ComputeMagnitude:" ){
errorLog << "loadModelFromFile(fstream &file) - Failed to read ComputeMagnitude header!" << std::endl;
return false;
}
file >> computeMagnitude;
file >> word;
if( word != "ComputePhase:" ){
errorLog << "loadModelFromFile(fstream &file) - Failed to read ComputePhase header!" << std::endl;
return false;
}
file >> computePhase;
//Init the FFT module to ensure everything is initialized correctly
return init(fftWindowSize,hopSize,numInputDimensions,fftWindowFunction,computeMagnitude,computePhase);
}
bool DecisionTreeClusterNode::loadParametersFromFile( std::fstream &file ){
if(!file.is_open())
{
errorLog << __GRT_LOG__ << " File is not open!" << std::endl;
return false;
}
//Load the DecisionTreeNode parameters
if( !DecisionTreeNode::loadParametersFromFile( file ) ){
errorLog << __GRT_LOG__ << " Failed to load DecisionTreeNode parameters from file!" << std::endl;
return false;
}
std::string word;
//Load the custom DecisionTreeThresholdNode Parameters
file >> word;
if( word != "FeatureIndex:" ){
errorLog << __GRT_LOG__ << " Failed to find FeatureIndex header!" << std::endl;
return false;
}
file >> featureIndex;
file >> word;
if( word != "Threshold:" ){
errorLog << __GRT_LOG__ << " Failed to find Threshold header!" << std::endl;
return false;
}
file >> threshold;
return true;
}
bool FFT::saveModelToFile( std::fstream &file ) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
//Write the file header
file << "GRT_FFT_FILE_V1.0" << std::endl;
//Save the base settings to the file
if( !saveFeatureExtractionSettingsToFile( file ) ){
errorLog << "saveFeatureExtractionSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << std::endl;
return false;
}
//Write the FFT settings
file << "HopSize: " << hopSize << std::endl;
file << "FftWindowSize: " << fftWindowSize << std::endl;
file << "FftWindowFunction: " << fftWindowFunction << std::endl;
file << "ComputeMagnitude: " << computeMagnitude << std::endl;
file << "ComputePhase: " << computePhase << std::endl;
return true;
}
bool FFTFeatures::saveModelToFile( std::fstream &file ) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
//Write the file header
file << "GRT_FFT_FEATURES_FILE_V1.0" << std::endl;
//Save the base settings to the file
if( !saveFeatureExtractionSettingsToFile( file ) ){
errorLog << "saveFeatureExtractionSettingsToFile(fstream &file) - Failed to save base feature extraction settings to file!" << std::endl;
return false;
}
//Write the FFT features settings
file << "FFTWindowSize: " << fftWindowSize << std::endl;
file << "NumChannelsInFFTSignal: " << numChannelsInFFTSignal << std::endl;
file << "ComputeMaxFreqFeature: " << computeMaxFreqFeature << std::endl;
file << "ComputeMaxFreqSpectrumRatio: " << computeMaxFreqSpectrumRatio << std::endl;
file << "ComputeCentroidFeature: " << computeCentroidFeature << std::endl;
file << "ComputeTopNFreqFeatures: " << computeTopNFreqFeatures << std::endl;
file << "N: " << N << std::endl;
return true;
}
bool Softmax::save( std::fstream &file ) const{
if(!file.is_open())
{
errorLog << __GRT_LOG__ << " The file is not open!" << std::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 << __GRT_LOG__ << " Failed to save classifier base settings to file!" << std::endl;
return false;
}
if( trained ){
file << "Models:\n";
for(UINT k=0; k<numClasses; k++){
file << "ClassLabel: " << models[k].classLabel << std::endl;
file << "Weights: " << models[k].w0;
for(UINT n=0; n<numInputDimensions; n++){
file << " " << models[k].w[n];
}
file << std::endl;
}
}
return true;
}
bool KMeans::saveModelToFile( std::fstream &file ) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(fstream &file) - Failed to save model, file is not open!" << std::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!" << std::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 << std::endl;
}
}
return true;
}
bool HierarchicalClustering::saveModelToFile( std::fstream &file ) const{
if( !file.is_open() ){
errorLog << "saveModelToFile(string filename) - Failed to open file!" << std::endl;
return false;
}
file << "GRT_HIERARCHICAL_CLUSTERING_FILE_V1.0\n";
if( !saveClustererSettingsToFile( file ) ){
errorLog << "saveModelToFile(fstream &file) - Failed to save cluster settings to file!" << std::endl;
return false;
}
if( trained ){
file << "M: " << M << std::endl;
file << "N: " << N << std::endl;
file << "NumLevels: " << clusters.getSize() << std::endl;
for(UINT i=0; i<clusters.getSize(); i++){
file << "Level: " << clusters[i].getLevel() << std::endl;
file << "NumClusters: " << clusters[i].getNumClusters() << std::endl;
}
}
return true;
}
void check_is_file_open( const std::fstream& file, const char *path)
{
if ( !file.is_open() )
{
std::cerr << "Failed to open file " << path << std::endl;
exit( 1 );
}
}
void PcieAccessInterfaceTest::write_file(char* data, const std::string& path, uint32_t size, uint32_t offset) {
memory_file.open(path, std::ios::out |std::ios::binary | std::ios::trunc);
if (memory_file.is_open()) {
memory_file.seekg(offset, std::ios::beg);
memory_file.write(data, size);
memory_file.close();
}
}
int deserialize(hash_map<K,V> &map, std::fstream& in) {
if(!in.is_open())
throw serialize_exception();
// TODO
return -1;
}
int serialize(hash_map<K,V>& map, std::fstream& out) {
if(!out.is_open())
throw serialize_exception();
// TODO
return -1;
}
bool BernoulliRBM::saveModelToFile( std::fstream &file ) const{
if(!file.is_open())
{
errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << std::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!" << std::endl;
return false;
}
file << "NumVisibleUnits: " << numVisibleUnits << std::endl;
file << "NumHiddenUnits: " << numHiddenUnits << std::endl;
file << "BatchSize: " << batchSize << std::endl;
file << "BatchStepSize: " << batchStepSize << std::endl;
file << "LearningRate: " << learningRate << std::endl;
file << "LearningRateUpdate: " << learningRateUpdate << std::endl;
file << "Momentum: " << momentum << std::endl;
file << "RandomizeWeightsForTraining: " << randomizeWeightsForTraining << std::endl;
file << "Ranges: \n";
for(UINT n=0; n<ranges.size(); n++){
file << ranges[n].minValue << "\t" << ranges[n].maxValue << std::endl;
}
//If the model has been trained then write the model
if( trained ){
file << "WeightsMatrix: " << std::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 << std::endl;
}
file << "VisibleLayerBias: ";
for(unsigned int i=0; i<visibleLayerBias.size(); i++){
file << visibleLayerBias[i];
if( i < visibleLayerBias.size()-1 ) file << " ";
}
file << std::endl;
file << "HiddenLayerBias: ";
for(unsigned int i=0; i<hiddenLayerBias.size(); i++){
file << hiddenLayerBias[i];
if( i < hiddenLayerBias.size()-1 ) file << " ";
}
file << std::endl;
}
return true;
}
bool ParticleClassifier::saveModelToFile( std::fstream &file ) const{
if(!file.is_open())
{
errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << std::endl;
return false;
}
return true;
}
NMPRKC_API nmprk_status_t NMPRK_StopDebugLogging()
{
if(si_fsDebugLog.is_open() == true)
{
SI_DEBUG_INFO(SI_THIS_MODULE, "Debug Logging Stopped");
si_fsDebugLog.close();
}
return NMPRK_SUCCESS;
}
void load_terran() {
terrain_stream.open("Data/Terrain.raw", std::ios::out | std::ios::binary | std::ios::in);
if (terrain_stream.is_open()) {
fprintf(stdout, "Terrain file open success\n");
}
else {
fprintf(stderr, "Terrain file open fail\n");
}
terrain_stream.read((char*)terrain, MAP_SIZE * MAP_SIZE);
}
bool MovementDetector::loadModelFromFile( std::fstream &file ){
clear();
if(!file.is_open())
{
errorLog << "loadModelFromFile(string filename) - Could not open file to load model!" << std::endl;
return false;
}
std::string word;
file >> word;
//Write the header info
if( word != "GRT_MOVEMENT_DETECTOR_MODEL_FILE_V1.0" ){
errorLog <<"loadModelFromFile(fstream &file) - Failed to read file header!" << std::endl;
return false;
}
//Load the base settings from the file
if( !MLBase::loadBaseSettingsFromFile(file) ){
errorLog << "loadModelFromFile(string filename) - Failed to load base settings from file!" << std::endl;
return false;
}
file >> word;
if( word != "SearchTimeout:" ){
errorLog <<"loadModelFromFile(fstream &file) - Failed to read SearchTimeout header!" << std::endl;
return false;
}
file >> searchTimeout;
file >> word;
if( word != "UpperThreshold:" ){
errorLog <<"loadModelFromFile(fstream &file) - Failed to read UpperThreshold header!" << std::endl;
return false;
}
file >> upperThreshold;
file >> word;
if( word != "LowerThreshold:" ){
errorLog <<"loadModelFromFile(fstream &file) - Failed to read LowerThreshold header!" << std::endl;
return false;
}
file >> lowerThreshold;
file >> word;
if( word != "Gamma:" ){
errorLog <<"loadModelFromFile(fstream &file) - Failed to read Gamma header!" << std::endl;
return false;
}
file >> gamma;
return true;
}
bool BAG::save( std::fstream &file ) const{
if(!file.is_open())
{
errorLog <<"save(fstream &file) - The file is not open!" << std::endl;
return false;
}
const UINT ensembleSize = getEnsembleSize();
//Write the header info
file << "GRT_BAG_MODEL_FILE_V2.0\n";
//Write the classifier settings to the file
if( !Classifier::saveBaseSettingsToFile(file) ){
errorLog <<"save(fstream &file) - Failed to save classifier base settings to file!" << std::endl;
return false;
}
if( trained ){
file << "EnsembleSize: " << ensembleSize << std::endl;
if( getEnsembleSize() > 0 ){
//Save the weights
file << "Weights: ";
for(UINT i=0; i<getEnsembleSize(); i++){
file << weights[i];
if( i < ensembleSize-1 ) file << "\t";
else file << "\n";
}
//Save the classifier types
file << "ClassifierTypes: ";
for(UINT i=0; i<getEnsembleSize(); i++){
file << ensemble[i]->getClassifierType() << std::endl;
}
//Save the ensemble
file << "Ensemble: \n";
for(UINT i=0; i<getEnsembleSize(); i++){
if( !ensemble[i]->save( file ) ){
errorLog <<"save(fstream &file) - Failed to save classifier " << i << " to file!" << std::endl;
return false;
}
}
}
}
//NOTE: We do not need to close the file
return true;
}
void read_vector_body(std::fstream& f, std::vector<ScalarType>& v) {
if(!f.is_open())
throw std::invalid_argument("File is not opened");
for(std::size_t i = 0; i < v.size(); i++)
{
ScalarType val = 0.0;
f >> val;
v[i] = val;
}
}
