/**
* Função contruída com o objetivo de facilitar o treinamento de uma rede. Utiliza critérios
* de parada pré-definidos. O objetivo é paralizar o treinamento a partir do momento em que o
* erro médio quadrático da rede em relação às amostras para de diminuir. Recebe um parâmetro
* indicando um número mínimo de treinos, a a partir do qual se inicia a verificação da variaçao
* do erro médio quadrático. Recebe também o número de treinamentos a ser executado até que uma
* nova medição do erro seja feita. Caso a variância (porcentual) das últimas n medições seja
* menor ou igual a um determinado valor (entre 0 e 1), paraliza o treinamento.
* A função recebe ainda um conjunto de amostras (matriz de entradas/matriz de saídas), número
* de amostras contidas nas matrizes, a dimensão de cada amostra de entrada e de cada amostra de
* saída e um flag indicando se as amostras devem ser treinadas aleatoriamente ou em ordem.
*/
int BKPNeuralNet::AutoTrain( float**inMatrix, float **outMatrix, int inSize, int outSize, int nSamples,
int minTrains, int varVectorSize, float minStdDev, int numTrains, TrainType type,
float l_rate, float momentum, int* retExecutedTrains )
{
// Casos de retorno:
if( (!inMatrix) || (!outMatrix) || (inSize!=_nLayers[0]) || (_nLayers[_layers-1]!=outSize) )
return -1;
// O número de treinamentos inicial tem que ser pelo menos 0:
if( *retExecutedTrains < 0 )
*retExecutedTrains = 0;
int thisSample = -1; //< Variável auxiliar, indica a amostra a ser treinada.
// Executando os treinamentos obrigatórios:
for( int i=0 ; i<minTrains ; i++ )
{
if( type == ORDERED_TRAIN )
thisSample = (++thisSample)%nSamples;
if( type == RANDOM_TRAIN )
thisSample = RandInt(0, (nSamples-1));
Train( inSize, inMatrix[thisSample], outSize, outMatrix[thisSample], l_rate, momentum );
}
// Executando os demais treinamentos:
float* varVector = new float[varVectorSize]; //< Vetor para conter as últimas medições de erro.
int ptVarVector = 0; //< Aponta para a primeira posição vazia de varVector.
float lastVariance = (float)MAX_VALUE; //< Variâvel que mantém o valor da varirância.
float StdDev = (float)MAX_VALUE; //< Variâvel que mantém o valor do desvio-padrão.
thisSample = -1;
int nTrains=minTrains + *retExecutedTrains; //< Mantém o número de treinamentos executados.
bool varFlag = false;
while( StdDev > minStdDev )
{
if( type == ORDERED_TRAIN )
thisSample = (++thisSample)%nSamples;
if( type == RANDOM_TRAIN )
thisSample = RandInt(0, (nSamples-1));
Train( inSize, inMatrix[thisSample], outSize, outMatrix[thisSample], l_rate, momentum );
if( (nTrains%numTrains) == 0 ) //< A cada numTrains treinamentos, testa o erro:
{
float retRMS_Error = 0;
float mean = 0;
RMS_error( inMatrix, outMatrix, inSize, outSize, nSamples, &retRMS_Error );
varFlag = ShiftLeft( varVector, varVectorSize, retRMS_Error, ptVarVector );
if( varFlag == true )
{
lastVariance = Variance( varVector, varVectorSize, &mean );
StdDev = ((float)sqrt(lastVariance))/mean;
}
ptVarVector++;
}
nTrains++;
if( nTrains >= 90000 ) //< O número máximo de treinamentos será 150000.
StdDev = minStdDev;
}
*retExecutedTrains = nTrains;
return 0;
}
RNN<LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::RNN(LayerType &&network,
OutputType &&outputLayer,
const arma::mat& predictors,
const arma::mat& responses,
InitializationRuleType initializeRule,
PerformanceFunction performanceFunction) :
network(std::forward<LayerType>(network)),
outputLayer(std::forward<OutputType>(outputLayer)),
performanceFunc(std::move(performanceFunction)),
inputSize(0),
outputSize(0)
{
static_assert(std::is_same<typename std::decay<LayerType>::type,
LayerTypes>::value,
"The type of network must be LayerTypes.");
static_assert(std::is_same<typename std::decay<OutputType>::type,
OutputLayerType>::value,
"The type of outputLayer must be OutputLayerType.");
initializeRule.Initialize(parameter, NetworkSize(this->network), 1);
NetworkWeights(parameter, this->network);
Train(predictors, responses);
}
NaiveBayesClassifier<MatType>::NaiveBayesClassifier(
const MatType& data,
const arma::Row<size_t>& labels,
const size_t classes,
const bool incremental) :
trainingPoints(0) // Set when we call Train().
{
const size_t dimensionality = data.n_rows;
// Perform training, after initializing the model to 0 (that is, if Train()
// won't do that for us, which it won't if we're using the incremental
// algorithm).
if (incremental)
{
probabilities.zeros(classes);
means.zeros(dimensionality, classes);
variances.zeros(dimensionality, classes);
}
else
{
probabilities.set_size(classes);
means.set_size(dimensionality, classes);
variances.set_size(dimensionality, classes);
}
Train(data, labels, incremental);
}
xml::XMLElement* DecisionTree::loadXMLSettings(xml::XMLElement* elem)
{
xml::XMLElement*e;
e=elem->getSubElement(mT("scheme"));
if(e)
{
if(m_scheme)
delete m_scheme;
m_scheme=new AttributesScheme();
m_scheme->loadXMLSettings(e);
}
e=elem->getSubElement(mT("training"));
if(e)
{
xml::XMLAttribute*attr=e->getAttribute(mT("Target"));
if(!attr)
return elem;
TrainingDataSet ds(m_scheme);
ds.loadExamplesFromXML(e);
Train(&ds,attr->value);
//test
xml::XMLElement e(mT("DT"));
exportXMLSettings(&e);
GCPtr<OS::IStream> stream=gFileSystem.openFile(mT("IDT.xml"),OS::TXT_WRITE);
xml::XMLWriter w;
w.addElement(&e);
OS::StreamWriter ww(stream);
ww.writeString(w.flush());
stream->close();
}
return elem;
}
/* Main function */
int main(int argc, char** argv){
char* trainingFile, * trainingTargetFile, * testingFile;
double duration = 0;
verbose = 0;
if(getenv("VERBOSE") != 0)
{
verbose = atoi(getenv("VERBOSE"));
}
/* read num inputs/outputs nodes */
numInputNodes = atoi(argv[1]);
numOutputNodes = atoi(argv[2]);
numTrainingSamples = atoi(argv[3]);
numTestSamples = atoi(argv[4]);
/* read the number of Hidden layers in net */
numHiddenLayers = atoi(argv[5]);
neuronsPerLayer = atoi(argv[6]);
/* read learning rate */
learningRate = atof(argv[7]);
/* read testing data file */
testingFile = argv[8];
/* read training data file */
trainingFile = argv[9];
/* read training target data */
trainingTargetFile = argv[10];
/* initialize the neural network */
InitNeuralNet();
InitSamples(numTrainingSamples, numTestSamples);
ReadFile(trainingFile, numInputNodes, numTrainingSamples, trainingSamples);
ReadFile(trainingTargetFile, numOutputNodes, numTrainingSamples, trainingTargets);
ReadFile(testingFile, numInputNodes, numTestSamples, testSamples);
/* train the neural network */
timerStart();
Train();
duration = timerStop();
printf("Duration: %f seconds\n", (duration));
Test();
cleanUp();
return 0;
}
LogisticRegression<MatType>::LogisticRegression(
OptimizerType<LogisticRegressionFunction<MatType>>& optimizer) :
parameters(optimizer.Function().GetInitialPoint()),
lambda(optimizer.Function().Lambda())
{
Train(optimizer);
}
void Train(InputType& trainingData,
OutputType& trainingLabels,
InputType& validationData,
OutputType& validationLabels)
{
// This generates [0 1 2 3 ... (ElementCount(trainingData) - 1)]. The
// sequence will be used to iterate through the training data.
index = arma::linspace<arma::Col<size_t> >(0,
ElementCount(trainingData) - 1, ElementCount(trainingData));
epoch = 0;
while(true)
{
if (shuffle)
index = arma::shuffle(index);
Train(trainingData, trainingLabels);
Evaluate(validationData, validationLabels);
if (validationError <= tolerance)
break;
if (maxEpochs > 0 && ++epoch >= maxEpochs)
break;
}
}
void Train() {
clock_t t;
t = clock();
for (int index = 0 ; index < featureSize ; index++)
Train(index);
t = clock() - t;
journal << "Computing classifiers locally " << negCount + posCount << " times: " << ((float)t)/CLOCKS_PER_SEC << "seconds.\n";
}
AdaBoost<MatType, WeakLearner>::AdaBoost(
const MatType& data,
const arma::Row<size_t>& labels,
const WeakLearner& other,
const size_t iterations,
const double tol)
{
Train(data, labels, other, iterations, tol);
}
SoftmaxRegression<OptimizerType>::SoftmaxRegression(
OptimizerType<SoftmaxRegressionFunction>& optimizer) :
parameters(optimizer.Function().GetInitialPoint()),
numClasses(optimizer.Function().NumClasses()),
lambda(optimizer.Function().Lambda()),
fitIntercept(optimizer.Function().FitIntercept())
{
Train(optimizer);
}
LogisticRegression<MatType>::LogisticRegression(
const MatType& predictors,
const arma::Row<size_t>& responses,
const double lambda) :
parameters(arma::zeros<arma::vec>(predictors.n_rows + 1)),
lambda(lambda)
{
Train(predictors, responses);
}
int OnlineSVR::Train (double**X, double *Y, int ElementsNumber, int ElementsSize)
{
Matrix<double>* NewX = new Matrix<double>(X, ElementsNumber, ElementsSize);
Vector<double>* NewY = new Vector<double>(Y, ElementsNumber);
int Flops = Train(NewX,NewY);
delete NewX;
delete NewY;
return Flops;
}
//=================================================================================================
void PlayerController::TrainMove(float dt, bool run)
{
move_tick += (run ? dt : dt / 10);
if(move_tick >= 1.f)
{
move_tick -= 1.f;
Train(TrainWhat::Move, 0.f, 0);
}
}
//=================================================================================================
void PlayerController::Rest(int days, bool resting, bool travel)
{
// update effects that work for days, end other
int best_nat;
float prev_hp = unit->hp,
prev_stamina = unit->stamina;
unit->EndEffects(days, &best_nat);
// regenerate hp
if(unit->hp != unit->hpmax)
{
float heal = 0.5f * unit->Get(Attribute::END);
if(resting)
heal *= 2;
if(best_nat)
{
if(best_nat != days)
heal = heal*best_nat * 2 + heal*(days - best_nat);
else
heal *= 2 * days;
}
else
heal *= days;
heal = min(heal, unit->hpmax - unit->hp);
unit->hp += heal;
Train(Attribute::END, int(heal));
}
// send update
Game& game = Game::Get();
if(Net::IsOnline() && !travel)
{
if(unit->hp != prev_hp)
{
NetChange& c = Add1(Net::changes);
c.type = NetChange::UPDATE_HP;
c.unit = unit;
}
if(unit->stamina != prev_stamina && this != game.pc)
game.GetPlayerInfo(this).update_flags |= PlayerInfo::UF_STAMINA;
}
// reset last damage
last_dmg = 0;
last_dmg_poison = 0;
dmgc = 0;
poison_dmgc = 0;
// reset action
action_cooldown = 0;
action_recharge = 0;
action_charges = GetAction().charges;
}
LogisticRegression<MatType>::LogisticRegression(
const MatType& predictors,
const arma::Row<size_t>& responses,
OptimizerType& optimizer,
const double lambda) :
parameters(arma::rowvec(predictors.n_rows + 1, arma::fill::zeros)),
lambda(lambda)
{
Train(predictors, responses, optimizer);
}
MachineABC* LinearRegressorTrainer::Train(DataSet* pData)
{
RegressorTrainerABC::SetData (pData);
LinearRegressor* pRegressor=new LinearRegressor;
if (!Train (pRegressor)){
delete pRegressor;
pRegressor=0;
}
return pRegressor;
}
bool Layer::Train(const NNetData& data) {
for (std::size_t iter = 0; iter < options_.max_iterations; ++iter) {
VLOG(2) << "iteration#" << iter;
for (std::size_t i = 0; i < data.size(); ++i) {
VLOG(4) << "instance#" << i;
Train(data.input(i), data.label(i));
}
}
return true;
}
LogisticRegression<MatType>::LogisticRegression(
const MatType& predictors,
const arma::Row<size_t>& responses,
const arma::vec& initialPoint,
const double lambda) :
parameters(initialPoint),
lambda(lambda)
{
Train(predictors, responses);
}
double SoftmaxRegression<OptimizerType>::Train(const arma::mat& data,
const arma::Row<size_t>& labels,
const size_t numClasses)
{
SoftmaxRegressionFunction regressor(data, labels, numClasses,
lambda, fitIntercept);
OptimizerType<SoftmaxRegressionFunction> optimizer(regressor);
return Train(optimizer);
}
/*****************************************************************************************
* int K_MeansPredict::k_FoldXV( const vector< vector< float > >& Ex, const float stopDist, const int stopIter )
* purpose:
* performs k-fold cross validation using training examples
*
* 03.06.2006 djh eliminated reassignment of _totalErrMean
* 03.07.2006 djh changed this_model_err += err;
* to this_model_err += pow(err,2);
* because old way allowed model with large negative error
* to be selected as best
* 03.08.2006 djh replaced _totalUpper/_totalLowerConfBound with _totalBoundStub
*****************************************************************************************/
int K_MeansPredict::k_FoldXV( const vector< vector< float > >& Ex, const float stopDist, const int stopIter ){
// number of folds
int nFolds = 10;
//Divide Examples into folds
int chunk = Ex.size()/nFolds;
float sum_err=0.;
float sum_err2=0.;
K_MeansPredict bestModel;
float best_model_err;
for( int i=0; i<nFolds; i++ ){
cout << "# Fold " << i << endl;
// Don't have to reset means, Train does that for us
vector< vector< float > > TestSet;
vector< vector< float > > TrainSet;
float this_model_err=0;
for( int j=0; j<Ex.size(); j++ ){
if( j>=i*chunk && j<(i+1)*chunk ){
//cout << "Example " << j << "added to test set\n";
TestSet.push_back( Ex[j] );
}
else{
TrainSet.push_back( Ex[j] );
//cout << "Example " << j << "added to train set\n";
}
}
Train( TrainSet, stopDist, stopIter, 0 );
for( int j=0; j<TestSet.size(); j++ ){
float err = EvaluatePattern( TestSet[j] )-TestSet[j][0]; // first element is target value
this_model_err += pow(err,2);
sum_err += err;
sum_err2 += pow( err,2 );
}
if( i==0 || this_model_err < best_model_err ){
best_model_err = this_model_err;
bestModel = *this;
}
}
*this = bestModel;
//bestPercep.Output( cout );
//_totalErrMean = sum_err/( Ex.size() );
float errVar = (sum_err2 - (sum_err/float(Ex.size())))/float(Ex.size()-1);
cout << "# 10-fold x-validation:\n";
cout << "# Error:\n";
cout << "# Mean of Squared Errors (MSE) is : " << sum_err2/(float(Ex.size()) ) << endl; // 01/07/2006
cout << "# Error Mean is : " << _totalErrMean << endl;
cout << "# Error variance is: " << errVar << endl;
float t_val = 1.960; // value t_(alpha/2,v-1) for alpha = 0.05 v=large
// this gives the t value for a 95% confidence
//_totalLowerConfBound = _totalErrMean - t_val*sqrt( errVar * (1.0+( 1.0/float(Ex.size()) )) );
//_totalUpperConfBound = _totalErrMean + t_val*sqrt( errVar * (1.0+( 1.0/float(Ex.size()) )) );
_totalBoundStub = sqrt( errVar * (1.0+( 1.0/float(Ex.size()) )) );
}
Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Perceptron(
const Perceptron& other,
const MatType& data,
const arma::Row<size_t>& labels,
const arma::rowvec& instanceWeights) :
maxIterations(other.maxIterations)
{
// Insert a row of ones at the top of the training data set.
WeightInitializationPolicy wip;
wip.Initialize(weights, biases, data.n_rows, other.NumClasses());
Train(data, labels, instanceWeights);
}
Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Perceptron(
const MatType& data,
const arma::Row<size_t>& labels,
const size_t numClasses,
const size_t maxIterations) :
maxIterations(maxIterations)
{
WeightInitializationPolicy wip;
wip.Initialize(weights, biases, data.n_rows, numClasses);
// Start training.
Train(data, labels);
}
MainWindow::MainWindow(QWidget *parent) :
QMainWindow(parent),
ui(new Ui::MainWindow)
{
ui->setupUi(this);
this->faceDet = new facedetect();
this->recognize = new facerecognize();
QObject::connect(ui->PhotoDet_action, SIGNAL(triggered()), this, SLOT(PhotoDetec()));
QObject::connect(ui->Video_action, SIGNAL(triggered()), this, SLOT(VideoDetec()));
QObject::connect(ui->Train_action, SIGNAL(triggered()), this, SLOT(Train()));
QObject::connect(ui->Recog_action, SIGNAL(triggered()), this, SLOT(PhotoRecognize()));
}
int main(int argc, char** argv) {
if (!strcmp(argv[1], "--train") && argc == 4) {
Train(argv[2], argv[3]);
return EXIT_SUCCESS;
}
if (!strcmp(argv[1], "--interactive") && argc == 3) {
Interactive(argv[2]);
return EXIT_SUCCESS;
}
std::cerr << "Usage: " << argv[0] << " --train <dataset> <model>\n";
std::cerr << "Usage: " << argv[0] << " --interactive <model>\n";
return EXIT_FAILURE;
}
LocalCoordinateCoding::LocalCoordinateCoding(
const arma::mat& data,
const size_t atoms,
const double lambda,
const size_t maxIterations,
const double tolerance,
const DictionaryInitializer& initializer) :
atoms(atoms),
lambda(lambda),
maxIterations(maxIterations),
tolerance(tolerance)
{
// Train the model.
Train(data, initializer);
}
SoftmaxRegression<OptimizerType>::SoftmaxRegression(const arma::mat& data,
const arma::Row<size_t>& labels,
const size_t numClasses,
const double lambda,
const bool fitIntercept) :
numClasses(numClasses),
lambda(lambda),
fitIntercept(fitIntercept)
{
SoftmaxRegressionFunction regressor(data, labels, numClasses,
lambda, fitIntercept);
OptimizerType<SoftmaxRegressionFunction> optimizer(regressor);
parameters = regressor.GetInitialPoint();
Train(optimizer);
}
/* Main function */
int main(int argc, char** argv){
int** trainingData,** targetValues;
char* trainingFile, * trainingTargetFile, * testingFile;
/* read num inputs/outputs nodes */
neuronsPerLayer = atoi(argv[1]);
numOutputNodes = atoi(argv[2]);
numTrainingSamples = atoi(argv[3]);
numTestSamples = atoi(argv[4]);
/* read the number of Hidden layers in net */
numHiddenLayers = atoi(argv[5]);
/* read learning rate */
learningRate = atof(argv[6]);
/* read testing data file */
testingFile = argv[7];
/* read training data file */
trainingFile = argv[8];
/* read training target data */
trainingTargetFile = argv[9];
/* initialize the neural network */
InitNeuralNet();
InitSamples(numTrainingSamples, numTestSamples);
ReadFile(trainingFile, neuronsPerLayer, numTrainingSamples, trainingSamples);
ReadFile(trainingTargetFile, numOutputNodes, numTrainingSamples, trainingTargets);
ReadFile(testingFile, neuronsPerLayer, numTestSamples, testSamples);
/* train the neural network */
Train();
Test();
return 0;
}
void cScenarioBallRL::NewCycleUpdate()
{
if (EnableTraining())
{
// finish recording tuple from previous cycle
RecordState(mCurrTuple.mStateEnd);
RecordEndFlags(mCurrTuple);
mCurrTuple.mReward = CalcReward(mCurrTuple);
// do something with the tuple
if (!mFirstCycle)
{
mTupleBuffer[mNumTuples] = mCurrTuple;
++mNumTuples;
if (mNumTuples == static_cast<int>(mTupleBuffer.size()))
{
Train();
double exp_rate = GetExpRate();
double exp_temp = GetExpTemp();
auto& ctrl = mBall.GetController();
ctrl->SetExpRate(exp_rate);
ctrl->SetExpTemp(exp_temp);
printf("\n");
printf("Exp Rate: %.3f\n", exp_rate);
printf("Exp Temp: %.3f\n", exp_temp);
}
}
// start recording new tuple
mCurrTuple.mStateBeg = mCurrTuple.mStateEnd;
RecordAction(mCurrTuple.mAction);
mCurrTuple.mActionLikelihood = GetActionLikelihood();
ClearFlags(mCurrTuple);
RecordBegFlags(mCurrTuple);
mFirstCycle = false;
}
}
// the actual training regiment
void DoSimulation( void )
{
// reset the network, train patterns and test performance. Repeat for desired number of runs
cerr << "run: ";
for ( int run = 0; run < gCALMAPI->CALMGetNumRuns(); run++ )
{
*(gCALMAPI->GetCALMLog()) << "\nRUN " << run << endl;
cerr << run << ' ';
// start clean
gCALMAPI->CALMReset( O_WT | O_TIME | O_WIN );
// record duration of simulation
gCALMAPI->CALMDuration( kStart );
*(gCALMAPI->GetCALMLog()) << "\nTRAINING" << endl;
// train the network on pattern file
Train();
*(gCALMAPI->GetCALMLog()) << "\nTESTING" << endl;
// reset winning node information as well as time-delay activations
// (the latter only applies if time-delay connections are used)
gCALMAPI->CALMReset( O_TIME | O_WIN );
// test the network on pattern file
Test( run );
// end time recording, display duration
gCALMAPI->CALMDuration( kEnd );
// print out the final weight configuration
gCALMAPI->CALMShowWeights();
}
cerr << endl;
#if PLOT3D
// stop 3D plotting
gCALMAPI->CALMEnd3DPlot();
#endif
}
void DriftModel::Record(double next_angle, double angle, double previous_angle, double velocity, double radius, double direction) {
// If next_angle is so small, we probably just started and we are not
// drifting any more. Doesn't make sense to record such entries.
if (fabs(next_angle) < 1e-5) return;
if (direction != 0) {
raw_points_turn_.push_back({angle, previous_angle, velocity, radius, direction, next_angle});
} else {
raw_points_straight_.push_back({angle, previous_angle, velocity, radius, direction, next_angle});
}
if (!very_ready_) {
Train();
}
double predicted = 0;
if (IsReady()) {
predicted = Predict(angle, previous_angle, velocity, radius, direction);
error_tracker_.Add(predicted, next_angle);
}
}
