Vec2d predict2(InputArray _sample, OutputArray _probs) const
{
int ptype = CV_64F;
Mat sample = _sample.getMat();
CV_Assert(isTrained());
CV_Assert(!sample.empty());
if(sample.type() != CV_64FC1)
{
Mat tmp;
sample.convertTo(tmp, CV_64FC1);
sample = tmp;
}
sample = sample.reshape(1, 1);
Mat probs;
if( _probs.needed() )
{
if( _probs.fixedType() )
ptype = _probs.type();
_probs.create(1, nclusters, ptype);
probs = _probs.getMat();
}
return computeProbabilities(sample, !probs.empty() ? &probs : 0, ptype);
}
bool MyFusion::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
ssi_size_t n_streams = samples.getStreamSize ();
if (n_streams != n_models) {
ssi_err ("#models (%u) differs from #streams (%u)", n_models, n_streams);
}
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
models[n_model]->train (samples, n_model);
}
}
_is_trained = true;
return true;
}
void FunctionApproximatorGPR::train(const MatrixXd& inputs, const MatrixXd& targets)
{
if (isTrained())
{
cerr << "WARNING: You may not call FunctionApproximatorGPR::train more than once. Doing nothing." << endl;
cerr << " (if you really want to retrain, call reTrain function instead)" << endl;
return;
}
assert(inputs.rows() == targets.rows());
assert(inputs.cols()==getExpectedInputDim());
const MetaParametersGPR* meta_parameters_gpr =
dynamic_cast<const MetaParametersGPR*>(getMetaParameters());
double max_covar = meta_parameters_gpr->maximum_covariance();
VectorXd sigmas = meta_parameters_gpr->sigmas();
// Compute the gram matrix
// In a gram matrix, every input point is itself a center
MatrixXd centers = inputs;
// Replicate sigmas, because they are the same for each data point/center
MatrixXd widths = sigmas.transpose().colwise().replicate(centers.rows());
MatrixXd gram(inputs.rows(),inputs.rows());
bool normalize_activations = false;
bool asymmetric_kernels = false;
BasisFunction::Gaussian::activations(centers,widths,inputs,gram,normalize_activations,asymmetric_kernels);
gram *= max_covar;
setModelParameters(new ModelParametersGPR(inputs,targets,gram,max_covar,sigmas));
}
void FunctionApproximatorGPR::predictVariance(const MatrixXd& inputs, MatrixXd& variances)
{
if (!isTrained())
{
cerr << "WARNING: You may not call FunctionApproximatorLWPR::predict if you have not trained yet. Doing nothing." << endl;
return;
}
const ModelParametersGPR* model_parameters_gpr = static_cast<const ModelParametersGPR*>(getModelParameters());
assert(inputs.cols()==getExpectedInputDim());
unsigned int n_samples = inputs.rows();
variances.resize(n_samples,1);
MatrixXd ks;
model_parameters_gpr->kernelActivations(inputs, ks);
double maximum_covariance = model_parameters_gpr->maximum_covariance();
MatrixXd gram_inv = model_parameters_gpr->gram_inv();
for (unsigned int ii=0; ii<n_samples; ii++)
variances(ii) = maximum_covariance - (ks.row(ii)*gram_inv).dot(ks.row(ii).transpose());
}
void MZTrafoModel::getCoefficients( double& intercept, double& slope, double& power )
{
if (!isTrained()) throw Exception::Precondition(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "Model is not trained yet.");
intercept = coeff_[0];
slope = coeff_[1];
power = coeff_[2];
}
void FunctionApproximatorIRFRLS::predict(const MatrixXd& input, MatrixXd& output)
{
if (!isTrained())
{
cerr << "WARNING: You may not call FunctionApproximatorIRFRLS::predict if you have not trained yet. Doing nothing." << endl;
return;
}
const ModelParametersIRFRLS* model_parameters_irfrls = static_cast<const ModelParametersIRFRLS*>(getModelParameters());
MatrixXd proj_inputs;
proj(input, model_parameters_irfrls->cosines_periodes_, model_parameters_irfrls->cosines_phase_, proj_inputs);
output = proj_inputs * model_parameters_irfrls->linear_models_;
}
void FunctionApproximatorIRFRLS::train(const MatrixXd& inputs, const MatrixXd& targets)
{
if (isTrained())
{
cerr << "WARNING: You may not call FunctionApproximatorIRFRLS::train more than once. Doing nothing." << endl;
cerr << " (if you really want to retrain, call reTrain function instead)" << endl;
return;
}
assert(inputs.rows() == targets.rows()); // Must have same number of examples
assert(inputs.cols()==getExpectedInputDim());
const MetaParametersIRFRLS* meta_parameters_irfrls =
static_cast<const MetaParametersIRFRLS*>(getMetaParameters());
int nb_cos = meta_parameters_irfrls->number_of_basis_functions_;
// Init random generator.
boost::mt19937 rng(getpid() + time(0));
// Draw periodes
boost::normal_distribution<> twoGamma(0, sqrt(2 * meta_parameters_irfrls->gamma_));
boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > genPeriods(rng, twoGamma);
MatrixXd cosines_periodes(nb_cos, inputs.cols());
for (int r = 0; r < nb_cos; r++)
for (int c = 0; c < inputs.cols(); c++)
cosines_periodes(r, c) = genPeriods();
// Draw phase
boost::uniform_real<> twoPi(0, 2 * boost::math::constants::pi<double>());
boost::variate_generator<boost::mt19937&, boost::uniform_real<> > genPhases(rng, twoPi);
VectorXd cosines_phase(nb_cos);
for (int r = 0; r < nb_cos; r++)
cosines_phase(r) = genPhases();
MatrixXd proj_inputs;
proj(inputs, cosines_periodes, cosines_phase, proj_inputs);
// Compute linear model analatically
double lambda = meta_parameters_irfrls->lambda_;
MatrixXd toInverse = lambda * MatrixXd::Identity(nb_cos, nb_cos) + proj_inputs.transpose() * proj_inputs;
VectorXd linear_model = toInverse.inverse() *
(proj_inputs.transpose() * targets);
setModelParameters(new ModelParametersIRFRLS(linear_model, cosines_periodes, cosines_phase));
}
bool MyModel::train (ISamples &samples,
ssi_size_t stream_index) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_classes = samples.getClassSize ();
_n_features = samples.getStream (stream_index).dim;
_centers = new ssi_real_t *[_n_classes];
for (ssi_size_t i = 0; i < _n_classes; i++) {
_centers[i] = new ssi_real_t[_n_features];
for (ssi_size_t j = 0; j < _n_features; j++) {
_centers[i][j] = 0;
}
}
ssi_sample_t *sample;
samples.reset ();
ssi_real_t *ptr = 0;
while (sample = samples.next ()) {
ssi_size_t id = sample->class_id;
ptr = ssi_pcast (ssi_real_t, sample->streams[stream_index]->ptr);
for (ssi_size_t j = 0; j < _n_features; j++) {
_centers[id][j] += ptr[j];
}
}
for (ssi_size_t i = 0; i < _n_classes; i++) {
ssi_size_t num = samples.getSize (i);
for (ssi_size_t j = 0; j < _n_features; j++) {
_centers[i][j] /= num;
}
}
return true;
}
void LayoutTest::testTrainer() {
//cv::Mat testM(10, 10, CV_8UC1);
//
//for (int rIdx = 0; rIdx < testM.rows; rIdx++) {
// unsigned char* ptr = testM.ptr<unsigned char>(rIdx);
// for (int cIdx = 0; cIdx < testM.cols; cIdx++) {
// ptr[cIdx] = cIdx*rIdx+cIdx;
// }
//}
//
//QJsonObject jo = Image::matToJson(testM);
//cv::Mat t2 = Image::jsonToMat(jo);
//cv::Scalar s = cv::sum(cv::abs(testM - t2));
//if (s[0] != 0)
// qWarning() << "inconsistent json2Mat I/O";
//else
// qInfo() << "json to mat is just fine...";
Timer dt;
FeatureCollectionManager fcm = FeatureCollectionManager::read(mConfig.featureCachePath());
// train classifier
SuperPixelTrainer spt(fcm);
if (!spt.compute())
qCritical() << "could not train data...";
spt.write(mConfig.classifierPath());
// read back the model
QSharedPointer<SuperPixelModel> model = SuperPixelModel::read(mConfig.classifierPath());
auto f = model->model();
if (f->isTrained())
qDebug() << "the classifier I loaded is trained...";
//qDebug() << fcm.numFeatures() << "SuperPixels trained in" << dt;
}
bool SimpleFusion::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
ssi_size_t n_streams = samples.getStreamSize ();
if (n_streams != 1 && n_streams != n_models) {
ssi_err ("#models (%u) differs from #streams (%u)", n_models, n_streams);
}
if (samples.hasMissingData ()) {
ISMissingData samples_h (&samples);
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
samples_h.setStream(n_streams == 1 ? 0 : n_model);
models[n_model]->train (samples_h, n_model);
}
}
} else {
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
models[n_model]->train(samples, n_streams == 1 ? 0 : n_model);
}
}
}
_is_trained = true;
return true;
}
bool MajorityVoting::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (samples.getStreamSize () != n_models) {
ssi_wrn ("#models (%u) differs from #streams (%u)", n_models, samples.getStreamSize ());
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_streams = samples.getStreamSize ();
_n_classes = samples.getClassSize ();
_n_models = n_models;
if (samples.hasMissingData ()) {
ISMissingData samples_h (&samples);
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
samples_h.setStream (n_model);
models[n_model]->train (samples_h, n_model);
}
}
}
else{
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) { models[n_model]->train (samples, n_model); }
}
}
return true;
}
bool SimpleKNN::train (ISamples &samples,
ssi_size_t stream_index) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (samples.getSize () < _options.k) {
ssi_wrn ("sample list has less than '%u' entries", _options.k);
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_classes = samples.getClassSize ();
_n_samples = samples.getSize ();
_n_features = samples.getStream (stream_index).dim;
_data = new ssi_real_t[_n_features*_n_samples];
_classes = new ssi_size_t[_n_samples];
ssi_sample_t *sample;
samples.reset ();
ssi_real_t *data_ptr = _data;
ssi_size_t *class_ptr = _classes;
ssi_stream_t *stream_ptr = 0;
ssi_size_t bytes_to_copy = _n_features * sizeof (ssi_real_t);
while (sample = samples.next ()) {
memcpy (data_ptr, sample->streams[stream_index]->ptr, bytes_to_copy);
*class_ptr++ = sample->class_id;
data_ptr += _n_features;
}
return true;
}
void FunctionApproximatorGPR::predict(const MatrixXd& inputs, MatrixXd& outputs)
{
if (!isTrained())
{
cerr << "WARNING: You may not call FunctionApproximatorLWPR::predict if you have not trained yet. Doing nothing." << endl;
return;
}
const ModelParametersGPR* model_parameters_gpr = static_cast<const ModelParametersGPR*>(getModelParameters());
assert(inputs.cols()==getExpectedInputDim());
unsigned int n_samples = inputs.rows();
outputs.resize(n_samples,1);
MatrixXd ks;
model_parameters_gpr->kernelActivations(inputs, ks);
VectorXd weights = model_parameters_gpr->weights();
for (unsigned int ii=0; ii<n_samples; ii++)
outputs(ii) = ks.row(ii).dot(weights);
}
bool SVM::train (ISamples &samples,
ssi_size_t stream_index) {
if (_options.seed > 0) {
srand(_options.seed);
} else {
srand(ssi_time_ms());
}
ISamples *s_balance = 0;
switch (_options.balance) {
case BALANCE::OFF: {
s_balance = &samples;
break;
}
case BALANCE::OVER: {
s_balance = new ISOverSample(&samples);
ssi_pcast(ISOverSample, s_balance)->setOver(ISOverSample::RANDOM);
ssi_msg(SSI_LOG_LEVEL_BASIC, "balance training set '%u' -> '%u'", samples.getSize(), s_balance->getSize());
break;
}
case BALANCE::UNDER: {
s_balance = new ISUnderSample(&samples);
ssi_pcast(ISUnderSample, s_balance)->setUnder(ISUnderSample::RANDOM);
ssi_msg(SSI_LOG_LEVEL_BASIC, "balance training set '%u' -> '%u'", samples.getSize(), s_balance->getSize());
break;
}
}
_n_samples = s_balance->getSize();
if (_n_samples == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_classes = s_balance->getClassSize();
_n_features = s_balance->getStream(stream_index).dim;
ssi_size_t elements = _n_samples * (_n_features + 1);
init_class_names(*s_balance);
_problem = new svm_problem;
_problem->l = ssi_cast (int, _n_samples);
_problem->y = new double[_problem->l];
_problem->x = new svm_node *[_problem->l];
s_balance->reset();
ssi_sample_t *sample;
int n_sample = 0;
float *ptr = 0;
svm_node *node = 0;
while (sample = s_balance->next()) {
ptr = ssi_pcast (float, sample->streams[stream_index]->ptr);
_problem->x[n_sample] = new svm_node[_n_features + 1];
_problem->y[n_sample] = ssi_cast (float, sample->class_id);
node = _problem->x[n_sample];
for (ssi_size_t nfeat = 0; nfeat < _n_features; nfeat++) {
node->index = nfeat+1;
node->value = *ptr;
ptr++;
++node;
}
node->index = -1;
++n_sample;
}
if(_options.params.gamma == 0 && _n_features > 0) {
_options.params.gamma = 1.0 / _n_features;
}
if (_options.params.kernel_type == PRECOMPUTED) {
int max_index = ssi_cast (int, _n_features);
for (int i = 0; i < _problem->l; i++) {
if (_problem->x[i][0].index != 0) {
ssi_err ("wrong input format: first column must be 0:sample_serial_number");
}
if ((int)_problem->x[i][0].value <= 0 || (int)_problem->x[i][0].value > max_index) {
ssi_err ("wrong input format: sample_serial_number out of range");
}
}
}
void FunctionApproximator::train(const MatrixXd& inputs, const MatrixXd& targets, string save_directory, bool overwrite)
{
train(inputs,targets);
if (save_directory.empty())
return;
if (!isTrained())
return;
if (getExpectedInputDim()<3)
{
VectorXd min = inputs.colwise().minCoeff();
VectorXd max = inputs.colwise().maxCoeff();
int n_samples_per_dim = 100;
if (getExpectedInputDim()==2) n_samples_per_dim = 40;
VectorXi n_samples_per_dim_vec = VectorXi::Constant(getExpectedInputDim(),n_samples_per_dim);
model_parameters_->saveGridData(min, max, n_samples_per_dim_vec, save_directory, overwrite);
}
MatrixXd outputs;
predict(inputs,outputs);
saveMatrix(save_directory,"inputs.txt",inputs,overwrite);
saveMatrix(save_directory,"targets.txt",targets,overwrite);
saveMatrix(save_directory,"outputs.txt",outputs,overwrite);
string filename = save_directory+"/plotdata.py";
ofstream outfile;
outfile.open(filename.c_str());
if (!outfile.is_open())
{
cerr << __FILE__ << ":" << __LINE__ << ":";
cerr << "Could not open file " << filename << " for writing." << endl;
}
else
{
// Python code generation in C++. Rock 'n' roll! ;-)
if (inputs.cols()==2) {
outfile << "from mpl_toolkits.mplot3d import Axes3D \n";
}
outfile << "import numpy \n";
outfile << "import matplotlib.pyplot as plt \n";
outfile << "directory = '" << save_directory << "' \n";
outfile << "inputs = numpy.loadtxt(directory+'/inputs.txt') \n";
outfile << "targets = numpy.loadtxt(directory+'/targets.txt') \n";
outfile << "outputs = numpy.loadtxt(directory+'/outputs.txt') \n";
outfile << "fig = plt.figure() \n";
if (inputs.cols()==2) {
outfile << "ax = Axes3D(fig) \n";
outfile << "ax.plot(inputs[:,0],inputs[:,1],targets, '.', label='targets',color='black') \n";
outfile << "ax.plot(inputs[:,0],inputs[:,1],outputs, '.', label='predictions',color='red')\n";
outfile << "ax.set_xlabel('input_1'); ax.set_ylabel('input_2'); ax.set_zlabel('output') \n";
outfile << "ax.legend(loc='lower right') \n";
} else {
outfile << "plt.plot(inputs,targets, '.', label='targets',color='black') \n";
outfile << "plt.plot(inputs,outputs, '.', label='predictions',color='red') \n";
outfile << "plt.xlabel('input'); plt.ylabel('output'); \n";
outfile << "plt.legend(loc='lower right') \n";
}
outfile << "plt.show() \n";
outfile << endl;
outfile.close();
//cout << " ______________________________________________________________" << endl;
//cout << " | Plot saved data with:" << " 'python " << filename << "'." << endl;
//cout << " |______________________________________________________________" << endl;
}
}
bool WeightedMajorityVoting::forward (ssi_size_t n_models,
IModel **models,
ssi_size_t n_streams,
ssi_stream_t *streams[],
ssi_size_t n_probs,
ssi_real_t *probs) {
if (!isTrained ()) {
ssi_wrn ("not trained");
return false;
}
if (n_streams != _n_streams) {
ssi_wrn ("#streams (%u) differs from #streams (%u)", n_streams, _n_streams);
return false;
}
if (_n_streams != n_models) {
ssi_wrn ("#models (%u) differs from #streams (%u)", n_models, _n_streams);
return false;
}
if (_n_classes != n_probs) {
ssi_wrn ("#probs (%u) differs from #classes (%u)", n_probs ,_n_classes);
return false;
}
bool found_data = false;
IModel *model = 0;
ssi_stream_t *stream = 0;
//calculate actual models
ssi_size_t miss_counter = 0;
ssi_size_t *available = new ssi_size_t[n_models];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
stream = streams[n_model];
if (stream->num > 0) {
found_data = true;
available[n_model] = 1;
}
else{
miss_counter++;
available[n_model] = 0;
}
}
ssi_size_t counter = 0;
ssi_size_t *models_actual = new ssi_size_t[(n_models - miss_counter)];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if(available[n_model] == 1){
models_actual[counter] = n_model;
counter++;
}
}
if(found_data){
ssi_size_t *votes = new ssi_size_t[(n_models - miss_counter)];
for (ssi_size_t n_model = 0; n_model < (n_models - miss_counter); n_model++) {
model = models[models_actual[n_model]];
stream = streams[models_actual[n_model]];
model->forward (*stream, n_probs, probs);
ssi_size_t max_ind = 0;
ssi_real_t max_val = probs[0];
for (ssi_size_t i = 1; i < n_probs; i++) {
if (probs[i] > max_val) {
max_val = probs[i];
max_ind = i;
}
}
votes[n_model] = max_ind;
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
for (ssi_size_t num_probs = 0; num_probs < n_probs; num_probs++){
ssi_print("%f ", probs[num_probs]);
}
ssi_print("- vote: %d\n", max_ind);
}
}
//clear probs
for (ssi_size_t num_probs = 0; num_probs < n_probs; num_probs++){
probs[num_probs] = 0;
}
//fill probs with votes
ssi_char_t weighting_method = 'a';
//a = average
//c = class
for(ssi_size_t n_model = 0; n_model < (n_models - miss_counter); n_model++){
switch (weighting_method)
{
case ('a'):
probs[votes[n_model]] = (probs[votes[n_model]])+(_weights[models_actual[n_model]][_n_classes]);
break;
case ('c'):
probs[votes[n_model]] = (probs[votes[n_model]])+(_weights[models_actual[n_model]][votes[n_model]]);
break;
}
}
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\n");
}
if(votes){
delete[] votes;
votes = 0;
}
}
if(available){
delete [] available;
available = 0;
}
if(models_actual){
delete [] models_actual;
models_actual = 0;
}
/// is there a draw ? ///
ssi_size_t max_ind = 0;
ssi_size_t max_ind_draw = 0;
ssi_real_t max_val = probs[0];
bool draw = false;
for (ssi_size_t i = 1; i < n_probs; i++) {
if (probs[i] >= max_val) {
if(probs[i] == max_val){
draw = true;
max_ind_draw = i;
}
max_val = probs[i];
max_ind = i;
}
}
if(draw && (max_ind == max_ind_draw)){
return false;
}else{
return found_data;
}
}
bool StatModel::empty() const { return !isTrained(); }
bool WeightedMajorityVoting::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (samples.getStreamSize () != n_models) {
ssi_wrn ("#models (%u) differs from #streams (%u)", n_models, samples.getStreamSize ());
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_streams = samples.getStreamSize ();
_n_classes = samples.getClassSize ();
_n_models = n_models;
_weights = new ssi_real_t*[n_models];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
_weights[n_model] = new ssi_real_t[_n_classes+1];
}
if (samples.hasMissingData ()) {
ISMissingData samples_h (&samples);
Evaluation eval;
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
samples_h.setStream (n_model);
models[n_model]->train (samples_h, n_model);
}
eval.eval (*models[n_model], samples_h, n_model);
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
_weights[n_model][n_class] = eval.get_class_prob (n_class);
}
_weights[n_model][_n_classes] = eval.get_classwise_prob ();
}
}
else{
Evaluation eval;
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) { models[n_model]->train (samples, n_model); }
eval.eval (*models[n_model], samples, n_model);
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
_weights[n_model][n_class] = eval.get_class_prob (n_class);
}
_weights[n_model][_n_classes] = eval.get_classwise_prob ();
}
}
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nClassifier Weights: \n");
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
ssi_print ("%f ", _weights[n_model][n_class]);
}
ssi_print ("%f\n", _weights[n_model][_n_classes]);
}
}
return true;
}
bool FeatureFusion::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_streams = samples.getStreamSize ();
_n_classes = samples.getClassSize ();
_n_models = n_models;
//initialize weights
ssi_real_t **weights = new ssi_real_t*[n_models];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
weights[n_model] = new ssi_real_t[_n_classes+1];
}
if (samples.hasMissingData ()) {
_handle_md = true;
ISMissingData samples_h (&samples);
Evaluation eval;
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nMissing data detected.\n");
}
//models[0] is featfuse_model, followed by singlechannel_models
ISMergeDim ffusionSamples (&samples);
ISMissingData ffusionSamples_h (&ffusionSamples);
ffusionSamples_h.setStream(0);
if (!models[0]->isTrained ()) { models[0]->train (ffusionSamples_h, 0); }
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
eval.eval (*models[0], ffusionSamples_h, 0);
eval.print();
}
//dummy weights for fused model
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
weights[0][n_class] = 0.0f;
}
weights[0][_n_classes] = 0.0f;
for (ssi_size_t n_model = 1; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
samples_h.setStream (n_model - 1);
models[n_model]->train (samples_h, n_model - 1);
}
eval.eval (*models[n_model], samples_h, n_model - 1);
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
eval.print();
}
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
weights[n_model][n_class] = eval.get_class_prob (n_class);
}
weights[n_model][_n_classes] = eval.get_classwise_prob ();
}
//calculate fillers
_filler = new ssi_size_t[_n_streams];
for (ssi_size_t n_fill = 0; n_fill < _n_streams; n_fill++) {
_filler[n_fill] = 1;
ssi_real_t filler_weight = weights[1][_n_classes];
for (ssi_size_t n_model = 2; n_model < n_models; n_model++) {
if (filler_weight < weights[n_model][_n_classes]) {
_filler[n_fill] = n_model;
filler_weight = weights[n_model][_n_classes];
}
}
weights[_filler[n_fill]][_n_classes] = 0.0f;
}
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nfiller:\n");
for (ssi_size_t n_model = 0; n_model < _n_streams; n_model++) {
ssi_print("%d ", _filler[n_model]);
}ssi_print("\n");
}
}
else{
_handle_md = false;
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nNo missing data detected.\n");
}
ISMergeDim ffusionSamples (&samples);
if (!models[0]->isTrained ()) { models[0]->train (ffusionSamples, 0); }
//dummy
_filler = new ssi_size_t[_n_streams];
for (ssi_size_t n_fill = 0; n_fill < _n_streams; n_fill++) {
_filler[n_fill] = 0;
}
}
if (weights) {
for (ssi_size_t n_model = 0; n_model < _n_models; n_model++) {
delete[] weights[n_model];
}
delete[] weights;
weights = 0;
}
return true;
}
bool FeatureFusion::forward (ssi_size_t n_models,
IModel **models,
ssi_size_t n_streams,
ssi_stream_t *streams[],
ssi_size_t n_probs,
ssi_real_t *probs) {
if (!isTrained ()) {
ssi_wrn ("not trained");
return false;
}
if (n_streams != _n_streams) {
ssi_wrn ("#streams (%u) differs from #streams (%u)", n_streams, _n_streams);
return false;
}
if (_n_models != n_models) {
ssi_wrn ("#models (%u) differs from #models (%u)", n_models, _n_models);
return false;
}
if (_n_classes != n_probs) {
ssi_wrn ("#probs (%u) differs from #classes (%u)", n_probs ,_n_classes);
return false;
}
//No Missing Data:
if(!_handle_md){
IModel *model = 0;
ssi_stream_t *stream = 0;
model = models[0];
ssi_stream_t *fusion_stream = new ssi_stream_t;
ssi_size_t fusion_stream_dim = 0;
for(ssi_size_t nstrm = 0; nstrm < _n_streams; nstrm++){
fusion_stream_dim += streams[nstrm]->dim;
}
//create aligned streams
ssi_stream_init (*fusion_stream, 1, fusion_stream_dim, streams[0]->byte, streams[0]->type, streams[0]->sr);
ssi_byte_t *ptr = fusion_stream->ptr;
for(ssi_size_t i = 0; i < _n_streams; i++){
memcpy(ptr, streams[i]->ptr, ( streams[i]->byte * streams[i]->dim ) );
ptr += ( streams[i]->byte * streams[i]->dim );
}
//clear probs
for (ssi_size_t num_probs = 0; num_probs < n_probs; num_probs++){
probs[num_probs] = 0.0f;
}
model->forward (*fusion_stream, n_probs, probs);
ssi_stream_destroy(*fusion_stream);
delete fusion_stream;
fusion_stream = 0;
///// is there a draw ? ///
ssi_size_t max_ind = 0;
ssi_size_t max_ind_draw = 0;
ssi_real_t max_val = probs[0];
bool draw = false;
for (ssi_size_t i = 1; i < n_probs; i++) {
if (probs[i] >= max_val) {
if(probs[i] == max_val){
draw = true;
max_ind_draw = i;
}
max_val = probs[i];
max_ind = i;
}
}
if(draw && (max_ind == max_ind_draw)){
return false;
}else{
return true;
}
}//No Missing Data
//Missing Data:
bool found_data = false;
IModel *model = 0;
ssi_stream_t *stream = 0;
//calculate actual models
ssi_size_t miss_counter = 0;
ssi_size_t *available = new ssi_size_t[n_models];
available[0] = 1;
for (ssi_size_t n_model = 1; n_model < _n_models; n_model++) {
stream = streams[n_model - 1];
if (stream->num > 0) {
found_data = true;
available[n_model] = 1;
}
else{
miss_counter++;
available[n_model] = 0;
if(available[0] == 1){
available[0] = 0;
miss_counter++;
}
}
}
ssi_size_t counter = 0;
ssi_size_t *models_actual = new ssi_size_t[(n_models - miss_counter)];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if(available[n_model] == 1){
models_actual[counter] = n_model;
counter++;
}
}
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\n\n-----------------------------\navailable models:\n");
for(ssi_size_t i = 0; i < (n_models - miss_counter); i++){
ssi_print("%d ", models_actual[i]);
}ssi_print("\n");
}
if(found_data){
if(available[0] == 1){
//feature fusion possible
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nfeature fusion possible\n");
}
model = models[0];
stream = 0;
ssi_stream_t *fusion_stream = new ssi_stream_t;
ssi_size_t fusion_stream_dim = 0;
for(ssi_size_t nstrm = 0; nstrm < _n_streams; nstrm++){
fusion_stream_dim += streams[nstrm]->dim;
}
//create aligned streams
ssi_stream_init (*fusion_stream, 1, fusion_stream_dim, streams[0]->byte, streams[0]->type, streams[0]->sr);
ssi_byte_t *ptr = fusion_stream->ptr;
for(ssi_size_t i = 0; i < _n_streams; i++){
memcpy(ptr, streams[i]->ptr, ( streams[i]->byte * streams[i]->dim ) );
ptr += ( streams[i]->byte * streams[i]->dim );
}
//clear probs
for (ssi_size_t num_probs = 0; num_probs < n_probs; num_probs++){
probs[num_probs] = 0.0f;
}
model->forward (*fusion_stream, n_probs, probs);
ssi_stream_destroy(*fusion_stream);
delete fusion_stream;
fusion_stream = 0;
if(available){
delete [] available;
available = 0;
}
if(models_actual){
delete [] models_actual;
models_actual = 0;
}
return true;
}else{
//feature fusion not possible, choose filler ...
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nfeature fusion not possible: filler needed\n");
ssi_print("\nfiller:\n");
for (ssi_size_t n_model = 0; n_model < _n_streams; n_model++) {
ssi_print("%d ", _filler[n_model]);
}ssi_print("\n");
}
bool model_available = false;
ssi_size_t model_id = 0;
for(ssi_size_t h = 0; h < _n_streams; h++){
model_id = _filler[h];
for(ssi_size_t i = 0; i < (n_models - miss_counter); i++){
if(model_id == models_actual[i]){
model_available = true;
break;
}
}
if(model_available == true){
model = models[model_id];
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nSelected Model: %d", model_id);
}
break;
}
}
model->forward(*streams[model_id - 1], n_probs, probs);
}
}
if(available){
delete [] available;
available = 0;
}
if(models_actual){
delete [] models_actual;
models_actual = 0;
}
/// is there a draw ? ///
ssi_size_t max_ind = 0;
ssi_size_t max_ind_draw = 0;
ssi_real_t max_val = probs[0];
bool draw = false;
for (ssi_size_t i = 1; i < n_probs; i++) {
if (probs[i] >= max_val) {
if(probs[i] == max_val){
draw = true;
max_ind_draw = i;
}
max_val = probs[i];
max_ind = i;
}
}
if(draw && (max_ind == max_ind_draw)){
return false;
}else{
return found_data;
}
}