Esempi in C++ (Cpp) per OptimizerType::Function

Esempio n. 1

File: logistic_regression_impl.hpp Progetto: AmesianX/mlpack

LogisticRegression<MatType>::LogisticRegression(
    OptimizerType<LogisticRegressionFunction<MatType>>& optimizer) :
    parameters(optimizer.Function().GetInitialPoint()),
    lambda(optimizer.Function().Lambda())
{
  Train(optimizer);
}

Esempio n. 2

File: softmax_regression_impl.hpp Progetto: AmesianX/mlpack

SoftmaxRegression<OptimizerType>::SoftmaxRegression(
    OptimizerType<SoftmaxRegressionFunction>& optimizer) :
    parameters(optimizer.Function().GetInitialPoint()),
    numClasses(optimizer.Function().NumClasses()),
    lambda(optimizer.Function().Lambda()),
    fitIntercept(optimizer.Function().FitIntercept())
{
  Train(optimizer);
}

Esempio n. 3

File: logistic_regression_impl.hpp Progetto: GABowers/MinGW_libs

LogisticRegression<OptimizerType>::LogisticRegression(
    OptimizerType<LogisticRegressionFunction>& optimizer) :
    parameters(optimizer.Function().GetInitialPoint()),
    lambda(optimizer.Function().Lambda())
{
  Timer::Start("logistic_regression_optimization");
  const double out = optimizer.Optimize(parameters);
  Timer::Stop("logistic_regression_optimization");

  Log::Info << "LogisticRegression::LogisticRegression(): final objective of "
      << "trained model is " << out << "." << std::endl;
}

Esempio n. 4

File: logistic_regression_impl.hpp Progetto: AmesianX/mlpack

void LogisticRegression<MatType>::Train(
    OptimizerType<LogisticRegressionFunction<MatType>>& optimizer)
{
  // Everything is good.  Just train the model.
  parameters = optimizer.Function().GetInitialPoint();

  Timer::Start("logistic_regression_optimization");
  const double out = optimizer.Optimize(parameters);
  Timer::Stop("logistic_regression_optimization");

  Log::Info << "LogisticRegression::LogisticRegression(): final objective of "
      << "trained model is " << out << "." << std::endl;
}

Esempio n. 5

File: sparse_autoencoder_impl.hpp Progetto: Andrew-He/mlpack

SparseAutoencoder<OptimizerType>::SparseAutoencoder(
    OptimizerType<SparseAutoencoderFunction> &optimizer) :
    parameters(optimizer.Function().GetInitialPoint()),
    visibleSize(optimizer.Function().VisibleSize()),
    hiddenSize(optimizer.Function().HiddenSize()),
    lambda(optimizer.Function().Lambda()),
    beta(optimizer.Function().Beta()),
    rho(optimizer.Function().Rho())
{
  Timer::Start("sparse_autoencoder_optimization");
  const double out = optimizer.Optimize(parameters);
  Timer::Stop("sparse_autoencoder_optimization");

  Log::Info << "SparseAutoencoder::SparseAutoencoder(): final objective of "
      << "trained model is " << out << "." << std::endl;
}