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