本文整理汇总了C++中Basis::begin方法的典型用法代码示例。如果您正苦于以下问题：C++ Basis::begin方法的具体用法？C++ Basis::begin怎么用？C++ Basis::begin使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Basis的用法示例。
在下文中一共展示了Basis::begin方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
int main(int argc, char * argv[])
{
srand(time(NULL));
// Let us define a 3 layer perceptron architecture
auto input = gaml::mlp::input<X>(INPUT_DIM, fillInput);
auto l1 = gaml::mlp::layer(input, HIDDEN_LAYER_SIZE, gaml::mlp::mlp_sigmoid(), gaml::mlp::mlp_dsigmoid());
auto l2 = gaml::mlp::layer(l1, HIDDEN_LAYER_SIZE, gaml::mlp::mlp_sigmoid(), gaml::mlp::mlp_dsigmoid());
auto output = gaml::mlp::layer(l2, OUTPUT_DIM, gaml::mlp::mlp_identity(), gaml::mlp::mlp_didentity());
auto mlp = gaml::mlp::perceptron(output, output_of);
// Create a training base
// Let us try to fit a noisy sinc function
Basis basis;
basis.resize(NB_SAMPLES);
for(auto& d: basis)
{
d.first = {{ -10.0 + 20.0 * gaml::random::uniform(0.0, 1.0) }} ;
d.second = noisy_oracle(d.first);
}
// Set up the parameters for learning the MLP with a gradient descent
gaml::mlp::learner::gradient::parameter gradient_params;
gradient_params.alpha = 1e-2;
gradient_params.dalpha = 1e-3;
gradient_params.verbose = true;
// The stopping criteria
gradient_params.max_iter = 10000;
gradient_params.min_dparams = 1e-7;
// Create the learner
auto learning_algorithm = gaml::mlp::learner::gradient::algorithm(mlp, gradient_params, gaml::mlp::loss::Quadratic(), fillOutput);
// Call the learner on the basis and get the learned predictor
auto predictor = learning_algorithm(basis.begin(),
basis.end(),
input_of_data,
output_of_data);
// Print out the structure of the perceptron we learned
std::cout << predictor << std::endl;
// Dump the results
std::ofstream outfile("example-005-samples.data");
for(auto& b: basis)
outfile << b.first[0] << " "
<< b.second[0] << " "
<< std::endl;
outfile.close();
outfile.open("example-005-regression.data");
X x;
for(x[0] = -10; x[0] < 10 ; x[0] += 0.1)
{
auto output = predictor(x);
outfile << x[0] << " "
<< oracle(x)[0] << " "
<< output[0] << std::endl;
}
outfile.close();
std::cout << "You can plot the results using gnuplot :" << std::endl;
std::cout << "gnuplot " << ML_MLP_SHAREDIR << "/plot-example-005.gplot" << std::endl;
std::cout << "This will produce example-005.ps" << std::endl;
// Let us compute the empirical risk.
auto evaluator = gaml::risk::empirical(gaml::mlp::loss::Quadratic());
double risk = evaluator(predictor,
basis.begin(),
basis.end(),
input_of_data,
output_of_data);
std::cout << "Empirical risk = " << risk << std::endl;
// We will use a 6-fold cross-validation to estimate the real risk.
auto kfold_evaluator = gaml::risk::cross_validation(gaml::mlp::loss::Quadratic(),
gaml::partition::kfold(6),
true);
double kfold_risk = kfold_evaluator(learning_algorithm,
basis.begin(),basis.end(),
input_of_data,output_of_data);
std::cout << "Estimation of the real risk (6-fold): "
<< kfold_risk << std::endl;
}示例2: main
int main(int argc, char * argv[])
{
srand(time(NULL));
// Create a training base
// Let us try to fit a noisy sinc function
Basis basis;
basis.resize(NB_SAMPLES);
for(auto& d: basis)
{
d.first = {{ -10.0 + 20.0 * gaml::random::uniform(0.0, 1.0) }} ;
d.second = {{ sin(d.first[0])/d.first[0] + gaml::random::uniform(-0.1, 0.1) }};
}
// Create the learner
MetaLearner learning_algorithm(true);
std::cout << "Finding the optimal perceptron for the basis and train it..." << std::endl;
// Call the learner on the basis and get the learned predictor
auto predictor = learning_algorithm(basis.begin(),
basis.end(),
input_of_data,
output_of_data);
std::cout << "done!" << std::endl;
// Print ou the structure of the perceptron
std::cout << predictor << std::endl;
// Dump the results
std::ofstream outfile("example-003.data");
for(auto& b: basis)
{
auto output = predictor(b.first);
outfile << b.first[0] << " "
<< b.second[0] << " "
<< output[0] << " "
<< std::endl;
}
outfile.close();
std::cout << "You can plot the results using gnuplot :" << std::endl;
std::cout << "gnuplot " << ML_MLP_SHAREDIR << "/plot-example-003.gplot" << std::endl;
// Let us compute the empirical risk.
auto evaluator = gaml::risk::empirical(gaml::mlp::loss::Quadratic());
double risk = evaluator(predictor,
basis.begin(),
basis.end(),
input_of_data,
output_of_data);
std::cout << "Empirical risk = " << risk << std::endl;
// We will use a 6-fold cross-validation to estimate the real risk.
auto kfold_evaluator = gaml::risk::cross_validation(gaml::mlp::loss::Quadratic(),
gaml::partition::kfold(6),
true);
learning_algorithm.verbosity = false;
double kfold_risk = kfold_evaluator(learning_algorithm,
basis.begin(),basis.end(),
input_of_data,output_of_data);
std::cout << "Estimation of the real risk (6-fold): "
<< kfold_risk << std::endl;
return 0;
}