// REF [file] >> ${WAFFLES_HOME}/demos/hello_ml/src/main.cpp.
void ml_example()
{
// Define the feature attributes (or columns)
std::vector<size_t> feature_values;
feature_values.push_back(0); // diameter = continuous
feature_values.push_back(3); // crust_type = { thin_crust=0, Chicago_style_deep_dish=1, Neapolitan=2 }
feature_values.push_back(2); // meatiness = { vegan=0, meaty=1 }
feature_values.push_back(4); // presentation = { dine_in=0, take_out=1, delivery=2, frozen=3 }
// Define the label attributes (or columns)
std::vector<size_t> label_values;
label_values.push_back(2); // taste = { lousy=0, delicious=1 }
label_values.push_back(0); // cost = continuous
// Make some contrived hard-coded training data
GClasses::GMatrix feat(feature_values);
feat.newRows(12);
GClasses::GMatrix lab(label_values);
lab.newRows(12);
// diameter crust meatiness presentation taste cost
feat[0][0] = 14.0; feat[0][1] = 1; feat[0][2] = 1; feat[0][3] = 0; lab[0][0] = 1; lab[0][1] = 22.95;
feat[1][0] = 12.0; feat[1][1] = 0; feat[1][2] = 0; feat[1][3] = 3; lab[1][0] = 0; lab[1][1] = 3.29;
feat[2][0] = 14.0; feat[2][1] = 1; feat[2][2] = 1; feat[2][3] = 2; lab[2][0] = 1; lab[2][1] = 15.49;
feat[3][0] = 12.0; feat[3][1] = 2; feat[3][2] = 0; feat[3][3] = 0; lab[3][0] = 1; lab[3][1] = 16.65;
feat[4][0] = 18.0; feat[4][1] = 1; feat[4][2] = 1; feat[4][3] = 3; lab[4][0] = 0; lab[4][1] = 9.99;
feat[5][0] = 14.0; feat[5][1] = 1; feat[5][2] = 1; feat[5][3] = 0; lab[5][0] = 1; lab[5][1] = 14.49;
feat[6][0] = 12.0; feat[6][1] = 2; feat[6][2] = 0; feat[6][3] = 2; lab[6][0] = 1; lab[6][1] = 19.65;
feat[7][0] = 14.0; feat[7][1] = 0; feat[7][2] = 1; feat[7][3] = 1; lab[7][0] = 0; lab[7][1] = 6.99;
feat[8][0] = 14.0; feat[8][1] = 1; feat[8][2] = 1; feat[8][3] = 2; lab[8][0] = 1; lab[8][1] = 19.95;
feat[9][0] = 14.0; feat[9][1] = 2; feat[9][2] = 0; feat[9][3] = 3; lab[9][0] = 0; lab[9][1] = 12.99;
feat[10][0] = 16.0; feat[10][1] = 0; feat[10][2] = 1; feat[10][3] = 0; lab[10][0] = 0; lab[10][1] = 12.20;
feat[11][0] = 14.0; feat[11][1] = 1; feat[11][2] = 1; feat[11][3] = 1; lab[11][0] = 1; lab[11][1] = 15.01;
// Make a test vector
GClasses::GVec test_features(4);
GClasses::GVec predicted_labels(2);
std::cout << "This demo trains and tests several supervised learning models using some contrived hard-coded training data to predict the tastiness and cost of a pizza.\n\n";
test_features[0] = 15.0; test_features[1] = 2; test_features[2] = 0; test_features[3] = 0;
std::cout << "Predicting labels for a 15 inch pizza with a Neapolitan-style crust, no meat, for dine-in.\n\n";
// Use several models to make predictions
std::cout.precision(4);
local::decision_tree(feat, lab, test_features, predicted_labels);
std::cout << "The decision tree predicts the taste is " << (predicted_labels[0] == 0 ? "lousy" : "delicious") << ", and the cost is $" << predicted_labels[1] << std::endl;
local::neural_network(feat, lab, test_features, predicted_labels);
std::cout << "The neural network predicts the taste is " << (predicted_labels[0] == 0 ? "lousy" : "delicious") << ", and the cost is $" << predicted_labels[1] << std::endl;
local::knn(feat, lab, test_features, predicted_labels);
std::cout << "The knn model predicts the taste is " << (predicted_labels[0] == 0 ? "lousy" : "delicious") << ", and the cost is $" << predicted_labels[1] << std::endl;
local::naivebayes(feat, lab, test_features, predicted_labels);
std::cout << "The naive Bayes model predicts the taste is " << (predicted_labels[0] == 0 ? "lousy" : "delicious") << ", and the cost is $" << predicted_labels[1] << std::endl;
local::ensemble(feat, lab, test_features, predicted_labels);
std::cout << "Random forest predicts the taste is " << (predicted_labels[0] == 0 ? "lousy" : "delicious") << ", and the cost is $" << predicted_labels[1] << std::endl;
}
error_t RegressionForest<FeatT, LabT, SplitT, SplFitterT>::test_error(const feature_mtx<FeatT> & features,
const label_mtx<LabT> & ground_truth_labels) const {
datapoint_idx_t num_datapoints = features.rows();
// Check matrices are right size
if (!feature_mtx_correct_shape(features, num_datapoints)) {
} else if (!label_mtx_correct_shape(ground_truth_labels, num_datapoints)) {
throw std::invalid_argument("ground_truth_labels is wrong shape");
}
// Create output array for labels
label_mtx<LabT> predicted_labels(num_datapoints, forest_stats.label_dimensions);
// Do the prediction
predict(features, &predicted_labels);
return mean_squared_error(ground_truth_labels, predicted_labels);
}
