void evaluateTestData(const Network& net) {
CsvReader reader;
CsvWriter writer;
cout << "Leyendo test data" << endl;
MatrixXd testData = reader.csvReadToMatrix(TEST_DATA_FILE);
if (testData.rows() == 0 && testData.cols() == 0) {
printf("Error leyendo test data.\n");
return;
}
cout << "cantidad de features: " << (testData.cols() - 1) << endl << endl;
cout << "Evaluando test data" << endl;
MatrixXd results = net.evaluate(testData);
writer.makeSubmitWithMatrix(SUBMIT_FILE, results);
}
inputData_t generateInputData() {
CsvReader reader;
MatrixXd matrix = reader.csvReadToMatrix(TRAIN_DATA_FILE);
if (matrix.rows() == 0 && matrix.cols() == 0) {
printf("Error leyendo data.\n");
exit(-1);
} else {
cout << "cantidad de features: " << (matrix.cols() - 1) << endl << endl;
}
//shuflear toda la matrix
PermutationMatrix<Dynamic, Dynamic> permutacionFilasRandom(matrix.rows());
permutacionFilasRandom.setIdentity();
srand(time(0));
random_shuffle(permutacionFilasRandom.indices().data(),
permutacionFilasRandom.indices().data()
+ permutacionFilasRandom.indices().size());
matrix = permutacionFilasRandom * (matrix);
// Recuce el tamanio de la matriz para poder testear.
// TODO Para correr con todos los datos cambiar DATA_SIZE = 1.
int nuevo_ultimo_indice = round(matrix.rows() * DATA_SIZE);
matrix.conservativeResize(nuevo_ultimo_indice, matrix.cols());
int ultimo_indice_train = round(matrix.rows() * 0.8);
int ultimo_indice_test = round(matrix.rows() * 0.9);
MatrixXd matrix_train = matrix.block(0, 0, ultimo_indice_train, matrix.cols());
MatrixXd matrix_test = matrix.block(ultimo_indice_train, 0,
ultimo_indice_test - ultimo_indice_train, matrix_train.cols());
MatrixXd matrix_validation = matrix.block(ultimo_indice_test, 0,
matrix.rows() - ultimo_indice_test, matrix_train.cols());
matrix.resize(0, 0);
inputData_t data;
//separar matrix_train en x_train, y_train
//separar matrix_test en x_test, y_test
data.x_train = matrix_train.block(0, 0, matrix_train.rows(),
matrix_train.cols() - 1);
data.y_train = (matrix_train.block(0, matrix_train.cols() - 1,
matrix_train.rows(), 1)); //me dice que puse different types
data.x_test = matrix_test.block(0, 0, matrix_test.rows(),
matrix_test.cols() - 1);
data.y_test = matrix_test.block(0, matrix_test.cols() - 1, matrix_test.rows(),
1);
data.x_validation = matrix_validation.block(0, 0, matrix_validation.rows(),
matrix_validation.cols() - 1);
data.y_validation = matrix_validation.block(0, matrix_validation.cols() - 1,
matrix_validation.rows(), 1);
matrix_train.resize(0, 0);
matrix_test.resize(0, 0);
cout << "Train x: " << data.x_train.rows() << "x" << data.x_train.cols() << "\n";
cout << "Train y: " << data.y_train.rows() << "x" << data.y_train.cols() << "\n";
cout << "Test x: " << data.x_test.rows() << "x" << data.x_test.cols() << "\n";
cout << "Test y: " << data.y_test.rows() << "x" << data.y_test.cols() << "\n";
cout << "Validation x: " << data.x_validation.rows() << "x"
<< data.x_validation.cols() << "\n";
cout << "Validation y: " << data.y_validation.rows() << "x"
<< data.y_validation.cols() << "\n";
return data;
}
