#include <iostream>
#include <string>
#include <Eigen/Dense>
#include "./headers/file_manage.h"
#include "./headers/model_evaluation.h"
#include "./headers/decision_tree.h"
using namespace std;
using namespace Eigen;

int main()
{
	string file_name = "./dataset/iris.csv";

	MatrixXf X;
	VectorXi Y;

	// read csv
	SimpleML::read_csv(file_name, X, Y);

	// run decision tree classifier
	SimpleML::DecisionTree dt;
	dt.fit(X, Y);
	dt.print_tree();

	// evaluate
	VectorXi predicted = dt.predict(X);
	float acc = SimpleML::calc_accuracy(Y, predicted);
	cout << "\nAccuracy: " << acc * 100 << "%" << endl;

	// K-fold cross validation. Uncomment below if necessary
	/*float acc = SimpleML::evaluate_classification_model(dt, X, Y, 5);
	cout << "Accuracy(k-fold): " << acc * 100 << "%" << endl;*/
}

#include <iostream>
#include <string>
#include <Eigen/Dense>
#include "./headers/file_manage.h"
#include "./headers/model_evaluation.h"
#include "./headers/gaussian_mixture.h"
using namespace std;
using namespace Eigen;

int main()
{
	string file_name = "./dataset/iris.csv";

	MatrixXf X;
	VectorXi Y;

	// read csv
	SimpleML::read_csv(file_name, X, Y);

	// run gaussian mixture model
	SimpleML::GaussianMixture gm(3);
	gm.fit(X);

	// evaluate
	vector<vector<int>> predicted = gm.predict(X);
	float sil = SimpleML::silhouette_score(X, predicted);
	cout << "Silhouette score : " << sil << endl;

	return 0;
}