Update README.md

README.md

@@ -71,6 +71,99 @@ In these files there are following fields:
 
 # Dataset Simulation
 
-For this dataset, the full source code is available for simulation by the Monte Carlo method.
+For this dataset, the full source code (C++) is available for simulation by the Monte Carlo method. We presented the file main.cpp only.
+
+~~~
+#include"simulation_for_machine_learning.h"
+
+//Ñîçäàòü âñå êðèòåðèè
+vector<HomogeneityTest*> AllTests()
+{
+	vector<HomogeneityTest*> D;
+	
+	// ---- Classical Two-Sample tests for Uncensored Case ----
+	//D.push_back( new HT_AndersonDarlingPetitt );
+	//D.push_back( new HT_KolmogorovSmirnovTest );
+	//D.push_back( new HT_LehmannRosenblatt );
+	
+	// ---- Two-Sample tests for Right-Censored Case ----
+	D.push_back( new HT_Peto );
+	D.push_back( new HT_Gehan );
+	D.push_back( new HT_Logrank );
+	
+	D.push_back( new HT_BagdonaviciusNikulinGeneralizedCox );
+	D.push_back( new HT_BagdonaviciusNikulinMultiple );
+	D.push_back( new HT_BagdonaviciusNikulinSingle );
+
+	D.push_back( new HT_QTest );			//based on the Kaplan-Meier estimator
+	D.push_back( new HT_MAX );				//Maximum Value test
+	D.push_back( new HT_SynthesisTest );	//MIN3 test
+	
+	D.push_back( new HT_WeightedLogrank("logrank") );
+	D.push_back( new HT_WeightedLogrank("Tarone–Ware") );
+	D.push_back( new HT_WeightedLogrank("Breslow") );
+	D.push_back( new HT_WeightedLogrank("Peto–Prentice") );
+	D.push_back( new HT_WeightedLogrank("Prentice") );
+	
+	D.push_back( new HT_WeightedKaplanMeyer );
+		
+	return D;
+}
+
+// Example of two-sample testing using this code
+void EXAMPLE_1(vector<HomogeneityTest*> &D)
+{
+	// load the samples
+	Sample T1(".//samples//1Chemotherapy.txt");
+	Sample T2(".//samples//2Radiotherapy.txt");
+
+	// two-sample testing through selected tests
+	for(int j=0; j<D.size(); j++)
+	{
+		char test_name[512];
+		D[j]->TitleTest(test_name);
+		
+
+		double Sn = D[j]->CalculateStatistic(T1, T2);
+		double pvalue = D[j]->p_value(T1, T2, 1000);
+
+		printf("%s\n", &test_name);
+		printf("\t Sn: %lf\n", Sn);
+		printf("\t pv: %lf\n", pvalue);
+		printf("--------------------------------");
+	}
+}
+
+// Example of the dataset simulation for the proposed ML-method
+void EXAMPLE_2(vector<HomogeneityTest*> &D)
+{
+	// Run dataset (train or test sample) simulation (results in ".//to_machine_learning_2024//")
+	simulation_for_machine_learning sm(D);
+}
+
+// init point
+int main()
+{
+	// Set the number of threads
+	int k = omp_get_max_threads() - 1;
+	//int k = 1;
+	omp_set_num_threads( k );
+
+	// Select two-sample tests
+	auto D = AllTests();
+	
+	// Example of two-sample testing using this code
+	EXAMPLE_1(D);
+
+	// Example of the dataset simulation for the proposed ML-method
+	EXAMPLE_2(D);
+
+	// Freeing memory
+	ClearMemory(D);
+	
+	printf("The mission is completed.\n");
+	return 0;
+}
+~~~