Update README.md

README.md

@@ -70,6 +70,19 @@ In these files, there are following fields:
 - **SKLEARN_LogReg_test** is a statistic of the proposed ML-method based on Logistic Regression (implemented in sklearn);
 - **SKLEARN_GB_test** is a statistic of the proposed ML-method based on Gradient Boosting Machine (implemented in sklearn).
 
+# Citing
+
+~~~
+@misc {petr_philonenko_2024,
+	author       = { {Petr Philonenko} },
+	title        = { ML_for_TwoSampleTesting (Revision a4ae672) },
+	year         = 2024,
+	url          = { https://huggingface.co/datasets/pfilonenko/ML_for_TwoSampleTesting },
+	doi          = { 10.57967/hf/2978 },
+	publisher    = { Hugging Face }
+}
+~~~
+
 # Dataset Simulation
 
 For this dataset, the full source code (C++) is available [here](https://github.com/pfilonenko/ML_for_TwoSampleTesting/tree/main/dataset/simulation). 
@@ -166,16 +179,171 @@ int main()
 	return 0;
 }
 ```
+> simulation_for_machine_learning.h
+```C++
+#ifndef simulation_for_machine_learning_H
+#define simulation_for_machine_learning_H
 
-# Citing
+#include"HelpFucntions.h"
+
+// Object of the data simulation for training of the proposed ML-method
+class simulation_for_machine_learning{
+	private:
+		// p-value computation using the Test and Test Statistic (Sn)
+		double pvalue(double Sn, HomogeneityTest* Test)
+		{
+			auto f = Test->F( Sn );
+			double pv = 0;
+			if( Test->TestType().c_str() == "right" )
+				pv = 1.0 - f;
+			else
+				if( Test->TestType().c_str() == "left" )
+					pv = f;
+				else    // "double"
+					pv = 2.0*min( f, 1-f );
+			return pv;
+		}
+
+		// Process of simulation
+		void Simulation(int iter, vector<HomogeneityTest*> &D, int rank, mt19937boost Gw)
+		{
+			// ñôîðìèðîâàëè íàçâàíèå ôàéëà äëÿ ñîõðàíåíèÿ
+			char file_to_save[512];
+			sprintf(file_to_save,".//to_machine_learning_2024//to_machine_learning[rank=%d].csv", rank);
+
+			// åñëè ýòî ñàìàÿ ïåðâàÿ èòåðàöèÿ, òî ñîõðàíèëè øàïêó ôàéëà
+			if( iter == 0 )
+			{
+				FILE *ou = fopen(file_to_save,"w");
+				fprintf(ou, "num;H0/H1;model;n1;n2;perc;real_perc1;real_perc2;");
+				for(int i=0; i<D.size(); i++)
+				{
+					char title_of_test[512];
+					D[i]->TitleTest(title_of_test);
+					fprintf(ou, "Sn [%s];p-value [%s];", title_of_test, title_of_test);
+				}
+				fprintf(ou, "\n");
+				fclose(ou);
+			}
+
+			// Getting list of the Alternative Hypotheses (H01 - H27)
+			vector<int> H;
+			int l = 1;
+			for(int i=100; i<940; i+=100)			// Groups of Alternative Hypotheses (I, II, III, IV, V, VI, VII, VIII, IX)
+			{
+				for(int j=10; j<40; j+=10)			// Alternative Hypotheses in the Group (e.g., H01, H02, H03 into the I and so on)
+					//for(int l=1; l<4; l++)		// various families of distribution of censoring time F^C(t)
+						H.push_back( 1000+i+j+l );
+			}
+
+			// Sample sizes
+			vector<int> sample_sizes;
+			sample_sizes.push_back( 20 );	// n1 = n2 = 20
+			sample_sizes.push_back( 30 );	// n1 = n2 = 30
+			sample_sizes.push_back( 50 );	// n1 = n2 = 50
+			sample_sizes.push_back( 75 );	// n1 = n2 = 75
+			sample_sizes.push_back( 100 );	// n1 = n2 = 100
+			sample_sizes.push_back( 150 );	// n1 = n2 = 150
+			sample_sizes.push_back( 200 );	// n1 = n2 = 200
+			sample_sizes.push_back( 300 );	// n1 = n2 = 300
+			sample_sizes.push_back( 500 );	// n1 = n2 = 500
+			sample_sizes.push_back( 1000 );	// n1 = n2 = 1000
+
+			// Simulation (Getting H, Simulation samples, Computation of the test statistics & Save to file)
+			for(int i = 0; i<H.size(); i++)
+			{
+				int Hyp = H[i];
+		
+				if(rank == 0)
+					printf("\tH = %d\n",Hyp);
+
+				for(int per = 0; per<51; per+=10)
+				{
+					// ---- Getting Hi ----
+					AlternativeHypotheses H0_1(Hyp,1,0), H0_2(Hyp,2,0);
+					AlternativeHypotheses H1_1(Hyp,1,per), H1_2(Hyp,2,per);
+
+					for(int jj=0; jj<sample_sizes.size(); jj++)
+					{
+						int n = sample_sizes[jj];
+
+						// ---- Simulation samples ----
+						//competing hypothesis Í0
+						Sample A0(*H0_1.D,n,Gw);
+						Sample B0(*H0_1.D,n,Gw);
+						if( per > 0 )
+						{
+							A0.CensoredTypeThird(*H1_1.D,Gw);
+							B0.CensoredTypeThird(*H1_1.D,Gw);
+						}
+
+						//competing hypothesis Í1
+						Sample A1(*H0_1.D,n,Gw);
+						Sample B1(*H0_2.D,n,Gw);
+						if( per > 0 )
+						{
+							A1.CensoredTypeThird(*H1_1.D,Gw);
+							B1.CensoredTypeThird(*H1_2.D,Gw);
+						}
+
+						// ---- Computation of the test statistics & Save to file ----
+						//Sn and p-value computation under Í0
+						FILE *ou = fopen(file_to_save, "a");
+						auto perc1 = A0.RealCensoredPercent();
+						auto perc2 = B0.RealCensoredPercent();
+						fprintf(ou,"%d;", iter);
+						fprintf(ou,"H0;");
+						fprintf(ou,"%d;", Hyp);
+						fprintf(ou,"%d;%d;", n,n);
+						fprintf(ou,"%d;%lf;%lf", per, perc1, perc2);
+						for(int j=0; j<D.size(); j++)
+						{
+							auto Sn_H0 = D[j]->CalculateStatistic(A0, B0);
+							auto pv_H0 = 0.0;	// skip computation (it prepares in ML-framework)
+							fprintf(ou, ";%lf;0", Sn_H0);
+						}
+						fprintf(ou, "\n");
+
+						//Sn and p-value computation under Í1
+						perc1 = A1.RealCensoredPercent();
+						perc2 = B1.RealCensoredPercent();
+						fprintf(ou,"%d;", iter);
+						fprintf(ou,"H1;");
+						fprintf(ou,"%d;", Hyp);
+						fprintf(ou,"%d;%d;", n,n);
+						fprintf(ou,"%d;%lf;%lf", per, perc1, perc2);
+						for(int j=0; j<D.size(); j++)
+						{
+							auto Sn_H1 = D[j]->CalculateStatistic(A1, B1);
+							auto pv_H1 = 0.0;  // skip computation (it prepares in ML-framework)
+							fprintf(ou, ";%lf;0", Sn_H1);
+						}
+						fprintf(ou, "\n");
+						fclose( ou );
+					}
+				}
+			}
+		}
+
+	public:
+		// Constructor of the class
+		simulation_for_machine_learning(vector<HomogeneityTest*> &D)
+		{
+			int N = 40000;	// number of the Monte-Carlo replications
+			#pragma omp parallel for
+			for(int k=0; k<N; k++)
+			{
+				int rank = omp_get_thread_num();
+				auto gen = GwMT19937[rank];
+		
+				if(rank == 0)
+					printf("\r%d", k);
+
+				Simulation(k, D, rank, gen);
+			}
+		}
+};
+
+#endif
+```
 
-~~~
-@misc {petr_philonenko_2024,
-	author       = { {Petr Philonenko} },
-	title        = { ML_for_TwoSampleTesting (Revision a4ae672) },
-	year         = 2024,
-	url          = { https://huggingface.co/datasets/pfilonenko/ML_for_TwoSampleTesting },
-	doi          = { 10.57967/hf/2978 },
-	publisher    = { Hugging Face }
-}
-~~~