Update README.md

README.md

@@ -267,7 +267,7 @@ class simulation_for_machine_learning{
 						int n = sample_sizes[jj];
 
 						// ---- Simulation samples ----
-						//competing hypothesis Í0
+						//competing hypothesis H0
 						Sample A0(*H0_1.D,n,Gw);
 						Sample B0(*H0_1.D,n,Gw);
 						if( per > 0 )
@@ -276,7 +276,7 @@ class simulation_for_machine_learning{
 							B0.CensoredTypeThird(*H1_1.D,Gw);
 						}
 
-						//competing hypothesis Í1
+						//competing hypothesis H1
 						Sample A1(*H0_1.D,n,Gw);
 						Sample B1(*H0_2.D,n,Gw);
 						if( per > 0 )
@@ -286,7 +286,7 @@ class simulation_for_machine_learning{
 						}
 
 						// ---- Computation of the test statistics & Save to file ----
-						//Sn and p-value computation under Í0
+						//Sn and p-value computation under H0
 						FILE *ou = fopen(file_to_save, "a");
 						auto perc1 = A0.RealCensoredPercent();
 						auto perc2 = B0.RealCensoredPercent();
@@ -303,7 +303,7 @@ class simulation_for_machine_learning{
 						}
 						fprintf(ou, "\n");
 
-						//Sn and p-value computation under Í1
+						//Sn and p-value computation under H1
 						perc1 = A1.RealCensoredPercent();
 						perc2 = B1.RealCensoredPercent();
 						fprintf(ou,"%d;", iter);
@@ -328,7 +328,7 @@ class simulation_for_machine_learning{
 		// Constructor of the class
 		simulation_for_machine_learning(vector<HomogeneityTest*> &D)
 		{
-			int N = 40000;	// number of the Monte-Carlo replications
+			int N = 37650;	// number of the Monte-Carlo replications
 			#pragma omp parallel for
 			for(int k=0; k<N; k++)
 			{