DataGen with fixed variance

DataGenFixedVarianece.cpp

#include <iostream>
#include <fstream>
#include <string>
#include <random>

// Eigen
#include <Eigen/Dense>

using namespace std;
using namespace Eigen;


int main(){
	
	int N=2000; // number of observations
	int K=3; // number of mixture components
	Vector3d mu(-0.8, 0.0, 2.0); // mean vector
	double tau=1.5; //common variance 
	double pi[] = {0.5, 0.3, 0.2}; // mixture rate (sum is 1)
	VectorXd pi_total(K); 
	double running_total = 0.0;

	VectorXd genVec(N); // store generated data
	VectorXd Z(N);
	VectorXd class_(N);
	
	// Create Z (here, Z is mu vector)
	for(int i=0; i<sizeof(pi)/sizeof(pi[0]); i++){ //配列の要素数を求めている 
		running_total += pi[i];
		pi_total[i] = running_total;
	}
	
	// Weighted Random Generation http://goo.gl/mr5d8G 
	double rnd;
	random_device seed_gen; // http://cpprefjp.github.io/reference/random.html
	mt19937 engine(seed_gen());
	uniform_real_distribution<> dist1(0.0, 1.0);
	for(int i=0; i<N; i++){
		rnd = dist1(engine) * running_total;

		for(int s=0; s<K; s++){
			if (rnd < pi_total[s]){
				Z[i] = mu[s];
				class_[i] = s;
				break;
			}//end if
		}//end for(s)

	}//end for(i)
	
	// Create Observations (genVec)
	for(int i=0; i<N; i++){
		normal_distribution<> norm_dist(Z[i], tau); //!!: Z is not the same as in the original
		genVec[i] = norm_dist(engine);
	}//end(i)



	ofstream outputfileData("GeneratedData.txt");
  	outputfileData << genVec;
  	outputfileData.close();

	ofstream outputfileClass("GeneratedClass.txt");
  	outputfileClass << class_;
  	outputfileClass.close();

}