Inverse Transformation for some Statistical Distributions (C

This a Statistical program to find the inverse transformation for some distributions depending on randomization that we are using them in most of our simulations programs. Some Distributions such that:

• Uniform distribution.
• Normal distribution.
• Exponential distribution.

java code

#include <iostream>
#include <cmath>
#include <ctime>
#include <cstdlib>
#include <iomanip>
#include <unistd.h>
using namespace std;

double uniform(double randnum,double a, double b)
{
	return (a+(randnum*(b-a)));
}

double exponential(double randnum,double lambda)
{
	return (-lambda*(log(randnum)));
}

double normal(double mu, double sigma)
{
	srand(time(NULL));
	double random;
	double tot=0.0;

	for(int i=1;i<=12;i++)
	{
		random=(rand()%100)/100.0;
		tot+=random;
	}
	
	return(mu+sigma*(tot-6));
}

int main()
{
	double a;
	double b;
	double lambda;
	double mu;
	double sigma;
	double val;
	int choice;
	
	cout << "Choose a distribution: " << endl;
	cout << "	- Press 1 for uniform\n"
	     << "	- Press 2 for exponential\n"
	     << "	- Press 3 for normal\n"
	     << endl
	     << endl
	     << endl
	     << endl
	     << endl
	     << "Choice:";
	cin >> choice;

	srand(time(NULL));
	
	switch(choice)
	{
		case 1:
			cout << "Enter lower limit for uniform distribution (a): ";
			cin >> a;

			cout << "Enter upper limit for uniform distribution (b): ";
			cin >> b;
			
			cout << "F(x)\t\t\t\tx" << endl;
			cout << "------------------------------------" << endl;
			for(int i=0;i<10;i++)
			{
				val=(rand()%100)/100.0;
				cout << val << "\t\t\t\t" << uniform(val,a,b) << endl;
			}
			break;
		case 2:
			cout << "Enter the mean of exponential distrbution (lambda): ";
			cin >> lambda;

			cout << "F(x)\t\t\t\tx" << endl;
			cout << "------------------------------------" << endl;
			for(int i=0;i<10;i++)
			{
				val=(rand()%100)/100.0;
				cout << val << "\t\t\t\t" << exponential(val,lambda) << endl;
			}
			break;
		case 3:
			cout << "Enter the mean of normal distribution (mu): ";
			cin >> mu;

			cout << "Enter the standard deviation of normal distribution (sigma): ";
			cin >> sigma;
			cout << "These are 10 normally distributed random variables" << endl;
			for(int i=0;i<10;i++)
			{	
				cout << normal(mu,sigma) << endl;
				sleep(1);
			}
			break;
		default:
			cout << "Invalid choice" << endl;
	}
	return 0;
}

updated.