Random normal distribution by Gaussian in C++ - c++

Question

I have my function in Python for normal distribution. I need to convert it to C++ and i am not familiar with language.
Here is my Python:
def calculation(value):
sigma = 0.5
size = 10000
x = 200
x_distribution = np.random.normal(value, sigma, size)
for i in x_distribution:
x.append(i)
return x
And it works as expected. I am trying to re-write same thing in C++ and found only the Link and where the "std::normal_distribution<> d{5,2};
" has to make magic. But i could not figure it out how to implement.
Here what i have tried and it is failing.
# include frame.distribution
Frame DistributionModel(x_mu, x_sigma)
{
// Motion model;ignore it
model = std::normal_distribution<> d{x_mu,x_sigma};
return model;
}
Please, help me. Looking for any hints. Thanks.

Answer 1

Well, trouble without end...
# include frame.distribution
Syntax for inclusion is:
#include <name_of_header_file>
// or:
#include "name_of_header_file"
(The space in between # and include does not harm, but is absolutely uncommon...)
Frame DistributionModel(x_mu, x_sigma)
C++ is a strongly typed language, i. e. you cannot just give variables a name as in Python, but you need to give them a type!
Frame DistributionModel(double x_mu, double x_sigma)
Same for local variables; type must match what you actually assign to (unless using auto)
std::normal_distribution<double> nd(x_mu, x_sigma);
This is a bit special about C++: You define a local variable, e. g.
std::vector<int> v;
In case of a class, it gets already constructed using its default constructor. If you want to call a constructor with arguments, you just append the call to the variable name:
std::vector<int> v(10); // vector with 10 elements.
What you saw in the sample is a feature called "uniform initialisation", using braces instead of parentheses. I personally strongly oppose against its usage, though, so you won't ever see it in code I have written (see me constructing the std::normal_distribution above...).
std::normal_distribution is defined in header random, so you need to include it (before your function definition):
#include <random>
About the return value: You only can return Frame, if the data type is defined somewhere. Now before trying to define a new class, we just can use an existing one: std::vector (it's a template class, though). A vector is quite similar to a python list, it is a container class storing a number of objects in contiguous memory; other than python lists, though, the type of all elements stored must be the same. We can use such a vector to collect the results:
std::vector<double> result;
Such a vector can grow dynamically, however, this can result in necessity to re-allocate the internal storage memory. Costly. If you know the number of elements in advance, you can tell the vector to allocate sufficient memory in advance, too:
result.reserve(max);
The vector is what we are going to return, so we need to adjust the function signature (I allowed to give it a different name and added another parameter):
std::vector<double> getDistribution(double x_mu, double x_sigma, size_t numberOfValues)
It would be possible to let the compiler deduce the return type, using auto keyword for. While auto brings quite a lot of benefits, I do not recommend it for given purpose: With explicit return type, users of the function see right from the signature what kind of result to expect and do not have to look into the function body to know about.
std::normal_distribution now is a number generator; it does not deliver the entire sequence at once as the python equivalent does, you need to draw the values one by another explicitly:
while(numberOfValues-- > 0)
{
auto value = nd(gen);
result.push_back(value);
}
nd(gen): std::normal_distribution provides a function call operator operator(), so objects of can be called just like functions (such objects are called "functors" in C++ terminology). The function call, however, requires a random number generator as argument, so we need to provide it as in the example you saw. Putting all together:
#include <random>
#include <vector>
std::vector<double> getDistribution
(
double x_mu, double x_sigma, size_t numberOfValues
)
{
// shortened compared to your example:
std::mt19937 gen((std::random_device())());
// create temporary (anonymous) ^^
// instance and call it immediately ^^
// afterwards
std::normal_distribution<double> nd(x_mu, x_sigma);
std::vector<double> result;
result.reserve(numberOfValues);
while(numberOfValues-- > 0)
{
// shorter than above: using result of previous
// function (functor!) call directly as argument to next one
result.push_back(nd(gen));
}
// finally something familiar from python:
return result;
}

Answer 2

#include<iostream>
#include<random>
#include<chrono>
int main() {
unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
std::default_random_engine generator(seed);
std::normal_distribution<double> distribution(0.0, 3.0);
double number = abs(distribution(generator));
std::cout << number;
std::cin.get();
return 0;
}
This may help, create a random number using gaussian with mean=0.0 and std_dev= 3.0