Making mir.random.ndvariable.multivariateNormalVar create bigger data sets than 2

[jmh530]

On Tuesday, 27 February 2018 at 09:23:49 UTC, kerdemdemir wrote:
> I need a classifier in my project.
> Since it is I believe most easy to implement I am trying to 
> implement logistic regression.
>
> I am trying to do the same as the python example:  
> https://beckernick.github.io/logistic-regression-from-scratch/
>
> I need to data sets with which I will test.
>
> This works(https://run.dlang.io/is/yGa4a0) :
>
> 	double[2] x1;
> 	Random* gen = threadLocalPtr!Random;
> 	
> 	auto mu = [0.0, 0.0].sliced;
> 	auto sigma = [1.0, 0.75, 0.75, 1].sliced(2,2);
> 	auto rv = multivariateNormalVar(mu, sigma);
> 	rv(gen, x1[]);
> 	writeln(x1);
> 	
> But when I increase my data set size from double[2] to 
> double[100] I am getting an assert :
>
> mir-random-0.4.3/mir-random/source/mir/random/ndvariable.d(378): Assertion failure
>
> which is:
> assert(result.length == n);
>
> How can I have a result vector which has size like 5000 
> something?
>
> Erdemdem

I haven't made much use of mir.random yet...

The dimension 2 in this case is the size of the dimension of the 
random variable. What you want to do is simulate multiple times 
from this 2-dimensional random variable.

It looks like the examples on the main Readme page uses 
mir.random.algorithm.range. I tried below, but I got errors. I 
did notice that the MultivariateNormalVariable documentation says 
that it is in beta still.

void main()
{
     import mir.random : Random, unpredictableSeed;
     import mir.random.ndvariable : MultivariateNormalVariable;
     import mir.random.algorithm : range;
     import mir.ndslice.slice : sliced;
     import std.range : take;

     auto mu = [10.0, 0.0].sliced;
     auto sigma = [2.0, -1.5, -1.5, 2.0].sliced(2,2);

     auto rng = Random(unpredictableSeed);
     auto sample = range!rng
         		(MultivariateNormalVariable!double(mu, sigma))
         		.take(10);
}

However, doing it manually with a for loop works.

void main()
{
     import mir.random : rne;
     import mir.random.ndvariable : multivariateNormalVar;
     import mir.random.algorithm : range;
     import mir.ndslice.slice : sliced;
     import std.stdio : writeln;

     auto mu = [10.0, 0.0].sliced;
     auto sigma = [2.0, -1.5, -1.5, 2.0].sliced(2,2);

     auto rv = multivariateNormalVar(mu, sigma);

     double[2][100] x;
     for (size_t i = 0; i < 100; i++) {
         rv(rne, x[i][]);
     }
     writeln(x);
}

Nevertheless, it probably can't hurt to file an issue if you 
can't get something like the first one to work. I would think it 
should just work.