/// <summary>
/// Generates a simulation cube of uniform or normal distributed samples
/// </summary>
/// <param name="nsims"> total number of simulations to be generated</param>
/// <param name="dimension"> Dimensionality of the random vectos to be used</param>
/// <param name="nsteps">Number of samples per dimension that need to be generated per simulation</param>
public SimulationCube(UInt64 nsims, UInt64 dimension, UInt64 nsteps, DISTRIBUTION distribution = DISTRIBUTION.NORMAL)
{
number_of_sims = nsims;
number_of_dims = dimension;
number_of_steps = nsteps;
simulation_vector_length = number_of_dims * number_of_steps;
GenerateUCube(distribution);
}
private void GenerateUCube(DISTRIBUTION d)
{
if (array != null)
{
return;
}
array = new double[number_of_sims][][];
var uniform = new MathNet.Numerics.Random.MersenneTwister(seed);
var normal = new MathNet.Numerics.Distributions.Normal(0, 1, uniform);
Func <double[]> rgn = null;
switch (d)
{
case DISTRIBUTION.UNIFORM:
rgn = () =>
{
return(uniform.NextDoubles((int)number_of_steps));
};
break;
case DISTRIBUTION.NORMAL:
rgn = () =>
{
double[] r = new double[number_of_steps];
normal.Samples(r);
return(r);
};
break;
}
for (UInt64 i = 0; i < number_of_sims; ++i)
{
array[i] = new double[number_of_dims][];
for (UInt64 j = 0; j < number_of_dims; ++j)
{
array[i][j] = rgn();
}
}
}
