public Matrix Randomize(double lowest = 0.0, double highest = 1.0, EDistribution distribution = EDistribution.Uniform)
{
var _mat = Duplicate();
_mat.InRandomize(lowest, highest, distribution);
return(_mat);
}
/// <summary>
/// Get a random matrix filled with a custom distribution of random numbers
/// </summary>
/// <param name="lowest">Lower bound</param>
/// <param name="highest">Upper bound</param>
/// <param name="distribution">Distribution type</param>
/// <returns>A random matrix filled with a custom distribution of random numbers</returns>
public Matrix Randomize(double lowest = 0.0, double highest = 1.0,
EDistribution distribution = EDistribution.Gaussian)
{
var mat = Duplicate();
mat.InRandomize(lowest, highest, distribution);
return(mat);
}
/// <summary>
/// Fill the matrix with a custom distribution of random numbers
/// </summary>
/// <param name="lowest">Lower bound</param>
/// <param name="highest">Upper bound</param>
/// <param name="distribution">Distribution type</param>
public void InRandomize(double lowest = 0.0, double highest = 1.0, EDistribution distribution = EDistribution.Gaussian)
{
var diff = highest - lowest;
switch (distribution)
{
case EDistribution.Invalid:
{
throw new InvalidOperationException("Invalid Random Distribution Mode!");
}
case EDistribution.Uniform:
{
var random = new Random();
Parallel.For(0, Rows, row =>
{
for (var col = 0; col < Columns; col++)
{
_matrix[row, col] = random.NextDouble() * diff + lowest;
}
});
break;
}
case EDistribution.Normal:
case EDistribution.Gaussian:
{
var dispersion = diff / 2;
var random = new Random();
Parallel.For(0, Rows, row =>
{
for (var col = 0; col < Columns; col++)
{
var u1 = 1.0 - random.NextDouble();
var u2 = 1.0 - random.NextDouble();
var randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2);
//random normal(mean,stdDev^2)
var randNormal = 0 + dispersion * randStdNormal;
_matrix[row, col] = randNormal;
}
});
break;
}
default:
throw new InvalidOperationException("Invalid Random Distribution Mode!");
}
}
public void InRandomize(double lowest = 0.0, double highest = 1.0, EDistribution distribution = EDistribution.Uniform)
{
double _diff = highest - lowest;
if (distribution == EDistribution.Invalid)
{
throw new InvalidOperationException("Invalid Random Distribution Mode!");
}
else if (distribution == EDistribution.Uniform)
{
Random _random = new Random();
for (int _row = 0; _row < _matrix.GetLength(0); _row++)
{
for (int _col = 0; _col < _matrix.GetLength(1); _col++)
{
_matrix[_row, _col] = (_random.NextDouble() * _diff) + lowest;
}
}
}
else if (distribution == EDistribution.Gaussian)
{
double _dispersion = _diff / 2;
Random random = new Random(); //reuse this if you are generating many
for (int _row = 0; _row < _matrix.GetLength(0); _row++)
{
for (int _col = 0; _col < _matrix.GetLength(1); _col++)
{
//uniform(0,1] random doubles
double u1 = 1.0 - random.NextDouble();
double u2 = 1.0 - random.NextDouble();
//random normal(0,1)
double randStdNormal = System.Math.Sqrt(-2.0 * System.Math.Log(u1)) * System.Math.Sin(2.0 * System.Math.PI * u2);
//random normal(mean,stdDev^2)
double randNormal = 0 + _dispersion * randStdNormal;
_matrix[_row, _col] = randNormal;
}
}
}
}
/// <summary>
/// The distribution to use for calculating the random matrix. Sparse1 is:
/// sqrt(3) * { -1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6) } Sparse2
/// is: { -1 with prob(1/2), +1 with prob(1/2) }
/// </summary>
public RandomProjection Distribution(EDistribution newDstr)
{
Impl.setDistribution(new weka.core.SelectedTag((int)newDstr, weka.filters.unsupervised.attribute.RandomProjection.TAGS_DSTRS_TYPE));
return(this);
}
