/// <summary>Initializes of RandomGenerator.</summary>
/// <param name="randGen">Type of random generator:
/// 0 Mersenne Twister, 1 O'Neill, 2 C std, 3 XorShift, 4 C# std, 5 C# strong.</param>
/// <param name="normGen">Type of Gauss generator:
/// -1 no generator, 0 Abramowitz and Stegun, 1 Central Limit Theorem,
/// 2 Fog, 3Marsaglia, 4 C/C# std</param>
/// <param name="isExp">True if include lognorm generation</param>
/// <param name="isStdLogNorm">True if use C++ standard lognorm generation</param>
/// <param name="distVars">Gauss or lognorm mean and sigma</param>
/// <param name="ssVars">Size selection distribution mean and sigma, or null if size selection is inactive</param>
/// <returns>False if extern CRandom dll is not found, otherwise true</returns>
public static bool Init(int randGen, int normGen, bool isExp, bool isStdLogNorm,
float[] distVars, int[] ssVars)
{
Library = randGen < FIRST_IND_CSH ? ON_C : (randGen == FIRST_IND_CSH + 1 ? ON_SCH_STRONG : ON_CSH);
IsExp = isExp;
DistVars = distVars;
if (normGen == -1 && !isExp)
{
DistVars[0] = 0; DistVars[1] = 1000;
}
if (Library == ON_C)
{
if (System.IO.File.Exists(CRandom.FullDllFile))
{
CRandom.Init(randGen, normGen, isExp, isStdLogNorm, distVars, ssVars);
}
else
{
return(false);
}
}
else
{
RandomNumb = GetRandom;
CshStrongRandomGen.RecordGaussGen(normGen);
Distrib = Distribs[normGen + 1];
}
if (ssVars != null) // size selection is able
{
ssMean = ssVars[0];
//ssDVariance = 2 * ssVars[1] * ssVars[1];
//ssFactor = ssVars[1] * Math.Sqrt(2 * Math.PI);
//ssAlignRatio = 2.5 * ssVars[1];
ssFactor0 = (float)(ssVars[1] * Math.Sqrt(2));
ssFactor1 = 2.5f / (float)Math.Sqrt(2 * Math.PI);
}
else
{
ssMean = 0;
}
return(true);
}
/// <summary>Fills data by current distribution.</summary>
/// <param name="numbers">Data that should be filled</param>
/// <param name="cnt">Number or repetitions. Returned the number of random generator calls.</param>
/// <returns>Average Y value.</returns>
public static float GetDistrib(Numbers numbers, ref int cnt)
{
GenCallCnt = 0;
if (Library == ON_C)
{
float res = CRandom.GetDistrib(
numbers != null ? numbers.Data : null, ref cnt, ref GenCallCnt);
cnt = GenCallCnt;
return(res);
}
else if (Library == ON_SCH_STRONG)
{
CshStrongRandomGen gen = new CshStrongRandomGen(cnt);
RandomNumb = gen.GetNext;
}
decimal sum = 0;
int val;
int actualCnt = 0;
double dval;
float ssDev; // result of inverse normal distrib
int min, max;
for (int i = 0; i < cnt; i++)
{
dval = Distrib() * DistVars[1] + DistVars[0];
if (IsExp) // lognorm is able
{
val = (int)Math.Exp(dval);
if (ssMean > 0) // size selection is able
// === simple method
//if (GetRandom() > ssAlignRatio *
// Math.Exp(-Math.Pow(dval - ssMean, 2) / ssDVariance) / ssFactor)
// continue; // filter by size selection
// === method with using min and max
{
ssDev = ssFactor0 * (float)Math.Sqrt(Math.Log(ssFactor1 / GetRandom()));
min = (int)(ssMean - ssDev);
max = (int)(ssMean + ssDev);
if (val < min || val > max)
{
continue; // filter by size selection
}
}
}
else // lognorm is disable
{
val = (int)dval;
// round up val: otherwise all values will be round down during casting to int.
// in this case the distribution will not only be inaccurate,
// but worse - values less than 1 will be rounded to 0,
// which leads to a falsely large number of zero points.
// The variant val = (int)Math.Round(dval, 0) is much slower
if (dval > 0)
{
if (dval - val >= 0.5)
{
val++;
}
} // round up positives
else
{
if (dval - val <= -0.5)
{
val--;
}
} // round up negatieves
}
sum += val;
if (numbers != null)
{
numbers.Data[actualCnt] = val;
}
actualCnt++;
}
cnt = GenCallCnt;
return((float)sum / actualCnt);
}
