/** Method: Load data for calculation
* data - data from time series
* n - number (real) of convolutions */
void IConvolution.LoadData(List <double> data, double n)
{
if (data.Count == 0)
{
throw new Exception("Error. No data");
}
double res = n - (int)n;
this.n = n;
convData.Clear();
hist.Clear();
de = new KernelDensity(1, 100, 100);
int [] indexes = new int[(int)n + 1];
int indexRes = -1;
double sum;
for (int i = 0; i < maxIt / 2; i++)
{
sum = 0;
for (int j = 1; j <= n; j++)
{
indexes[j - 1] = rg.NextInt(0, data.Count - 1);
sum += data[indexes[j - 1]];
}
if (res > 0)
{
indexRes = rg.NextInt(0, data.Count - 1);
sum = sum + data[indexRes] * res;
}
convData.Add(sum);
sum = 0;
for (int j = 1; j <= n; j++)
{
sum += data[data.Count - 1 - indexes[j - 1]];
}
if (res > 0)
{
sum = sum + data[data.Count - 1 - indexRes] * res;
}
convData.Add(sum);
}
hist.LoadData(convData);
de.LoadHist(hist);
de.SetMaxInt();
}
private void LoadMontConvolution()
{
List <double> convData = new List <double>();
Histogram convHist = new Histogram();
double sum;
for (int i = 0; i < maxIt; i++)
{
sum = 0;
foreach (Distribution dist in distributions)
{
dist.RawData = dist.Histogram.GetRawData();
}
foreach (Distribution dist in distributions)
{
int nLeadTimesInt = (int)dist.NLeadTimes;
double nLeadTimesRes = dist.NLeadTimes - nLeadTimesInt;
int[] indexes = new int[(int)dist.NLeadTimes + 1];
int indexRes = -1;
for (int j = 1; j <= nLeadTimesInt; j++)
{
indexes[j - 1] = rg.NextInt(0, dist.RawData.Count - 1);
sum += dist.RawData[indexes[j - 1]];
}
if (nLeadTimesRes > 0)
{
indexRes = rg.NextInt(0, dist.RawData.Count - 1);
sum = sum + dist.RawData[indexRes] * nLeadTimesRes;
}
convData.Add(sum * dist.Factor);
}
}
convHist.LoadData(convData);
de = new KernelDensity(1, 100, 100);
de.LoadHist(convHist);
de.SetMaxInt();
}