} // end foreach
/* EndCalculation -- Complete calculation and return results. */
public EntCalcResult EndCalculation()
{
int i;
/* Complete calculation of serial correlation coefficient */
scct1 = scct1 + scclast * sccu0;
scct2 = scct2 * scct2;
scc = totalc * scct3 - scct2;
if (scc == 0.0)
{
scc = -100000;
}
else
{
scc = (totalc * scct1 - scct2) / scc;
}
/* Scan bins and calculate probability for each bin and
* Chi-Square distribution */
cexp = totalc / (binary ? 2.0 : 256.0); /* Expected count per bin */
for (i = 0; i < (binary ? 2 : 256); i++)
{
prob[i] = (double)ccount[i] / totalc;
a = ccount[i] - cexp;
chisq = chisq + (a * a) / cexp;
datasum += ((double)i) * ccount[i];
}
/* Calculate entropy */
for (i = 0; i < (binary ? 2 : 256); i++)
{
if (prob[i] > 0.0)
{
ent += prob[i] * log2(1 / prob[i]);
}
}
/* Calculate Monte Carlo value for PI from percentage of hits
* within the circle */
montepi = 4.0 * (((double)inmont) / mcount);
/* Calculate probability of observed distribution occurring from
* the results of the Chi-Square test */
double chip = Math.Sqrt(2.0 * chisq) - Math.Sqrt(2.0 * (binary ? 1 : 255.0) - 1.0);
a = Math.Abs(chip);
for (i = 9; i >= 0; i--)
{
if (chsqt[1, i] < a)
{
break;
}
}
chip = (chip >= 0.0) ? chsqt[0, i] : 1.0 - chsqt[0, i];
double compReductionPct = ((binary ? 1 : 8) - ent) / (binary ? 1.0 : 8.0);
/* Return results */
var result = new EntCalcResult
{
Entropy = ent,
ChiSquare = chisq,
ChiProbability = chip,
Mean = datasum / totalc,
ExpectedMeanForRandom = binary ? 0.5 : 127.5,
MonteCarloPiCalc = montepi,
MonteCarloErrorPct = (Math.Abs(Math.PI - montepi) / Math.PI),
SerialCorrelation = scc,
OptimumCompressionReductionPct = compReductionPct,
OccuranceCount = ccount,
NumberOfSamples = totalc
};
return(result);
}
} // end foreach
/* EndCalculation -- Complete calculation and return results. */
public EntCalcResult EndCalculation()
{
int i;
/* Complete calculation of serial correlation coefficient */
scct1 = scct1 + scclast * sccu0;
scct2 = scct2 * scct2;
scc = totalc * scct3 - scct2;
if (scc == 0.0)
{
scc = -100000;
}
else
{
scc = (totalc * scct1 - scct2) / scc;
}
/* Scan bins and calculate probability for each bin and
Chi-Square distribution */
cexp = totalc / (binary ? 2.0 : 256.0); /* Expected count per bin */
for (i = 0; i < (binary ? 2 : 256); i++)
{
prob[i] = (double) ccount[i] / totalc;
a = ccount[i] - cexp;
chisq = chisq + (a * a) / cexp;
datasum += ((double) i) * ccount[i];
}
/* Calculate entropy */
for (i = 0; i < (binary ? 2 : 256); i++)
{
if (prob[i] > 0.0)
{
ent += prob[i] * log2(1 / prob[i]);
}
}
/* Calculate Monte Carlo value for PI from percentage of hits
within the circle */
montepi = 4.0 * (((double) inmont) / mcount);
/* Calculate probability of observed distribution occurring from
the results of the Chi-Square test */
double chip = Math.Sqrt(2.0 * chisq) - Math.Sqrt(2.0 * (binary ? 1 : 255.0) - 1.0);
a = Math.Abs(chip);
for (i = 9; i >= 0; i--)
{
if (chsqt[1,i] < a)
{
break;
}
}
chip = (chip >= 0.0) ? chsqt[0,i] : 1.0 - chsqt[0,i];
double compReductionPct = ((binary ? 1 : 8) - ent) / (binary ? 1.0 : 8.0);
/* Return results */
EntCalcResult result = new EntCalcResult();
result.Entropy = ent;
result.ChiSquare = chisq;
result.ChiProbability = chip;
result.Mean = datasum / totalc;
result.ExpectedMeanForRandom = binary ? 0.5 : 127.5;
result.MonteCarloPiCalc = montepi;
result.MonteCarloErrorPct = (Math.Abs(Math.PI - montepi) / Math.PI);
result.SerialCorrelation = scc;
result.OptimumCompressionReductionPct = compReductionPct;
result.OccuranceCount = this.ccount;
result.NumberOfSamples = this.totalc;
return result;
}
