/// <summary>
/// Returns the corresponding distribution.
/// </summary>
/// <param name="wholeDistribution">the distribution of all configurations</param>
/// <param name="allBuckets">all buckets of the distribution</param>
/// <returns>the user-specified distribution (e.g., uniform)</returns>
public override Dictionary <double, double> CreateDistribution(Dictionary <double, List <Configuration> > wholeDistribution, List <double> allBuckets)
{
// If a normal distribution is wanted, the parameters can be computed
// from the whole population if available
if (this.distribution is NormalDistribution && !this.strategyParameter[USE_WHOLE_POPULATION].Equals("false"))
{
Dictionary <double, int> distr = DistributionUtils.CountConfigurations(wholeDistribution);
double sum = 0;
int count = 0;
foreach (double key in distr.Keys)
{
sum += key * distr[key];
count += distr[key];
}
// Compute the mean value of the whole distribution
double mean = sum / count;
// Compute the deviation
double deviation = Math.Sqrt(distr.Sum(x => x.Value * Math.Pow(x.Key - mean, 2)) / count);
return(((NormalDistribution)this.distribution).CreateDistribution(allBuckets, mean, deviation));
}
else
{
return(this.distribution.CreateDistribution(allBuckets));
}
}
/// <summary>
/// See <see cref="IDistribution.CreateDistribution(List{double})"/>.
/// </summary>
public Dictionary <double, double> CreateDistribution(List <double> allBuckets)
{
Dictionary <double, double> result = new Dictionary <double, double>();
double previousResultCache = 0;
for (int i = 0; i < allBuckets.Count; i++)
{
double currentResult = 1 - Math.Pow(1 - PROBABILITY, i + 1);
result[allBuckets[i]] = currentResult - previousResultCache;
previousResultCache = currentResult;
}
return(DistributionUtils.AdjustToOne(result));
}
/// <summary>
/// Creates the distribution by using the given mean and deviation value.
/// </summary>
/// <param name="allBuckets">All buckets.</param>
/// <param name="mean">The mean for the normal distribution.</param>
/// <param name="deviation">The standard deviation for the normal distribution.</param>
public virtual Dictionary <double, double> CreateDistribution(List <double> allBuckets, double mean, double deviation)
{
Dictionary <double, double> result = new Dictionary <double, double>();
double previousResultCache = 0;
for (int i = 0; i < allBuckets.Count; i++)
{
double currentResult = Normal.CDF(mean, deviation, allBuckets[i]);
result[allBuckets[i]] = currentResult - previousResultCache;
previousResultCache = currentResult;
}
return(DistributionUtils.AdjustToOne(result));
}
/// <summary>
/// See <see cref="IDistribution.CreateDistribution(List{double})"/>.
/// </summary>
public Dictionary <double, double> CreateDistribution(List <double> allBuckets)
{
GeometricDistribution geometricDistribution = new GeometricDistribution();
Dictionary <double, double> original = geometricDistribution.CreateDistribution(allBuckets);
Dictionary <double, double> result = new Dictionary <double, double>();
for (int i = 0; i < allBuckets.Count; i++)
{
double index = allBuckets[i];
double inverseIndex = allBuckets[allBuckets.Count - 1 - i];
result[index] = original[index] + original[inverseIndex];
}
return(DistributionUtils.AdjustToOne(result));
}
/// <summary>
/// See <see cref="Distribution.CreateDistribution(List{double})"/>.
/// </summary>
public Dictionary <double, double> CreateDistribution(List <double> allBuckets)
{
Dictionary <double, double> result = new Dictionary <double, double>();
int numberOfBuckets = allBuckets.Count - 1;
for (int k = 0; k <= numberOfBuckets; k++)
{
double firstPart = (Factorial(numberOfBuckets) / (Factorial(k) * Factorial(numberOfBuckets - k)));
double secondPart = Math.Pow(PROBABILITY, k) * Math.Pow((1 - PROBABILITY), (numberOfBuckets - k));
double probability = firstPart * secondPart;
result[allBuckets[k]] = probability;
}
// Adjust it to be 1.0 in the sum
result = DistributionUtils.AdjustToOne(result);
return(result);
}
