public MaximumChangeRatios GetMaximumChangeRatios(double[][] array)
{
var result = new MaximumChangeRatios {
Down = double.MinValue, Up = double.MinValue
};
for (var i = 0; i < array.Length - 1; i++)
{
var diff = CalculatePercentChange(array[i][3], array[i + 1][3]);
var checksum = array[i + 1][3] - array[i][3];
// down
if (checksum < 0)
{
if (result.Down < Math.Abs(diff))
{
result.Down = Math.Abs(diff);
}
}
// up
else
{
if (result.Up < diff)
{
result.Up = diff;
}
}
}
return(result);
}
public double[][] GenerateNextStagePopulation <TDistribution>(double[][] current, int k, int n, MaximumChangeRatios ratios, IHiddenMarkovModel <TDistribution> model)
where TDistribution : IDistribution
{
var dic = new SortedList <double, double[]>();
for (var j = 0; j < current.Length; j++)
{
var h = HeuristicFunction(current[j], model);
if (dic.Count < k)
{
dic.Add(h, current[j]);
}
else
{
if (dic.Keys[dic.Count] < h)
{
dic.Remove(dic.Keys[dic.Count]);
dic.Add(h, current[j]);
}
}
}
current = (from e in dic select e.Value).ToArray();
current = ExpandCurrentPopulation(current, n, ratios.Down, ratios.Up);
return(current);
}
public double[][] Search <TDistribution>(double[][] population, int k, int n, int numberOfIterations, MaximumChangeRatios ratios, IHiddenMarkovModel <TDistribution> model)
where TDistribution : IDistribution
{
var current = ExpandCurrentPopulation(population, n, ratios.Down, ratios.Up);;
for (var i = 0; i < numberOfIterations; i++)
{
var next = GenerateNextStagePopulation(current, k, n, ratios, model);
current = next;
}
return(current);
}
