private void Initialize(int N, int Dims, List <int>[] ids, double[][] P, double[] Y)
{
this.N = N;
this.Dims = Dims;
this.ids = ids;
this.P = P;
this.Y = Y;
// initialize reusable allocations
Grad = new double[N * Dims];
GradT = new double[N * Dims];
Ydelta = new double[N * Dims];
gains = new double[N * Dims];
for (int i = N * Dims - 1; i >= 0; i--)
{
gains[i] = 1;
}
//initialize repulsion methods
ChosenRepulsionMethod = GradientConfig.RepulsionMethod;
if (ChosenRepulsionMethod == RepulsionMethods.auto || ChosenRepulsionMethod == RepulsionMethods.barnes_hut)
{
HST = new HashSpatialTree(N, Dims, Y);
}
if (ChosenRepulsionMethod == RepulsionMethods.auto || ChosenRepulsionMethod == RepulsionMethods.fft)
{
InitializeFFTRepulsion(Y, GradT);
}
}
private void ChooseRepulsionMethod(long[] test_repulsion)
{
long min_time = long.MaxValue;
for (int i = 1; i < test_repulsion.Length; i++)
{
if (min_time > test_repulsion[i])
{
min_time = test_repulsion[i];
ChosenRepulsionMethod = (RepulsionMethods)i;
}
}
Console.WriteLine("Chosen repulsion method:\t{0}", ChosenRepulsionMethod);
if (ChosenRepulsionMethod != RepulsionMethods.barnes_hut)
{
HST = null;
}
if (ChosenRepulsionMethod != RepulsionMethods.fft)
{
FFTR = null;
}
}
private static RepulsionMethods UpdateRepulsionMethod(Dictionary <string, object> param, RepulsionMethods value)
{
string parameter = "RepulsionMethod";
if (param.TryGetValue(parameter, out object val))
{
param.Remove(parameter);
switch (Convert.ToString(val))
{
case "auto":
value = RepulsionMethods.auto;
break;
case "barnes_hut":
value = RepulsionMethods.barnes_hut;
break;
case "fft":
value = RepulsionMethods.fft;
break;
default:
Console.WriteLine("\tUnknown value {} for RepulsionMethod!");
break;
}
Console.WriteLine("\tSet {0} to:\t{1}", parameter, value);
}
return(value);
}
private double[] GradientStep(double exagg, RepulsionMethods method)
{
Task ATask = Task.Run(() =>
{
switch (Dims)
{
case 1:
Grad = GradientAttractive1(Grad, exagg);
break;
case 2:
Grad = GradientAttractive2(Grad, exagg);
break;
case 3:
Grad = GradientAttractive3(Grad, exagg);
break;
default:
Grad = GradientAttractive(Grad, exagg);
break;
}
});
double Z = 0;
Task RTask = Task.Run(() =>
{
switch (method)
{
case RepulsionMethods.barnes_hut:
HST.RebuildTree(BarnesHutConfig.Presort);
Array.Clear(GradT, 0, GradT.Length);
Z = HST.EstimateRepulsion(GradT, BarnesHutConfig.theta2);
break;
case RepulsionMethods.fft:
FFTR.ComputeFFTGradient();
break;
}
});
Task.WaitAll(new Task[] { RTask, ATask });
switch (method)
{
case RepulsionMethods.barnes_hut:
Z = 1.0 / Z;
for (int i = N * Dims - 1; i >= 0; i--)
{
Grad[i] += GradT[i] * Z;
}
break;
case RepulsionMethods.fft:
for (int i = N * Dims - 1; i >= 0; i--)
{
Grad[i] += GradT[i];
}
break;
}
return(Grad);
}
