private Algorithms.GravitationalClustering RunGCAlgorithm(ref Tuple <double[], int>[] data, Algorithms.GravitationalClustering clustering, bool sim, int rand = 4)
{
RandomizeArray(ref data, rand);
int take = data.Length / 2;
foreach (var item in data.Take(take))
{
clustering.OnlineTrainSingular(item.Item1, item.Item2);
}
Debug.WriteLine("Number of Clusters " + clustering.EnumeratePlanets().Count());
var d = data.Skip(take).Select(xx =>
{
int outP = 0;
if (sim)
{
outP = clustering.PredictSimulation(xx.Item1);
}
else
{
outP = clustering.PredictProbabilistic(xx.Item1);
}
Debug.WriteLine("Predicted Class " + outP);
if (outP == xx.Item2)
{
return(1);
}
return(0);
}).Sum();
#if DEBUG
{
Debug.WriteLine("Percent Accuracy " + ((double)d / ((double)data.Length - take)).ToString());
}
#else
{
MessageBox.Show(((double)d / ((double)data.Length - take)).ToString());
}
#endif
return(clustering);
}
private Algorithms.GravitationalClustering RunProtoGCAlgoritym(ref Tuple <double[], int>[] data, Algorithms.GravitationalClustering clustering, bool sim)
{
var newData = this.GetProto(data);
foreach (var item in newData)
{
clustering.OnlineTrainSingular(item.Item1, item.Item2);
}
Debug.WriteLine("Number of Clusters " + clustering.EnumeratePlanets().Count());
var d = data.Select(xx =>
{
int outP = 0;
if (sim)
{
outP = clustering.PredictSimulation(xx.Item1);
}
else
{
outP = clustering.PredictProbabilistic(xx.Item1);
}
Debug.WriteLine("Predicted Class " + outP);
if (outP == xx.Item2)
{
return(1);
}
return(0);
}).Sum();
#if DEBUG
{
Debug.WriteLine("Percent Accuracy " + ((double)d / ((double)data.Length)).ToString());
}
#else
{
MessageBox.Show(((double)d / ((double)data.Length)).ToString());
}
#endif
return(clustering);
}