static void Main(string[] args)
{
//RTreeQueryEvaluator qe = new RTreeQueryEvaluator("CA.txt", 100);
//int result = qe.KnnQuery(new Point(35f, -118f, 0f), 4);
//------------
RTreeQueryEvaluator qe = new RTreeQueryEvaluator("CA.txt", 300);
//int count = qe.RangeQuery(new Point(35f, -118f, 0f), 1f);
//From 1 to 5 percent
for (int k = 1; k <= 10; k++)
{
//Calculate the range for this percenage.
//double area = TotalArea * 0.1 * p;
//double kmRange = Math.Sqrt(area / Math.PI);
//double degRange = kmRange / kmPerDeg;
Console.WriteLine(k + " Nearest Neighbor");
//For each experiment, run 100 times
for (int i = 0; i < 100; i++)
{
double x = RandX();
Console.WriteLine(qe.KnnQuery(
new Point((float)x, (float)y(x)),
k));
}
}
//for (int i = 0; i < 30; i++)
// Console.WriteLine(RandX());
}
public ExperimentResults<int> RTreeKnnQuery(
string dataFile, int fanout, int min, int max,
int inc, int trials)
{
RTreeQueryEvaluator qe = new RTreeQueryEvaluator(dataFile, fanout);
ExperimentResults<int> result = new ExperimentResults<int>(
"knn_rtree_" + fanout + '_' + min + '_' + max);
for (int k = 1; k <= 10; k+=inc)
{
result.AddCategory(k + " NN");
//For each experiment, run trial number of times
for (int i = 0; i < trials; i++)
{
result.AddResult(qe.KnnQuery(
qp.GetRandomQueryPoint(),
k));
}
}
return result;
}
