public void Knn_RandomTest()
{
Matrix <float> queryMatrix = DenseMatrix.CreateRandom(3, 100, new ContinuousUniform(-10, 10));
DenseColumnMajorMatrixStorage <float> query = (DenseColumnMajorMatrixStorage <float>)queryMatrix.Storage;
Matrix <float> points = DenseMatrix.CreateRandom(3, 100, new ContinuousUniform(-10, 10));
Vector <float> maxRadii = DenseVector.Create(100, i => float.PositiveInfinity);
var search = new KdTreeNearestNeighborSearch((DenseColumnMajorMatrixStorage <float>)points.Storage);
var results = DenseColumnMajorMatrixStorage <int> .OfInit(1, 100, (i, j) => 0);
var resultDistances = DenseColumnMajorMatrixStorage <float> .OfInit(1, 100, (i, j) => 0);
search.knn(query, results, resultDistances, maxRadii, k: 1, epsilon: float.Epsilon, optionFlags: SearchOptionFlags.AllowSelfMatch);
var bruteForceSearch = new BruteForceNearestNeighbor(points);
var results2 = DenseColumnMajorMatrixStorage <int> .OfInit(1, 100, (i, j) => 0);
var resultDistances2 = DenseColumnMajorMatrixStorage <float> .OfInit(1, 100, (i, j) => 0);
search.knn(query, results2, resultDistances2, maxRadii, k: 1, epsilon: float.Epsilon, optionFlags: SearchOptionFlags.AllowSelfMatch);
for (int i = 0; i < results.ColumnCount; i++)
{
for (int j = 0; j < results.RowCount; j++)
{
Assert.AreEqual(results2.At(j, i), results.At(j, i));
}
}
}
public KdTreeMatcher(DataPoints cloud, int knn, float epsilon, float maxDist)
{
this.knn = knn;
this.epsilon = epsilon;
this.maxDist = maxDist;
this.kdtree = new KdTreeNearestNeighborSearch(CreateCloudMatrix(cloud));
}
