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));
}
}
}
/// <summary>
/// Create a new dense matrix and initialize each value to the same provided value.
/// </summary>
public static DenseMatrix Create(int rows, int columns, double value)
{
if (value == 0d)
{
return(new DenseMatrix(rows, columns));
}
return(new DenseMatrix(DenseColumnMajorMatrixStorage <double> .OfInit(rows, columns, (i, j) => value)));
}
/// <summary>
/// Create a new dense matrix and initialize each value to the same provided value.
/// </summary>
public static DenseMatrix Create(int rows, int columns, Complex32 value)
{
if (value == Complex32.Zero)
{
return(new DenseMatrix(rows, columns));
}
return(new DenseMatrix(DenseColumnMajorMatrixStorage <Complex32> .OfInit(rows, columns, (i, j) => value)));
}
public Matches FindClosests(DataPoints filteredReading)
{
int n = this.reference.points.Length;
var indexes = DenseColumnMajorMatrixStorage <int> .OfInit(1, n, (i, j) => j);
var distances = DenseColumnMajorMatrixStorage <float> .OfInit(
1,
n,
(i, j) => (filteredReading.points[j].point - this.reference.points[j].point).LengthSquared());
return(new Matches
{
Dists = distances,
Ids = indexes,
});
}
public Matches FindClosests(DataPoints filteredReading)
{
int n = filteredReading.points.Length;
var results = DenseColumnMajorMatrixStorage <int> .OfInit(1, n, (i, j) => 0);
var resultDistances = DenseColumnMajorMatrixStorage <float> .OfInit(1, n, (i, j) => 0);
Vector <float> maxRadii = DenseVector.Create(n, i => this.maxDist);
var query = CreateCloudMatrix(filteredReading);
kdtree.knn(query, results, resultDistances, maxRadii, this.knn, this.epsilon, SearchOptionFlags.AllowSelfMatch);
return(new Matches
{
Ids = results,
Dists = resultDistances
});
}
public DenseMatrix(int rows, int columns, Complex value)
: this(DenseColumnMajorMatrixStorage <Complex> .OfInit(rows, columns, (i, j) => value))
{
}
/// <summary>
/// Create a new dense matrix with values sampled from the provided random distribution.
/// </summary>
public static DenseMatrix CreateRandom(int rows, int columns, IContinuousDistribution distribution)
{
return(new DenseMatrix(DenseColumnMajorMatrixStorage <Complex> .OfInit(rows, columns,
(i, j) => new Complex(distribution.Sample(), distribution.Sample()))));
}
/// <summary>
/// Create a new dense matrix and initialize each value using the provided init function.
/// </summary>
public static DenseMatrix Create(int rows, int columns, Func <int, int, Complex> init)
{
return(new DenseMatrix(DenseColumnMajorMatrixStorage <Complex> .OfInit(rows, columns, init)));
}
/// <summary>
/// Create a new dense matrix and initialize each value using the provided init function.
/// </summary>
public Matrix <T> Dense(int rows, int columns, Func <int, int, T> init)
{
return(Dense(DenseColumnMajorMatrixStorage <T> .OfInit(rows, columns, init)));
}
public DenseMatrix(int rows, int columns, double value)
: this(DenseColumnMajorMatrixStorage <double> .OfInit(rows, columns, (i, j) => value))
{
}
/// <summary>
/// Create a new dense matrix and initialize each value using the provided init function.
/// </summary>
public static DenseMatrix Create(int rows, int columns, Func<int, int, double> init)
{
return new DenseMatrix(DenseColumnMajorMatrixStorage<double>.OfInit(rows, columns, init));
}
public DenseMatrix(int rows, int columns, float value)
: this(DenseColumnMajorMatrixStorage <float> .OfInit(rows, columns, (i, j) => value))
{
}
