private static void Create(int n1, int n2, int k, out double[][] inputs, out NaiveKNearestNeighbors naive, out KNearestNeighbors <double[]> normal, out KNearestNeighbors target)
{
int n = n1 + n2;
double[][] gauss1 = MultivariateNormalDistribution.Generate(n1,
mean: new double[] { 2, 1 },
covariance: new double[, ]
{
{ 1, 0 },
{ 0, 1 },
});
double[][] gauss2 = MultivariateNormalDistribution.Generate(n2,
mean: new double[] { -1, 4 },
covariance: new double[, ]
{
{ 2, 1 },
{ 0, 3 },
});
inputs = gauss1.Stack(gauss2);
int[] outputs = Matrix.Vector(n1, 0).Concatenate(Matrix.Vector(n2, +1));
var idx = Vector.Sample(n1 + n2);
inputs = inputs.Submatrix(idx);
outputs = outputs.Submatrix(idx);
naive = new NaiveKNearestNeighbors(k, inputs, outputs);
normal = new KNearestNeighbors <double[]>(k, inputs, outputs, new Euclidean());
target = new KNearestNeighbors(k, inputs, outputs);
}
private static void Create(int n1, int n2, int k, out double[][] inputs, out NaiveKNearestNeighbors naive, out KNearestNeighbors<double[]> normal, out KNearestNeighbors target)
{
int n = n1 + n2;
double[][] gauss1 = MultivariateNormalDistribution.Generate(n1,
mean: new double[] { 2, 1 },
covariance: new double[,]
{
{ 1, 0 },
{ 0, 1 },
});
double[][] gauss2 = MultivariateNormalDistribution.Generate(n2,
mean: new double[] { -1, 4 },
covariance: new double[,]
{
{ 2, 1 },
{ 0, 3 },
});
inputs = gauss1.Stack(gauss2);
int[] outputs = Matrix.Vector(n1, 0).Concatenate(Matrix.Vector(n2, +1));
var idx = Vector.Sample(n1 + n2);
inputs = inputs.Submatrix(idx);
outputs = outputs.Submatrix(idx);
naive = new NaiveKNearestNeighbors(k, inputs, outputs);
normal = new KNearestNeighbors<double[]>(k, inputs, outputs, new Euclidean());
target = new KNearestNeighbors(k, inputs, outputs);
}
