utils.knn_density

utils.knn_density(k_radius, n, p, k)

Compute the kNN density estimate for a set of points.

Parameters:

k_radius : 1-dimensional numpy array of floats

The distance to each points k’th nearest neighbor.

n : int

The number of points.

p : int

The dimension of the data.

k : int

The number of observations considered neighbors of each point.

Returns:

fhat : 1D numpy array of floats

Estimated density for the points corresponding to the entries of ‘k_radius’.

See also

knn_graph

Examples

>>> X = numpy.random.rand(100, 2)
>>> knn, radii = debacl.utils.knn_graph(X, k=8, method='kd-tree')
>>> density = debacl.utils.knn_density(radii, n=100, p=2, k=8)