Efficient approximate kernel density estimation for numpy/scipy.
In the naive approach to KDE, the kernel pdf is evaluated for every known data point. The ApproximateKernelDensity class in kdense improves on this by only evaluating the pdf against points that are close enough to make a meaningful contribution to the outcome. Depending on data set and parameters, this can improve performance by orders of magnitude.
ApproximateKernelDensity uses a Gaussian kernel with (configurable) standard deviation 1.0. Its std_cutoff parameter determines how many standard deviations away a point should be before being ignored, and defaults to 5.
sudo pip install kdensefrom kdense import ApproximateKernelDensity
import numpy as np
from numpy import random
data = np.array([
random.normal(loc=50.0, scale=20.0) for _ in xrange(200)
])
model = ApproximateKernelDensity(data)
probability_of_30 = model.pdf(30.0)