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%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
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from sklearn.datasets import make_moons, make_circles
# make classification problem
X, y = make_moons(n_samples=500, noise=0.3)
# represent the coordinates in several convenient ways
I, J = X.T
IJ = X[:,:2]
plt.gca().set_aspect('equal')
plt.scatter(I, J, c=y, cmap='cool')
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from scipy.spatial import cKDTree
from scipy import stats
# build a KDTree from the points
kdt = cKDTree(IJ)
# now make a grid; we will predict a class for each grid point
resolution = 2000
imin, imax = np.min(I), np.max(I)
jmin, jmax = np.min(J), np.max(J)
m = np.linspace(imin, imax, resolution)
n = np.linspace(jmin, jmax, resolution)
M, N = np.meshgrid(m,n)
MN = np.dstack((M,N)).reshape(-1,2)
# now find the k nearest neighbors for each grid point
dist, nabe = kdt.query(MN, k=7)
# find the class of each neighbor
k_classes = y[nabe]
# take a majority vote using the mode function
pred, _ = stats.mode(k_classes,axis=1)
pred_img = pred.reshape((resolution,resolution))
plt.gca().set_aspect('equal')
plt.scatter(I, J, c=y, cmap='cool')
plt.imshow(pred_img,origin='lower',cmap='cool',extent=[imin, imax, jmin, jmax])
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