K-nearest Neighbors Classifier subpackage (battelle.knn)

KNearestNeighborsClassifier([n_neighbors, ...])	Instance of a k-Nearest Neighbors Classifier.
KNearestNeighborsClassifier.predict(x)	Predict the class label for the provided point
KNearestNeighborsClassifier.train(x, y)	Fit the k-nearest neighbors classifier from the training dataset.

This module allows the user to create and train k-nearest neighbor classifiers.

Here is a simple example of binary classification:

import numpy as np
import matplotlib.pyplot as plt

from battelle.knn import KNearestNeighborsClassifier

neigh = KNearestNeighborsClassifier()

x = np.random.random(size=(300,2)) - 0.5
x = [list(xi) for xi in x]

y = np.array([np.sign(np.prod(k)) for k in x])

neigh.train(x,y)

# Plot training data
plt.subplot(1,2,1)
for i, xi in enumerate(x):
    if y[i] == 1:
        plt.plot(xi[0], xi[1], 'xr')
    else:
        plt.plot(xi[0], xi[1], 'xb')
plt.title("Testing data")

# Plot neural net result
plt.subplot(1,2,2)
xrange = np.linspace(-0.5, 0.5, 20)
yrange = np.linspace(-0.5, 0.5, 20)
for x in xrange:
    for y in yrange:
        if neigh.predict([x, y]) > 0:
            plt.plot(x, y, 'xr')
        else:
            plt.plot(x, y, 'xb')

plt.title("k-NN result")
plt.show()

This code creates the following graph: