KNN.py

# -*- coding: utf-8 -*-
"""
Created on Fri Mar  8 19:47:46 2019

@author: sun_y
"""

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets.samples_generator import make_classification

## make training data:
## X is input and Y is output.total samples number is 1000
## X has two features and Y has 3 classes

X, Y = make_classification(n_samples=1000, n_features=2, n_redundant=0,
                             n_clusters_per_class=1, n_classes=3)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=Y)
plt.show()

###### train model
from sklearn import neighbors
clf = neighbors.KNeighborsClassifier(n_neighbors = 15 , weights='distance')
clf.fit(X, Y)


### predict
from matplotlib.colors import ListedColormap
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])


x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1


## make testing data
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                         np.arange(y_min, y_max, 0.02))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

#plot training data
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=cmap_bold)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("3-Class classification (k = 15, weights = 'distance')" )