codes for assignment3 of ENGG5103

3a.py

#Reference: http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors

# Set to 1 for 3a, set to 3 for 3b
n_neighbors = 3

X = [[1, 1], [1, 2], [1, 0], [2, 0], [0.5, 0.5], [0, 0], [4, 0], [1, 3], [2, 2], [3, 1]]
X = np.asarray(X)
y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
y = np.asarray(y)
h = .02  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors)
clf.fit(X, y)


# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                edgecolor='k', s=20)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.text(3.7, 3.8, 'red: Class C1', color='red')
plt.text(3.7, 3.6, 'blue: Class C2', color='blue')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

# Predict result
print(clf.predict([-3, 0]))
print(clf.predict([0, 4]))