CS545 fall 2016

First some code for plotting the results:

decision_boundary

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
 
def plot_boundary(classifier, X, y) :
 
    classifier.fit(X, y)
 
    h = .02  # mesh size
    # Create color maps
    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
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Z = classifier.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)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.show()

nearest_neighbors.py

"""
Nearest neighbor classification with scikit-learn
full details at:
http://scikit-learn.org/stable/modules/neighbors.html#classification
"""
 
import numpy as np
from sklearn import neighbors, datasets
import decision_boundary
 
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # take the first two features.
y = iris.target
 
# the parameters of the scikit-learn nearest neighbor
# classifier:
# sklearn.neighbors.KNeighborsClassifier(n_neighbors=5,
# weights='uniform', algorithm='auto', leaf_size=30, p=2,
# metric='minkowski') 
# weights refers to how to weight each example
# 'algorithm' is the choice of algorithm for storing the
# training data ('brute', 'ball_tree', 'kd-tree')
# complete description of the available metrics:
# http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html#sklearn.neighbors.DistanceMetric
 
classifier = neighbors.KNeighborsClassifier(n_neighbors=10)
 
decision_boundary.plot_boundary(classifier, X, y)