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code:nearest_neighbors [CS545 fall 2016]

# CS545 fall 2016

### Sidebar

CS545

Instructor
Asa Ben-Hur

code:nearest_neighbors

## Nearest neighbor classification

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
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)