## Jupyter Snippet CB2nd 03_digits

Jupyter Snippet CB2nd 03_digits

# 8.3. Learning to recognize handwritten digits with a K-nearest neighbors classifier

import numpy as np
import sklearn
import sklearn.datasets as ds
import sklearn.model_selection as ms
import sklearn.neighbors as nb
import matplotlib.pyplot as plt
%matplotlib inline
X = digits.data
y = digits.target
print((X.min(), X.max()))
print(X.shape)
(0.0, 16.0)
(1797, 64)
nrows, ncols = 2, 5
fig, axes = plt.subplots(nrows, ncols,
figsize=(6, 3))
for i in range(nrows):
for j in range(ncols):
# Image index
k = j + i * ncols
ax = axes[i, j]
ax.matshow(digits.images[k, ...],
cmap=plt.cm.gray)
ax.set_axis_off()
ax.set_title(digits.target[k])

(X_train, X_test, y_train, y_test) = \
ms.train_test_split(X, y, test_size=.25)
knc = nb.KNeighborsClassifier()
knc.fit(X_train, y_train)
knc.score(X_test, y_test)
0.987
# Let's draw a 1.
one = np.zeros((8, 8))
one[1:-1, 4] = 16  # The image values are in [0, 16].
one[2, 3] = 16
fig, ax = plt.subplots(1, 1, figsize=(2, 2))
ax.imshow(one, interpolation='none',
cmap=plt.cm.gray)
ax.grid(False)
ax.set_axis_off()
ax.set_title("One")

# We need to pass a (1, D) array.
knc.predict(one.reshape((1, -1)))
array([1])