import matplotlib.pyplot as plt
from sklearn import datasets, svm, metrics
from sklearn.linear_model import LogisticRegression

digits = datasets.load_digits()
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))

# Create a classifier
classifier = LogisticRegression()

# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])

# Now predict the value of the digit on the second half:
expected = digits.target[n_samples // 2:]
predicted = classifier.predict(data[n_samples // 2:])

print("Accuracy: %s\n" % (metrics.accuracy_score(expected, predicted)))