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"""
================================
Digits Classification Exercise
================================
A tutorial exercise regarding the use of classification techniques on
the Digits dataset.
This exercise is used in the :ref:`clf_tut` part of the
:ref:`supervised_learning_tut` section of the
:ref:`stat_learn_tut_index`.
"""
from sklearn import datasets, linear_model, neighbors
X_digits, y_digits = datasets.load_digits(return_X_y=True)
X_digits = X_digits / X_digits.max()
n_samples = len(X_digits)
X_train = X_digits[: int(0.9 * n_samples)]
y_train = y_digits[: int(0.9 * n_samples)]
X_test = X_digits[int(0.9 * n_samples) :]
y_test = y_digits[int(0.9 * n_samples) :]
knn = neighbors.KNeighborsClassifier()
logistic = linear_model.LogisticRegression(max_iter=1000)
print("KNN score: %f" % knn.fit(X_train, y_train).score(X_test, y_test))
print(
"LogisticRegression score: %f"
% logistic.fit(X_train, y_train).score(X_test, y_test)
)
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