def test_perceptron():
X = [[0, 1, 0],
[0, 1, 0],
[1, 0, 0],
[0, 1, 0],
[1, 0, 0],
[0, 0, 1],
[0, 0, 1],
[0, 1, 0],
[1, 0, 0],
[1, 0, 0]]
y = [0, 0, 0, 0, 0, 1, 1, 0, 2, 2]
clf = StructuredPerceptron(verbose=False, random_state=37, max_iter=15)
clf.fit(X, y, [len(y)])
assert_array_equal(y, clf.predict(X))
# Try again with string labels and sparse input.
y_str = np.array(["eggs", "ham", "spam"])[y]
clf = clone(clf)
clf.fit(csc_matrix(X), y_str, [len(y_str)])
assert_array_equal(y_str, clf.predict(coo_matrix(X)))
X2 = np.vstack([X, X])
y2 = np.hstack([y_str, y_str])
assert_array_equal(y2, clf.predict(X2, lengths=[len(y), len(y)]))
# Train with Viterbi, test with best-first to make StructuredPerceptron
# behave a bit more like a linear model.
clf.fit(X, y, [len(y)])
clf.set_params(decode="bestfirst")
y_linearmodel = np.dot(X, clf.coef_.T).argmax(axis=1)
assert_array_equal(clf.predict(X), y_linearmodel)
