testing.py
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	"""
	EE-311
	======

	Lab 2: NumPy, Matplotlib and Scikit-learn (linear regression example)
	---------------------------------------------------------------------

	created by François Marelli on 03.03.2021
	"""

	import unittest
	import numpy as np
	from sklearn import datasets
	import homework

	import importlib

	importlib.reload(homework)


	class Test(unittest.TestCase):
	def test_train(self):
	expected = np.load("test_data.npy")

	wine_full, _ = datasets.load_wine(return_X_y=True)
	wine_X = wine_full[:, np.newaxis, 9]
	wine_y = wine_full[:, 0]

	data_X = wine_X[:20]
	data_y = wine_y[:20]

	predict_X = np.arange(0, 101, 10)[..., None]

	model = homework.train(data_X, data_y)
	prediction = model.predict(predict_X)

	np.testing.assert_array_almost_equal(prediction, expected)

	def test_mean_odd(self):
	array1 = np.arange(10)
	array2 = np.ones(10)
	array3 = np.arange(10) - 1
	array4 = np.ones((3, 3, 3))

	self.assertEqual(homework.mean_odd(array1), 5)
	self.assertEqual(homework.mean_odd(array2), 1)
	self.assertEqual(homework.mean_odd(array3), 3)
	self.assertEqual(homework.mean_odd(array4), 1)


	if __name__ == "__main__":
	unittest.main()