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

	Lab 4: support vector machines
	------------------------------

	created by Adrian Shajkofci and François marelli on 12.03.2020
	"""

	import unittest
	import numpy as np
	import homework

	from sklearn.datasets import load_iris
	from sklearn.svm import LinearSVC
	from sklearn.linear_model import LogisticRegression

	import importlib

	importlib.reload(homework)


	class TestFunctions(unittest.TestCase):
	def test_accuracy(self):
	np.random.seed(0)
	a = np.ones(10)
	b = np.zeros(10)
	c = np.concatenate([a, b], axis=0)
	d = np.concatenate([a, a], axis=0)
	f = homework.compute_accuracy
	np.testing.assert_array_equal(
	[
	f(a.copy(), a.copy()),
	f(a.copy(), b.copy()),
	f(c.copy(), d.copy()),
	f(d.copy(), c.copy()),
	],
	[1, 0, 0.5, 0.5],
	)

	def test_decision(self):
	data_X, data_y = load_iris(return_X_y=True)
	mask = (data_y == 0) \| (data_y == 1)
	data_X = data_X[mask]
	data_y = data_y[mask]
	data_X = data_X[:, 0:2]

	model = LinearSVC().fit(data_X, data_y)
	predict = model.predict(data_X)

	b = model.intercept_
	w = model.coef_.squeeze()

	np.testing.assert_array_equal(homework.smv_decision(w, b, data_X), predict)


	class TestTransform(unittest.TestCase):
	def setUp(self):
	data = np.load("data.npz")
	self.X = data["X"]
	self.Y = data["y"]

	def test_transform(self):
	X = homework.transform_space(self.X.copy())
	self.assertEqual(X.ndim, 2, "Transformed space should be 2D")
	score = LogisticRegression().fit(X, self.Y).score(X, self.Y)
	self.assertEqual(score, 1.0)


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