def test_on_perfect_fit(self):
test_argument = np.array([[1.0, 3.0], [2.0, 5.0], [3.0, 7.0]])
expected = 1.0
actual = model_test(test_argument, 2.0, 1.0)
message = "model_test({0}) should return {1}, but it actually returned {2}".format(
test_argument, expected, actual)
assert actual == pytest.approx(expected), message
def test_on_perfect_fit():
# Assign to a NumPy array containing a linear testing set
test_argument = np.array([[1.0, 3.0], [2.0, 5.0], [3.0, 7.0]])
# Fill in with the expected value of r^2 in the case of perfect fit
expected = 1.0
# Fill in with the slope and intercept of the model
actual = model_test(test_argument, slope=2.0, intercept=1.0)
# Complete the assert statement
assert actual == pytest.approx(expected), "Expected: {0}, Actual: {1}".format(expected, actual)
def test_on_circular_data(self):
theta = pi / 4.0
# Assign to a NumPy array holding the circular testing data
test_argument = np.array([[1.0, 0.0], [cos(theta), sin(theta)], [0.0, 1.0],
[cos(3 * theta), sin(3 * theta)], [-1.0, 0.0],
[cos(5 * theta), sin(5 * theta)], [0.0, -1.0],
[cos(7 * theta), sin(7 * theta)]])
# Fill in with the slope and intercept of the straight line
actual = model_test(test_argument, slope=0.0, intercept=0.0)
# Complete the assert statement
assert actual == pytest.approx(0.0)
def test_on_circular_data(self):
theta = pi / 4.0
test_argument = np.array([[0.0, 1.0], [cos(theta),
sin(theta)], [1.0, 0.0],
[cos(3 * theta),
sin(3 * theta)], [0.0, -1.0],
[cos(5 * theta),
sin(5 * theta)], [-1.0, 0.0],
[cos(7 * theta),
sin(7 * theta)]])
actual = model_test(test_argument, 0.0, 0.0)
assert actual == pytest.approx(0.0)
def test_on_one_dimensional_array(self):
test_argument = np.array([1.0, 2.0, 3.0, 4.0])
with pytest.raises(ValueError) as exc_info:
model_test(test_argument, 1.0, 1.0)
expected_error_msg = "Argument testing_set must be two dimensional. Got 1 dimensional array instead!"
assert exc_info.match(expected_error_msg)
