def test_approximate_max_intensity():
m1 = Matrix([
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
m2 = Matrix([
[0.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
])
m1.approximate(m2, 1.0)
expected = Matrix([
[0.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
])
assert matrixes_almost_equal(m1, expected)
def test_approximate_nothing():
m1 = Matrix([
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
m2 = Matrix([
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
m1.approximate(m2, 1.0)
expected = Matrix([
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
assert matrixes_almost_equal(m1, expected)
def test_create_default():
m = Matrix.create(2, 3)
assert m.get(0, 0) == 0.0
assert m.get(1, 2) == 0.0
with pytest.raises(IndexError):
m.get(2, 2)
with pytest.raises(IndexError):
m.get(1, 3)
with pytest.raises(ValueError):
Matrix.create(0, 3)
with pytest.raises(ValueError):
Matrix.create(2, 0)
def test_average_similarity():
m1 = Matrix([
[0.1, 0.2, 0.3],
[0.4, 0.5, 0.6],
])
m2 = Matrix([
[0.1, 0.1, 0.1],
[0.4, 0.4, 0.4],
])
# [0] sum min = 0.3, sum max = 0.6, limit = 0.3, res = 0.3 * 0.3 / 0.6 = 0.15
# [1] sum min = 1.2, sum max = 1.5, limit = 0.6, res = 0.6 * 1.2 / 1.5 = 0.48
# avg = 0.315
sim = m1.average_similarity(m2)
assert almost_equal(sim, 0.315)
m3 = Matrix([
[0.1, 0.2],
[0.4, 0.5],
])
with pytest.raises(ValueError):
m1.average_similarity(m3)
m4 = Matrix([
[0.1, 0.1, 0.1],
[0.4, 0.4, 0.4],
[0.4, 0.4, 0.4],
])
with pytest.raises(ValueError):
m1.average_similarity(m4)
def test_new_matrix():
data = [
[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
]
m = Matrix(data)
assert m.get_data() == data
with pytest.raises(ValueError):
Matrix([
[1.0, 2.0, 3.0],
[4.0, 5.0],
])
with pytest.raises(ValueError):
Matrix([])
with pytest.raises(ValueError):
Matrix([
[],
[]
])
def test_set():
m = Matrix.create(2, 3)
m.set(0, 0, 0.5)
assert almost_equal(m.get(0, 0), 0.5)
m.set(1, 2, 0.6)
assert almost_equal(m.get(1, 2), 0.6)
with pytest.raises(IndexError):
m.set(2, 2, 1.0)
with pytest.raises(IndexError):
m.set(1, 3, 1.0)
def test_approximate_some_intensity():
m1 = Matrix([
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
m2 = Matrix([
[0.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
])
m1.approximate(m2, 0.1)
expected = Matrix([
[0.9, 0.0, 0.1],
[0.9, 0.1, 0.0],
])
assert matrixes_almost_equal(m1, expected)
m1.approximate(m2, 0.1)
expected = Matrix([
[0.819, 0.0, 0.19],
[0.819, 0.19, 0.0],
])
assert matrixes_almost_equal(m1, expected)
def test_get_data():
m = Matrix.create(2, 3, 1.0)
expected_data = [
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
assert m.get_data() == expected_data
m.set(1, 1, 2.0)
m.set(1, 2, 3.0)
expected_data = [
[1.0, 1.0, 1.0],
[1.0, 2.0, 3.0],
]
assert m.get_data() == expected_data
def test_rows():
m = Matrix.create(2, 3, 1.0)
expected_data = [
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
assert list(m.rows()) == expected_data
m.set(1, 1, 2.0)
m.set(1, 2, 3.0)
expected_data = [
[1.0, 1.0, 1.0],
[1.0, 2.0, 3.0],
]
assert list(m.rows()) == expected_data
def test_create_with_initial_value():
m = Matrix.create(2, 3, initial_value=0.5)
assert m.get(0, 0) == 0.5
assert m.get(1, 2) == 0.5