def test_p_normalize_zero_row():
def almost(a, b, prec=0.00001):
return np.abs(a - b) < prec
# no axis given
X = np.array([
[0, 0, 0],
[0, 0, 0]
])
Y = fl.p_normalize(X)
assert Y.shape == (2, 3)
assert almost(np.sum(Y), 1.0)
X = np.array([
[0.0, 0.0, 0.0],
[0, 0, 0]
])
Y = fl.p_normalize(X, 1)
assert Y.shape == (2, 3)
assert almost(np.sum(Y), 2.0)
X = np.array([
[0.0, 0.0, 0.0],
[0, 0, 0],
])
Y = fl.p_normalize(X, 0)
assert Y.shape == (2, 3)
assert almost(np.sum(Y), 3.0)
def test_p_normalize_zero_row():
# no axis given
X = np.array([
[0, 0, 0],
[0, 0, 0]
])
Y = fl.p_normalize(X)
assert Y.shape == (2, 3)
assert np.sum(Y) == 0.0
X = np.array([
[0.0, 0.0, 0.0],
[0, 0, 0]
])
Y = fl.p_normalize(X, 1)
assert Y.shape == (2, 3)
assert np.sum(Y) == 0.0
X = np.array([
[0.0, 0.0, 0.0],
[0, 0, 0],
])
Y = fl.p_normalize(X, 0)
assert Y.shape == (2, 3)
assert np.sum(Y) == 0.0
def test_p_normalize_zero_row():
# no axis given
X = np.array([[0, 0, 0], [0, 0, 0]])
Y = fl.p_normalize(X)
assert Y.shape == (2, 3)
assert np.sum(Y) == 0.0
X = np.array([[0.0, 0.0, 0.0], [0, 0, 0]])
Y = fl.p_normalize(X, 1)
assert Y.shape == (2, 3)
assert np.sum(Y) == 0.0
X = np.array([
[0.0, 0.0, 0.0],
[0, 0, 0],
])
Y = fl.p_normalize(X, 0)
assert Y.shape == (2, 3)
assert np.sum(Y) == 0.0
def test_p_normalize_wrong_dimensions():
with pytest.raises(AssertionError):
X = np.array([[1, 2, 3]])
Y = fl.p_normalize(X, 2)
with pytest.raises(AssertionError):
X = np.array([[1, 2, 3]])
Y = fl.p_normalize(X, -1)
def test_p_normalize_wrong_dimensions():
with pytest.raises(ValueError):
X = np.array([[1, 2, 3]])
Y = fl.p_normalize(X, 2)
with pytest.raises(ValueError):
X = np.array([[1, 2, 3]])
Y = fl.p_normalize(X, -1)
def test_p_normalize():
def almost(a, b):
return np.abs(a - b) < 0.00001
X = np.array([1, 2, 3, 4])
Y = fl.p_normalize(X)
assert almost(1. / 10, Y[0])
assert almost(2. / 10, Y[1])
assert almost(3. / 10, Y[2])
assert almost(4. / 10, Y[3])
X = np.array([
[1, 2, 3, 4],
[4, 4, 2, 0],
])
Y = fl.p_normalize(X, 1)
assert almost(1. / 10, Y[0][0])
assert almost(2. / 10, Y[0][1])
assert almost(3. / 10, Y[0][2])
assert almost(4. / 10, Y[0][3])
assert almost(4. / 10, Y[1][0])
assert almost(4. / 10, Y[1][1])
assert almost(2. / 10, Y[1][2])
assert almost(0. / 10, Y[1][3])
X = np.array([
[3, 9],
[3, 1],
])
Y = fl.p_normalize(X, 0)
assert 0.5 == Y[0][0]
assert 0.5 == Y[1][0]
assert 0.9 == Y[0][1]
assert 0.1 == Y[1][1]
def test_p_normalize():
def almost(a, b):
return np.abs(a - b) < 0.00001
X = np.array([1, 2, 3, 4])
Y = fl.p_normalize(X)
assert almost(1. / 10, Y[0])
assert almost(2. / 10, Y[1])
assert almost(3. / 10, Y[2])
assert almost(4. / 10, Y[3])
X = np.array([
[1, 2, 3, 4],
[4, 4, 2, 0],
])
Y = fl.p_normalize(X, 1)
assert almost(1. / 10, Y[0][0])
assert almost(2. / 10, Y[0][1])
assert almost(3. / 10, Y[0][2])
assert almost(4. / 10, Y[0][3])
assert almost(4. / 10, Y[1][0])
assert almost(4. / 10, Y[1][1])
assert almost(2. / 10, Y[1][2])
assert almost(0. / 10, Y[1][3])
X = np.array([
[3, 9],
[3, 1],
])
Y = fl.p_normalize(X, 0)
assert 0.5 == Y[0][0]
assert 0.5 == Y[1][0]
assert 0.9 == Y[0][1]
assert 0.1 == Y[1][1]