def test_size(n: int = 10):
a = np.ones(n)
b = np.ones(n)
c = np.ones(n)
d = np.ones(n)
assert size(a, b, c, d) == n
def test_size_error():
x = np.ones(10)
y = np.ones(10)
z = np.ones(5)
with pytest.raises(ValueError):
n = size(x, y, z)
def grad(self, predicted: Tensor, target: Tensor) -> float:
n = size(predicted, target)
return 2 * (predicted - target) / n
def loss(self, predicted: Tensor, target: Tensor) -> float:
n = size(predicted, target)
return np.sum((predicted - target)**2) / n
def test_size_one(n: int = 10):
x = np.ones(n)
assert size(x) == n
def test_size_no_args():
with pytest.raises(ValueError):
n = size()