def test_LeastSquares(loss, verbose):
rng = np.random.default_rng(1)
x = np.linspace(0, 1, 1000)
ye = 0.1
y = rng.normal(2 * x + 1, ye)
def model(x, a, b):
return a + b * x
cost = LeastSquares(x, y, ye, model, loss=loss, verbose=verbose)
assert cost.ndata == len(x)
m = Minuit(cost, a=0, b=0)
m.migrad()
assert_allclose(m.values, (1, 2), rtol=0.05)
assert cost.loss == loss
if loss != "linear":
cost.loss = "linear"
assert cost.loss != loss
m.migrad()
assert_allclose(m.values, (1, 2), rtol=0.05)
assert m.ndof == len(x) - 2
assert_allclose(m.fmin.reduced_chi2, 1, atol=5e-2)
def test_LeastSquares(loss, verbose):
np.random.seed(1)
x = np.random.rand(20)
y = 2 * x + 1
ye = 0.1
y += ye * np.random.randn(len(y))
def model(x, a, b):
return a + b * x
cost = LeastSquares(x, y, ye, model, loss=loss, verbose=verbose)
m = Minuit(cost, a=0, b=0)
m.migrad()
assert_allclose(m.values, (1, 2), rtol=0.03)
assert cost.loss == loss
if loss != "linear":
cost.loss = "linear"
assert cost.loss != loss
m.migrad()
assert_allclose(m.values, (1, 2), rtol=0.02)
