def test_MnMigrad_np():
fcn = FCN(
lambda xy: 10 + xy[0]**2 + ((xy[1] - 1) / 2)**2,
lambda xy: [2 * xy[0], (xy[1] - 1)],
True,
1,
)
state = MnUserParameterState()
state.add("x", 5, 0.1)
state.add("😁", 3, 0.2, -5, 5)
assert len(state) == 2
str = MnStrategy(2)
migrad = MnMigrad(fcn, state, str)
fmin = migrad(0, 0.1)
state = fmin.state
assert len(state) == 2
assert state[0].number == 0
assert state[0].name == "x"
assert state[0].value == approx(0, abs=1e-2)
assert state[0].error == approx(1, abs=1e-2)
assert state[1].number == 1
assert state[1].name == "😁"
assert state[1].value == approx(1, abs=1e-2)
assert state[1].error == approx(2, abs=6e-2)
assert fcn._nfcn > 0
assert fcn._ngrad > 0
def test_MnMigrad_cfunc(npar):
nb = pytest.importorskip("numba")
c_sig = nb.types.double(nb.types.uintc, nb.types.CPointer(nb.types.double))
@nb.cfunc(c_sig)
def fcn(n, x):
x = nb.carray(x, (n, ))
r = 0.0
for i in range(n):
r += (x[i] - i)**2
return r
fcn = FCN(fcn, None, True, 1)
state = MnUserParameterState()
for i in range(npar):
state.add(f"x{i}", 5, 0.1)
migrad = MnMigrad(fcn, state, 1)
fmin = migrad(0, 0.1)
state = fmin.state
assert len(state) == npar
for i, p in enumerate(state):
assert p.number == i
assert p.value == approx(i, abs=1e-3)
assert p.error == approx(1, abs=1e-3)
def test_MnSimplex():
fcn = FCN(lambda x: 10 + x**2, None, False, 1)
state = MnUserParameterState()
state.add("x", 2, 5)
simplex = MnSimplex(fcn, state, 1)
fmin = simplex(0, 0.1)
assert fmin.is_valid
state = fmin.state
assert len(state) == 1
assert state[0].value == approx(0, abs=5e-2)
def test_FunctionMinimum():
fcn = FCN(lambda x: 10 + x**2, None, False, 1)
st = MnUserParameterState()
st.add("x", 0.01, 5)
str = MnStrategy(1)
fm1 = FunctionMinimum(fcn, st, str, 0.2)
assert fm1.is_valid
assert len(fm1.state) == 1
assert fm1.fval == 10.0001
fm2 = FunctionMinimum(fcn, st, str, 0)
assert not fm2.is_valid
def test_MnMigrad_grad():
fcn = FCN(lambda x: 10 + x**2, lambda x: [2 * x], False, 1)
state = MnUserParameterState()
state.add("x", 5, 0.1)
migrad = MnMigrad(fcn, state, 1)
fmin = migrad(0, 0.1)
state = fmin.state
assert len(state) == 1
assert state[0].number == 0
assert state[0].name == "x"
assert state[0].value == approx(0, abs=1e-3)
assert state[0].error == approx(1, abs=1e-3)
assert fcn._nfcn > 0
assert fcn._ngrad > 0
def test_MnMigrad():
fcn = FCN(fn, None, False, 1)
state = MnUserParameterState()
state.add("x", 5, 0.1)
state.add("y", 3, 0.2, -5, 5)
migrad = MnMigrad(fcn, state, 1)
fmin = migrad(0, 0.1)
assert fmin.is_valid
state = fmin.state
assert state[0].value == approx(0, abs=5e-3)
assert state[0].error == approx(1, abs=5e-3)
assert state[1].value == approx(1, abs=5e-3)
assert state[1].error == approx(2, abs=6e-2)
assert fcn._nfcn > 0
assert fcn._ngrad == 0
def test_FunctionMinimum_pickle():
st = MnUserParameterState()
st.add("x", 1, 0.1)
st.add("y", 2, 0.1, 1, 3)
fm = FunctionMinimum(FCN(fn, None, False, 1), st, 1, 0.1)
pkl = pickle.dumps(fm)
fm2 = pickle.loads(pkl)
assert len(fm.state) == len(fm2.state)
assert fm.state == fm2.state
assert fm.edm == fm2.edm
assert fm.fval == fm2.fval
assert fm.is_valid == fm2.is_valid
assert fm.has_valid_parameters == fm2.has_valid_parameters
assert fm.has_accurate_covar == fm2.has_accurate_covar
assert fm.has_posdef_covar == fm2.has_posdef_covar
assert fm.has_made_posdef_covar == fm2.has_made_posdef_covar
assert fm.hesse_failed == fm2.hesse_failed
assert fm.has_covariance == fm2.has_covariance
assert fm.is_above_max_edm == fm2.is_above_max_edm
assert fm.has_reached_call_limit == fm2.has_reached_call_limit
assert fm.errordef == fm2.errordef