Fixed bugs for combined fits with multiple independent variables (#211)

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s-kuberski 2023-10-24 19:30:52 +02:00 committed by GitHub
parent 0ef8649031
commit dc63142f8e
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2 changed files with 49 additions and 2 deletions

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@ -365,7 +365,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
raise Exception('The minimization procedure did not converge.') raise Exception('The minimization procedure did not converge.')
output.chisquare = chisquare output.chisquare = chisquare
output.dof = x_all.shape[-1] - n_parms + len(loc_priors) output.dof = y_all.shape[-1] - n_parms + len(loc_priors)
output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof) output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
if output.dof > 0: if output.dof > 0:
output.chisquare_by_dof = output.chisquare / output.dof output.chisquare_by_dof = output.chisquare / output.dof
@ -393,7 +393,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
hat_vector = prepare_hat_matrix() hat_vector = prepare_hat_matrix()
A = W @ hat_vector A = W @ hat_vector
P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
expected_chisquare = np.trace((np.identity(x_all.shape[-1]) - P_phi) @ W @ cov @ W) expected_chisquare = np.trace((np.identity(y_all.shape[-1]) - P_phi) @ W @ cov @ W)
output.chisquare_by_expected_chisquare = output.chisquare / expected_chisquare output.chisquare_by_expected_chisquare = output.chisquare / expected_chisquare
if not silent: if not silent:
print('chisquare/expected_chisquare:', output.chisquare_by_expected_chisquare) print('chisquare/expected_chisquare:', output.chisquare_by_expected_chisquare)

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@ -1142,6 +1142,53 @@ def test_fit_dof():
assert cd[0] != cd[0] # Check for nan assert cd[0] != cd[0] # Check for nan
assert np.all(np.array(cd[1:]) > 0) assert np.all(np.array(cd[1:]) > 0)
N = 5
def fitf(a, x):
return a[0] + 0 * x
def fitf_multi(a, x):
return a[0] + 0 * x[0] + 0*x[1]
for priors in [None, [pe.cov_Obs(3, 1, 'p')]]:
if priors is None:
lp = 0
else:
lp = len(priors)
x = [1. for i in range(N)]
y = [pe.cov_Obs(i, .1, '%d' % (i)) for i in range(N)]
[o.gm() for o in y]
res = pe.fits.least_squares(x, y, fitf, expected_chisquare=True, priors=priors)
assert(res.dof == N - 1 + lp)
if priors is None:
assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
kl = ['a', 'b']
x = {k: [1. for i in range(N)] for k in kl}
y = {k: [pe.cov_Obs(i, .1, '%d%s' % (i, k)) for i in range(N)] for k in kl}
[[o.gm() for o in y[k]] for k in y]
res = pe.fits.least_squares(x, y, {k: fitf for k in kl}, expected_chisquare=True, priors=priors)
assert(res.dof == 2 * N - 1 + lp)
if priors is None:
assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
x = np.array([[1., 2.] for i in range(N)]).T
y = [pe.cov_Obs(i, .1, '%d' % (i)) for i in range(N)]
[o.gm() for o in y]
res = pe.fits.least_squares(x, y, fitf_multi, expected_chisquare=True, priors=priors)
assert(res.dof == N - 1 + lp)
if priors is None:
assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
x = {k: np.array([[1., 2.] for i in range(N)]).T for k in kl}
y = {k: [pe.cov_Obs(i, .1, '%d%s' % (i, k)) for i in range(N)] for k in kl}
[[o.gm() for o in y[k]] for k in y]
res = pe.fits.least_squares(x, y, {k: fitf_multi for k in kl}, expected_chisquare=True, priors=priors)
assert(res.dof == 2 * N - 1 + lp)
if priors is None:
assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
def test_combined_fit_constant_shape(): def test_combined_fit_constant_shape():
N1 = 16 N1 = 16