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[Feat] Number of fit parameters can be explicitly passed to the fit functions (#269)

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s-kuberski 2025-10-09 13:14:17 +02:00 • committed by GitHub
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2 changed files with 116 additions and 36 deletions
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49
tests/fits_test.py
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@ -1098,6 +1098,7 @@ def test_combined_fit_xerr():
}
xd = {k: np.transpose([[1 + .01 * np.random.uniform(), 2] for i in range(len(yd[k]))]) for k in fitd}
pe.fits.least_squares(xd, yd, fitd)
pe.fits.least_squares(xd, yd, fitd, n_parms=4)
def test_x_multidim_fit():
@ -1340,6 +1341,54 @@ def test_combined_fit_constant_shape():
funcs = {"a": lambda a, x: a[0] + a[1] * x,
"": lambda a, x: a[1] + x * 0}
pe.fits.least_squares(x, y, funcs, method='migrad')
pe.fits.least_squares(x, y, funcs, method='migrad', n_parms=2)
def test_fit_n_parms():
# Function that fails if the number of parameters is not specified:
def fcn(p, x):
# Assumes first half of terms are A second half are E
NTerms = int(len(p)/2)
A = anp.array(p[0:NTerms])[:, np.newaxis] # shape (n, 1)
E_P = anp.array(p[NTerms:])[:, np.newaxis] # shape (n, 1)
# This if statement handles the case where x is a single value rather than an array
if isinstance(x, anp.float64) or isinstance(x, anp.int64) or isinstance(x, float) or isinstance(x, int):
x = anp.array([x])[np.newaxis, :] # shape (1, m)
else:
x = anp.array(x)[np.newaxis, :] # shape (1, m)
exp_term = anp.exp(-E_P * x)
weighted_sum = A * exp_term # shape (n, m)
return anp.mean(weighted_sum, axis=0) # shape(m)
c = pe.Corr([pe.pseudo_Obs(2. * np.exp(-.2 * t) + .4 * np.exp(+.4 * t) + .4 * np.exp(-.6 * t), .1, 'corr') for t in range(12)])
c.fit(fcn, n_parms=2)
c.fit(fcn, n_parms=4)
xf = [pe.pseudo_Obs(t, .05, 'corr') for t in range(c.T)]
yf = [c[t] for t in range(c.T)]
pe.fits.total_least_squares(xf, yf, fcn, n_parms=2)
pe.fits.total_least_squares(xf, yf, fcn, n_parms=4)
# Is expected to fail, this is what is fixed with n_parms
with pytest.raises(RuntimeError):
c.fit(fcn, )
with pytest.raises(RuntimeError):
pe.fits.total_least_squares(xf, yf, fcn, )
# Test for positivity
with pytest.raises(ValueError):
c.fit(fcn, n_parms=-2)
with pytest.raises(ValueError):
pe.fits.total_least_squares(xf, yf, fcn, n_parms=-4)
# Have to pass an interger
with pytest.raises(TypeError):
c.fit(fcn, n_parms=2.)
with pytest.raises(TypeError):
pe.fits.total_least_squares(xf, yf, fcn, n_parms=1.2343)
# Improper number of parameters (function should fail)
with pytest.raises(ValueError):
c.fit(fcn, n_parms=7)
with pytest.raises(ValueError):
pe.fits.total_least_squares(xf, yf, fcn, n_parms=5)
def fit_general(x, y, func, silent=False, **kwargs):