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refactor: chisqfunc rewritten as sum over residuals.

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Fabian Joswig 2023-03-01 16:12:31 +00:00
parent dc7033e51f
commit ee2944d5b0
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1 changed files with 13 additions and 18 deletions
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31
pyerrors/fits.py
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@ -580,6 +580,17 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
else: else:
x0 = [0.1] * n_parms x0 = [0.1] * n_parms
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals_corr(p):
model = np.concatenate([np.array(funcd[key](p, np.asarray(xd[key]))).reshape(-1) for key in key_ls])
chisq = anp.dot(chol_inv, (y_f - model))
return chisq
def chisqfunc_residuals(p):
model = np.concatenate([np.array(funcd[key](p, np.asarray(xd[key]))).reshape(-1) for key in key_ls])
chisq = ((y_f - model) / dy_f)
return chisq
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
corr = covariance(y_all, correlation=True, **kwargs) corr = covariance(y_all, correlation=True, **kwargs)
covdiag = np.diag(1 / np.asarray(dy_f)) covdiag = np.diag(1 / np.asarray(dy_f))
@ -592,15 +603,10 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True) chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
def chisqfunc_corr(p): def chisqfunc_corr(p):
model = np.concatenate([np.array(funcd[key](p, np.asarray(xd[key]))).reshape(-1) for key in key_ls]) return anp.sum(chisqfunc_residuals_corr(p) ** 2)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
def chisqfunc(p): def chisqfunc(p):
func_list = np.concatenate([[funcd[k]] * len(xd[k]) for k in key_ls]) return anp.sum(chisqfunc_residuals(p) ** 2)
model = anp.array([func_list[i](p, x_all[i]) for i in range(len(x_all))])
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
output.method = kwargs.get('method', 'Levenberg-Marquardt') output.method = kwargs.get('method', 'Levenberg-Marquardt')
if not silent: if not silent:
@ -627,17 +633,6 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
chisquare = fit_result.fun chisquare = fit_result.fun
else: else:
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals_corr(p):
model = np.concatenate([np.array(funcd[key](p, np.asarray(xd[key]))).reshape(-1) for key in key_ls])
chisq = anp.dot(chol_inv, (y_f - model))
return chisq
def chisqfunc_residuals(p):
model = np.concatenate([np.array(funcd[key](p, np.asarray(xd[key]))).reshape(-1) for key in key_ls])
chisq = ((y_f - model) / dy_f)
return chisq
if 'tol' in kwargs: if 'tol' in kwargs:
print('tol cannot be set for Levenberg-Marquardt') print('tol cannot be set for Levenberg-Marquardt')