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refactor: removed redundant formulations of the chisquare function in

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least_squares.
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Fabian Joswig 2023-03-01 16:26:37 +00:00
parent ee2944d5b0
commit a140b2ab39
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1 changed files with 17 additions and 19 deletions
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36
pyerrors/fits.py
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@ -580,17 +580,6 @@ 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))
@ -602,9 +591,24 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
chol = np.linalg.cholesky(corr) chol = np.linalg.cholesky(corr)
chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True) chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
def general_chisqfunc_corr(p, ivars):
model = anp.concatenate([anp.array(funcd[key](p, anp.asarray(xd[key]))).reshape(-1) for key in key_ls])
return anp.dot(chol_inv, (ivars - model))
def general_chisqfunc(p, ivars):
model = anp.concatenate([anp.array(funcd[key](p, anp.asarray(xd[key]))).reshape(-1) for key in key_ls])
return ((ivars - model) / dy_f)
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals_corr(p):
return general_chisqfunc_corr(p, y_f)
def chisqfunc_corr(p): def chisqfunc_corr(p):
return anp.sum(chisqfunc_residuals_corr(p) ** 2) return anp.sum(chisqfunc_residuals_corr(p) ** 2)
def chisqfunc_residuals(p):
return general_chisqfunc(p, y_f)
def chisqfunc(p): def chisqfunc(p):
return anp.sum(chisqfunc_residuals(p) ** 2) return anp.sum(chisqfunc_residuals(p) ** 2)
@ -700,16 +704,10 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d): def chisqfunc_compact(d):
func_list = np.concatenate([[funcd[k]] * len(xd[k]) for k in key_ls]) return anp.sum(general_chisqfunc_corr(d[:n_parms], d[n_parms:]) ** 2)
model = anp.array([func_list[i](d[:n_parms], x_all[i]) for i in range(len(x_all))])
chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
return chisq
else: else:
def chisqfunc_compact(d): def chisqfunc_compact(d):
func_list = np.concatenate([[funcd[k]] * len(xd[k]) for k in key_ls]) return anp.sum(general_chisqfunc(d[:n_parms], d[n_parms:]) ** 2)
model = anp.array([func_list[i](d[:n_parms], x_all[i]) for i in range(len(x_all))])
chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
return chisq
jac_jac_y = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f))) jac_jac_y = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f)))