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Merge pull request #85 from fjosw/fix/pinv_for_chi_exp
Replaced inverse by the pseudo inverse in the calculation of chi_exp
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commit
e5d7271a2b
2 changed files with 19 additions and 2 deletions
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@ -252,7 +252,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
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A = W @ new_jac
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P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
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P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
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expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
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if expected_chisquare <= 0.0:
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warnings.warn("Negative expected_chisquare.", RuntimeWarning)
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@ -531,7 +531,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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W = np.diag(1 / np.asarray(dy_f))
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cov = covariance(y)
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A = W @ jacobian(func)(fit_result.x, x)
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P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
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P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
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expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
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output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
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if not silent:
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@ -319,6 +319,23 @@ def test_prior_fit():
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fitp = pe.fits.least_squares([0, 1], y, f, priors=y, resplot=True, qqplot=True)
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def test_correlated_fit_covobs():
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x1 = pe.cov_Obs(1.01, 0.01 ** 2, 'test1')
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x2 = pe.cov_Obs(2.01, 0.01 ** 2, 'test2')
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x3 = pe.cov_Obs(2.99, 0.01 ** 2, 'test3')
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[o.gamma_method() for o in [x1, x2, x3]]
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def func(a, x):
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return a[0] * x + a[1]
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fit_res = pe.fits.least_squares(np.arange(1, 4), [x1, x2, x3], func, expected_chisquare=True)
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assert np.isclose(fit_res.chisquare_by_dof, fit_res.chisquare_by_expected_chisquare)
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fit_res_corr = pe.fits.least_squares(np.arange(1, 4), [x1, x2, x3], func, correlated_fit=True)
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assert np.isclose(fit_res.chisquare_by_dof, fit_res_corr.chisquare_by_dof)
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def test_error_band():
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def f(a, x):
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return a[0] + a[1] * x
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