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tests: linear least_squares fit error estimation tested against jackknife resampling.
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@ -381,6 +381,40 @@ def test_error_band():
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pe.fits.error_band(x, f, fitp.fit_parameters)
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def test_fit_vs_jackknife():
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od = 0.9999999999
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cov1 = np.array([[1, od, od], [od, 1.0, od], [od, od, 1.0]])
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cov1 *= 0.05
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nod = -0.4
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cov2 = np.array([[1, nod, nod], [nod, 1.0, nod], [nod, nod, 1.0]])
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cov2 *= 0.05
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cov3 = np.identity(3)
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cov3 *= 0.05
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samples = 500
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for i, cov in enumerate([cov1, cov2, cov3]):
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dat = pe.misc.gen_correlated_data(np.arange(1, 4), cov, 'test', 0.5, samples=samples)
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[o.gamma_method(S=0) for o in dat];
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func = lambda a, x: a[0] + a[1] * x
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fr = pe.least_squares(np.arange(1, 4), dat, func)
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fr.gamma_method(S=0)
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jd = np.array([o.export_jackknife() for o in dat]).T
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jfr = []
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for jacks in jd:
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def chisqfunc_residuals(p):
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model = func(p, np.arange(1, 4))
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chisq = ((jacks - model) / [o.dvalue for o in dat])
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return chisq
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tf = scipy.optimize.least_squares(chisqfunc_residuals, [0.0, 0.0], method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
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jfr.append(tf.x)
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ajfr = np.array(jfr).T
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err = np.array([np.sqrt(np.var(ajfr[j][1:], ddof=0) * (samples - 1)) for j in range(2)])
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assert np.allclose(err, [o.dvalue for o in fr], atol=1e-8)
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def test_fit_no_autograd():
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dim = 10
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x = np.arange(dim)
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