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Fit_result gammma_method, str and repr added
This commit is contained in:
Fabian Joswig
parent
4ceb35992e
commit
d63c749a17
2 changed files with 41 additions and 22 deletions
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@ -18,6 +18,26 @@ class Fit_result:
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def __init__(self):
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self.fit_parameters = None
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def gamma_method(self):
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"""Apply the gamma method to all fit parameters"""
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[o.gamma_method() for o in self.fit_parameters]
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def __str__(self):
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self.gamma_method()
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my_str = ''
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if hasattr(self, 'chisquare_by_dof'):
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my_str += '\u03C7\u00b2/d.o.f. = ' + f'{self.chisquare_by_dof:2.6f}' + '\n'
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elif hasattr(self, 'residual_variance'):
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my_str += 'residual variance = ' + f'{self.residual_variance:2.6f}' + '\n'
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if hasattr(self, 'chisquare_by_expected_chisquare'):
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my_str += '\u03C7\u00b2/\u03C7\u00b2exp = ' + f'{self.chisquare_by_expected_chisquare:2.6f}' + '\n'
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my_str += 'Fit parameters:\n'
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for i_par, par in enumerate(self.fit_parameters):
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my_str += str(i_par) + '\t' + str(par) + '\n'
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return my_str
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def __repr__(self):
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return 'Fit_result' + str([o.value for o in self.fit_parameters]) + '\n'
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def standard_fit(x, y, func, silent=False, **kwargs):
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@ -57,7 +77,6 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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has to be calculated (default False).
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"""
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output = Fit_result()
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output.fit_function = func
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@ -105,7 +124,7 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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return chisq
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if 'method' in kwargs:
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utput.method = kwargs.get('method')
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output.method = kwargs.get('method')
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if not silent:
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print('Method:', kwargs.get('method'))
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if kwargs.get('method') == 'migrad':
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@ -207,8 +226,6 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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Keyword arguments
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-----------------
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dict_output -- If true, the output is a dictionary containing all relevant
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data instead of just a list of the fit parameters.
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silent -- If true all output to the console is omitted (default False).
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initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear
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fits with many parameters.
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@ -263,20 +280,20 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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model = Model(func)
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odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=0, deriv=1)
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output = odr.run()
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out = odr.run()
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output.residual_variance = output.res_var
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output.residual_variance = out.res_var
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output.method = 'ODR'
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output.xplus = output.xplus
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output.xplus = out.xplus
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if not silent:
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print('Method: ODR')
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print(*output.stopreason)
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print(*out.stopreason)
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print('Residual variance:', output.residual_variance)
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if output.info > 3:
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if out.info > 3:
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raise Exception('The minimization procedure did not converge.')
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m = x_f.size
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@ -296,9 +313,9 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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number_of_x_parameters = int(m / x_f.shape[-1])
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old_jac = jacobian(func)(output.beta, output.xplus)
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old_jac = jacobian(func)(out.beta, out.xplus)
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fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
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fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(output.xplus, output.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
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fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
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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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@ -307,19 +324,19 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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if expected_chisquare <= 0.0:
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warnings.warn("Negative expected_chisquare.", RuntimeWarning)
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expected_chisquare = np.abs(expected_chisquare)
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output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel()))) / expected_chisquare
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output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
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if not silent:
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print('chisquare/expected_chisquare:',
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output.chisquare_by_expected_chisquare)
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hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((output.beta, output.xplus.ravel()))))
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hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((out.beta, out.xplus.ravel()))))
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def odr_chisquare_compact_x(d):
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model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
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chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
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return chisq
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jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((output.beta, output.xplus.ravel(), x_f.ravel())))
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jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((out.beta, out.xplus.ravel(), x_f.ravel())))
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deriv_x = -hess_inv @ jac_jac_x[:n_parms + m, n_parms + m:]
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@ -328,17 +345,17 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
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return chisq
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jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((output.beta, output.xplus.ravel(), y_f)))
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jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((out.beta, out.xplus.ravel(), y_f)))
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deriv_y = -hess_inv @ jac_jac_y[:n_parms + m, n_parms + m:]
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(output.beta[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(x.ravel()) + list(y), man_grad=[0] + list(deriv_x[i]) + list(deriv_y[i])))
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(out.beta[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(x.ravel()) + list(y), man_grad=[0] + list(deriv_x[i]) + list(deriv_y[i])))
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output.fit_parameters = result
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output.odr_chisquare = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel())))
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output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
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output.dof = x.shape[-1] - n_parms
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return output
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@ -650,7 +667,7 @@ def ks_test(obs=None):
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print(scipy.stats.kstest(Qs, 'uniform'))
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def fit_general(x, y, func, silent=False, **kwargs):
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def fit_general(x, y, func, silent=silent, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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Plausibility of the results should be checked. To control the numerical differentiation
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@ -27,7 +27,8 @@ def test_standard_fit():
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y = a[0] * np.exp(-a[1] * x)
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return y
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beta = pe.fits.standard_fit(x, oy, func)
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out = pe.fits.standard_fit(x, oy, func)
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beta = out.fit_parameters
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pe.Obs.e_tag_global = 5
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for i in range(2):
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@ -70,7 +71,8 @@ def test_odr_fit():
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odr.set_job(fit_type=0, deriv=1)
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output = odr.run()
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beta = pe.fits.odr_fit(ox, oy, func)
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out = pe.fits.odr_fit(ox, oy, func)
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beta = out.fit_parameters
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pe.Obs.e_tag_global = 5
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for i in range(2):
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@ -95,8 +97,8 @@ def test_odr_derivatives():
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def func(a, x):
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return a[0] + a[1] * x ** 2
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fit1 = pe.fits.odr_fit(x, y, func)
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out = pe.fits.odr_fit(x, y, func)
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fit1 = out.fit_parameters
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tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
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assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
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