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feat: positive semi-definite estimator for the covariance implemented,

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fits.covariance matrix deprecated, covariance can now handle lists of
observables.
This commit is contained in:
Fabian Joswig 2022-03-01 09:45:25 +00:00
parent 8e3e34bbea
commit 82419b7a88
5 changed files with 65 additions and 79 deletions
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32
pyerrors/fits.py
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@ -239,7 +239,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
if kwargs.get('covariance') is not None:
cov = kwargs.get('covariance')
else:
cov = covariance_matrix(np.concatenate((y, x.ravel())))
cov = covariance(np.concatenate((y, x.ravel())))
number_of_x_parameters = int(m / x_f.shape[-1])
@ -455,7 +455,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
x0 = [0.1] * n_parms
if kwargs.get('correlated_fit') is True:
cov = covariance_matrix(y)
cov = covariance(y)
covdiag = np.diag(1. / np.sqrt(np.diag(cov)))
corr = np.copy(cov)
for i in range(len(y)):
@ -527,7 +527,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if kwargs.get('expected_chisquare') is True:
if kwargs.get('correlated_fit') is not True:
W = np.diag(1 / np.asarray(dy_f))
cov = covariance_matrix(y)
cov = covariance(y)
A = W @ jacobian(func)(fit_result.x, x)
P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
@ -651,33 +651,9 @@ def residual_plot(x, y, func, fit_res):
plt.draw()
def covariance_matrix(y):
"""Returns the covariance matrix of y.
Parameters
----------
y : list or numpy.ndarray
List or one dimensional array of Obs
"""
length = len(y)
cov = np.zeros((length, length))
for i, item in enumerate(y):
for j, jtem in enumerate(y[:i + 1]):
if i == j:
cov[i, j] = item.dvalue ** 2
else:
cov[i, j] = covariance(item, jtem)
cov = cov + cov.T - np.diag(np.diag(cov))
eigenvalues = np.linalg.eigh(cov)[0]
if not np.all(eigenvalues >= 0):
warnings.warn("Covariance matrix is not positive semi-definite", RuntimeWarning)
print("Eigenvalues of the covariance matrix:", eigenvalues)
return cov
def error_band(x, func, beta):
"""Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
cov = covariance_matrix(beta)
cov = covariance(beta)
if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)