def _setup_residuals(self):
self._residuals = ci.sqrt(self._weightings_vectorized) * \
ci.veccat([ \
self._discretization.optimization_variables["V"],
self._discretization.optimization_variables["EPS_U"],
])
def _setup_residuals(self):
self._residuals = ci.sqrt(self._weightings_vectorized) * \
ci.veccat([ \
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["V"],
])
def standard_deviations(self):
try:
return ci.sqrt([abs(var) for var \
in ci.diag(self.covariance_matrix)])
except AttributeError:
raise AttributeError('''
Standard deviations for the estimated parameters not yet computed.
Run compute_covariance_matrix() to do so.
''')
def _print_experimental_properties(self, covariance_matrix):
np.set_printoptions(linewidth = 200, \
formatter={'float': lambda x: format(x, ' 10.8e')})
print("\nParameters p_i:")
for k, pk in enumerate(self._pdata):
print(" p_{0:<3} = {1} +/- {2}".format( \
k, pk, ci.sqrt(abs(ci.diag(covariance_matrix)[k]))))
print("\nCovariance matrix for this setup:")
print(np.atleast_2d(covariance_matrix))
def _print_experimental_properties(self, covariance_matrix):
np.set_printoptions(linewidth = 200, \
formatter={'float': lambda x: format(x, ' 10.8e')})
print("\nParameters p_i:")
for k, pk in enumerate(self._pdata):
print(" p_{0:<3} = {1} +/- {2}".format( \
k, pk, ci.sqrt(abs(ci.diag(covariance_matrix)[k]))))
print("\nCovariance matrix for this setup:")
print(np.atleast_2d(covariance_matrix))
def standard_deviations(self):
try:
variances = []
for k in range(ci.diag(self.covariance_matrix).numel()):
variances.append(abs(ci.diag(self.covariance_matrix)[k]))
standard_deviations = ci.sqrt(variances)
return standard_deviations
# return ci.sqrt([abs(var) for var \
# in ci.diag(self.covariance_matrix)])
except AttributeError:
raise AttributeError('''
Standard deviations for the estimated parameters not yet computed.
Run compute_covariance_matrix() to do so.
''')
