robustness_quantifier module¶
Quantification of robustness of the optimum to variables perturbations
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class
gemseo.post.core.robustness_quantifier.
RobustnessQuantifier
(history, approximation_method='SR1')[source]¶ Bases:
object
classdocs
Constructor
- Parameters
history – an approximation history.
approximation_method – an approximation method for the Hessian.
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AVAILABLE_APPROXIMATIONS
= ['BFGS', 'SR1', 'LEAST_SQUARES']¶
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compute_approximation
(funcname, first_iter=0, last_iter=0, b0_mat=None, at_most_niter=- 1, func_index=None)[source]¶ Builds the BFGS approximation for the hessian
- Parameters
at_most_niter – maximum number of iterations to take (Default value = -1).
funcname – param first_iter: (Default value = 0).
last_iter – Default value = 0).
b0_mat – Default value = None).
func_index – Default value = None).
first_iter – (Default value = 0).
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compute_expected_value
(expect, cov)[source]¶ computes 1/2*E(e.T B e) , where e is a vector of expected values expect and covariance matrix cov
- Parameters
expect – the expected value of inputs.
cov – the covariance matrix of inputs.
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compute_function_approximation
(x_vars)[source]¶ Computes a second order approximation of the function
- Parameters
x – the point on which the approximation is evaluated.
x_vars – x vars.
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compute_gradient_approximation
(x_vars)[source]¶ - Computes a first order approximation of the gradient
based on the hessian
- Parameters
x_vars – the point on which the approximation is evaluated.
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compute_variance
(expect, cov)[source]¶ computes 1/2*E(e.T B e), where e is a vector of expected values expect and covariance matrix cov
- Parameters
expect – the expected value of inputs.
cov – the covariance matrix of inputs.
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montecarlo_average_var
(mean, cov, n_samples=100000, func=None)[source]¶ Computes the variance and expected value using Monte Carlo approach
- Parameters
mean – the mean value.
cov – the covariance matrix.
n_samples – the number of samples for the distribution (Default value = 100000).
func – if None, the compute_function_approximation function, otherwise a user function (Default value = None).