def __repr__(self): d = { "01. Sampling module": self.sampling_module, "02. Sampler": self.sampler, "03. Number of samples": self.n_samples, "04. Number of output parameters": self.n_outputs, "05. Samples' shape": self.shape, } return formal_repr(self, d) + "\n06. Distribution parameters: " + dict_str(self.params) + \ "\n07 Resulting statistics: " + dict_str(self.stats)
def __repr__(self): d = { "01. Sampling module": self.sampling_module, "02. Sampler": self.sampler, "03. Number of samples": self.n_samples, "04. Number of output parameters": self.n_outputs, "05. Samples' shape": self.shape, "06. Random seed": self.random_seed, } return formal_repr(self, d) + \ "\n07. Distribution parameters: " + dict_str(self.params) + \ "\n08. Truncation limits: " + str([dict_str(d) for d in dicts_of_lists_to_lists_of_dicts(self.trunc_limits)]) + \ "\n08. Resulting statistics: " + dict_str(self.stats)
def __repr__(self): d = { "01. Method": self.method, "02. Second order calculation flag": self.calc_second_order, "03. Confidence level": self.conf_level, "05. Number of inputs": self.n_inputs, "06. Number of outputs": self.n_outputs, "07. Input names": self.input_names, "08. Output names": self.output_names, "09. Input bounds": self.input_bounds, "10. Problem": dict_str(self.problem), "11. Other parameters": dict_str(self.other_parameters), } return formal_repr(self, d)
mu = 0.25 std = 0.5 elif distribution == "beta": mu = 0.5 std = 0.25 elif distribution == "binomial": mu = 1.0 std = 1.0 / np.sqrt(2) elif distribution == "chisquare": mu = 1.0 std = np.sqrt(2 * mu) else: mu = 0.5 std = 0.5 logger.info(dict_str({"mu": mu, "std": std})) p = mean_std_to_distribution_params(distribution, mu=mu, std=std) logger.info(str(p)) logger.info("\nDistribution " + distribution + " to mu, std:") mu1, std1 = distribution_params_to_mean_std(distribution, **p) logger.info(dict_str({"mu": mu, "std": std})) if np.abs(mu - mu1) > 10**-6 or np.abs(std - std1) > 10**-6: raise ValueError("mu - mu1 = " + str(mu - mu1) + "std - std1 = " + str(std - std1))
if distribution == "poisson": mu = 0.25 std = 0.5 elif distribution == "beta": mu = 0.5 std = 0.25 elif distribution == "binomial": mu = 1.0 std = 1.0 / np.sqrt(2) elif distribution == "chisquare": mu = 1.0 std = np.sqrt(2 * mu) else: mu = 0.5 std = 0.5 LOG.info(dict_str({"mu": mu, "std": std})) p = mean_std_to_distribution_params(distribution, mu=mu, std=std) LOG.info(str(p)) LOG.info("\nDistribution " + distribution + " to mu, std:") mu1, std1 = distribution_params_to_mean_std(distribution, **p) LOG.info(dict_str({"mu": mu, "std": std})) if np.abs(mu - mu1) > 10**-6 or np.abs(std - std1) > 10**-6: raise ValueError("mu - mu1 = " + str(mu - mu1) + "std - std1 = " + str(std - std1))