Пример #1
0
 def sample_lambda_bart(self, param, data, settings):
     lambda_alpha = param.alpha_bart + 0.5 * data['n_train']
     lambda_beta = param.beta_bart + 0.5 * np.sum(
         (data['y_train_orig'] - self.pred_val_sum_train)**2)
     param.lambda_bart = float(
         np.random.gamma(lambda_alpha, 1.0 / lambda_beta, 1))
     param.log_lambda_bart = math.log(param.lambda_bart)
     self.lambda_logprior = compute_gamma_loglik(param.lambda_bart,
                                                 param.alpha_bart,
                                                 param.beta_bart)
Пример #2
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 def __init__(self, data, param, settings, cache, cache_tmp):
     self.trees = []
     self.pmcmc_objects = []
     self.pred_val_mat_train = np.zeros((data['n_train'], settings.m_bart))
     self.update_pred_val_sum()      # updates pred_val_sum_train
     for i_t in range(settings.m_bart):
         p, pred_tmp, pmcmc = init_tree_mcmc(data, settings, param, cache, cache_tmp)
         sample_param(p, settings, param, False)  #NOTE: deterministic initialization if True
         self.trees.append(p)
         self.pmcmc_objects.append(pmcmc)
         self.update_pred_val(i_t, data, param, settings)
     self.lambda_logprior = compute_gamma_loglik(param.lambda_bart, param.alpha_bart, param.beta_bart)
Пример #3
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 def __init__(self, data, param, settings, cache, cache_tmp):
     self.trees = []
     self.pmcmc_objects = []
     self.pred_val_mat_train = np.zeros((data['n_train'], settings.m_bart))
     self.update_pred_val_sum()      # updates pred_val_sum_train
     for i_t in range(settings.m_bart):
         p, pred_tmp, pmcmc = init_tree_mcmc(data, settings, param, cache, cache_tmp)
         sample_param(p, settings, param, False)  #NOTE: deterministic initialization if True
         self.trees.append(p)
         self.pmcmc_objects.append(pmcmc)
         self.update_pred_val(i_t, data, param, settings)
     self.lambda_logprior = compute_gamma_loglik(param.lambda_bart, param.alpha_bart, param.beta_bart)
Пример #4
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 def __init__(self,data,param,settings,cache,cache_tmp):
     self.trees =[]
     self.pmcmc_objects=[]
     self.predicted_value_mat_train = np.zeros((data['n_train'],settings.m_bart))
     self.update_predicted_value_sum()
     for ele in range(settings.m_bart):
         p,predicted_tmp,pmcmc = init_tree_mcmc(data,settings,param,cache,cache_tmp)
         sample_param(p,settings,param,False)
         self.trees.append(p)
         self.pmcmc_objects.append(pmcmc)
         self.update_predicted_value(ele,data,param,settings)
     self.lambda_logprior = compute_gamma_loglik(param.lambda_bart,param.alpha_bart,param.beta_bart)
Пример #5
0
 def sample_lambda_bart(self, param, data, settings):
     lambda_alpha = param.alpha_bart + 0.5 * data['n_train']
     lambda_beta = param.beta_bart + 0.5 * np.sum((data['y_train_orig'] - self.pred_val_sum_train) ** 2)
     param.lambda_bart = float(np.random.gamma(lambda_alpha, 1.0 / lambda_beta, 1))
     param.log_lambda_bart = math.log(param.lambda_bart)
     self.lambda_logprior = compute_gamma_loglik(param.lambda_bart, param.alpha_bart, param.beta_bart)