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)
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)
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)