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我正在使用 R 中的 bnlearn 包来预测某些结果。但是，对于我数据集中的所有行，我得到相同的预测。

训练

buildModel <- function()
{
#building bn model

#for Hill Climbing, works fine
#hcbn = hc(bndf, blacklist = blacklist, score='bic',restart = 0)

#for Max Min Hill Climbing, also works fine, get different predictions for rows
#hcbn = mmhc(bndf, blacklist = blacklist, optimized=TRUE)

#for Grow Shrink, get the same predictions every row
hcbn = cextend(gs(bndf, blacklist = blacklist, optimized=TRUE))

hcbn.fitted = bn.fit(hcbn, bndf, method='bayes')
hcbn.grain <<- as.grain(hcbn.fitted)
}

预言

hcpredthirtyday1[[i]] <- foreach (j = start:min(end, nrow(predictdf)), .combine=rbind) %dopar%
    {
                predict(hcbn.grain, response = c("myresponse"), newdata = predictdf[j, ], predictors = myypredictors, type = "distribution")$pred$myresponse;
    }

HC 和 MMHC 的输出（不同输入的不同预测）

              0          1
 [1,] 0.8617731 0.13822686
 [2,] 0.8617731 0.13822686
 [3,] 0.8617731 0.13822686
 [4,] 0.8617731 0.13822686
 [5,] 0.8617731 0.13822686
 [6,] 0.8617731 0.13822686
 [7,] 0.8617731 0.13822686
 [8,] 0.8617731 0.13822686
 [9,] 0.8617731 0.13822686
[10,] 0.9077158 0.09228421

GS 的输出（每行的预测相同）

              0         1
 [1,] 0.8633219 0.1366781
 [2,] 0.8633219 0.1366781
 [3,] 0.8633219 0.1366781
 [4,] 0.8633219 0.1366781
 [5,] 0.8633219 0.1366781
 [6,] 0.8633219 0.1366781
 [7,] 0.8633219 0.1366781
 [8,] 0.8633219 0.1366781
 [9,] 0.8633219 0.1366781
[10,] 0.8633219 0.1366781
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