我正在學習使用，bnlearn并且在下面代碼的最后一行中不斷遇到以下錯誤：
custom.fit(dag, cpt) 中的錯誤：條件概率分布的數量錯誤
我究竟做錯了什么？
modelstring(dag)= "[s][r][nblw|r][nblg|nblw][mlw|s:r][f|s:r:mlw][mlg|mlw:f]
[mlgr|mlg:nblg]"
###View DAG Specifics
dag
arcs(dag)
nodes(dag)
# Create Levels
State <- c("State0", "State1")
##Create probability distributions given; these are all 2d b/c they have 1 or 2 nodes
cptS <- matrix(c(0.6, 0.4), ncol=2, dimnames=list(NULL, State))
cptR <- matrix(c(0.7, 0.3), ncol=2, dimnames=list(NULL, State))
cptNBLW <- matrix(c(0.95, 0.05, 0.05, 0.95), ncol=2, dimnames=list(NULL, "r"= State))
cptNBLG <- matrix(c(0.9, 0.099999999999999998, 0.2, 0.8), ncol=2, dimnames=list(NULL,
"nblw"=State))
cptMLG <- matrix(c(0.95, 0.05, 0.4, 0.6, 0.2, 0.8, 0.05, 0.95),ncol=2,nrow = 2,
dimnames=list("mlw"= State, "f"=State))
cptMLGR <- matrix(c(0.6,0.4,0.95,0.05,0.2,0.8,0.55,0.45),ncol=2,nrow = 2,
dimnames=list("mlg"= State, "nblg"=State))
cptMLW <-matrix(c(0.95, 0.05, 0.1, 0.9, 0.2, 0.8, 0.01, 0.99), ncol=2,nrow = 2,byrow = TRUE,
dimnames=list("r"= State, "s"=State))
# Build 3-d matrices( becuase you have 3 nodes, you can't use the matrix function; you
have to build it from scratch)
cptF <- c(0.05, 0.95, 0.4, 0.6, 0.9, 0.1, 0.99, 0.01, 0.9, 0.1, 0.95, 0.05, 0.95, 0.05, 0.99,
0.01)
dim(cptF) <- c(2, 2, 2, 2)
dimnames(cptF) <- list("s"=State, "r"=State, "mlw"=State)
###Create CPT Table
cpt <- list(s = cptS, r = cptR, mlw = cptMLW,nblw= cptNBLW,
mlg= cptMLG, nblg= cptNBLG, mlgr= cptMLGR)
# Construct BN network with Conditional Probability Table
S.net <- custom.fit(dag,cpt)
參考：https : //rpubs.com/sarataheri/bnlearnCGM
uj5u.com熱心網友回復：
您的 CPT 定義中有幾個錯誤。首先，您需要確保：
- 提供的概率數等于子節點和父節點中狀態數的乘積，
- 矩陣/陣列的維數等于父節點數加一，對于子節點，
- 當節點維度大于一時，子節點應在第一個維度中給出。
dimnames引數中給出的名稱（例如 中的名稱dimnames=list(ThisName = ...)）應與 DAG 中定義的名稱相匹配，在您的情況下為modelstring，在我的回答中為model2network。（所以我之前使用的建議dimnames=list(cptNBLW = ...)應該是dimnames=list(nblw = ...)匹配節點nblw在模型字串中的宣告方式）
您也沒有將節點添加f到您的 cpt 串列中。
以下是您的代碼，其中包含已更改的注釋。（我已經注釋掉了有問題的行并在之后直接添加了）
library(bnlearn)
dag <- model2network("[s][r][nblw|r][nblg|nblw][mlw|s:r][mlg|mlw:f][mlgr|mlg:nblg][f|s:r:mlw]")
State <- c("State0", "State1")
cptS <- matrix(c(0.6, 0.4), ncol=2, dimnames=list(NULL, State))
cptR <- matrix(c(0.7, 0.3), ncol=2, dimnames=list(NULL, State))
# add child node into first slot of dimnames
cptNBLW <- matrix(c(0.95, 0.05, 0.05, 0.95), ncol=2, dimnames=list(nblw=State, "r"= State))
cptNBLG <- matrix(c(0.9, 0.099999999999999998, 0.2, 0.8), ncol=2, dimnames=list(nblg=State,"nblw"=State))
# Use a 3d array and not matrix, and add child node into dimnames
# cptMLG <- matrix(c(0.95, 0.05, 0.4, 0.6, 0.2, 0.8, 0.05, 0.95),ncol=2,nrow = 2, dimnames=list("mlw"= State, "f"=State))
cptMLG <- array(c(0.95, 0.05, 0.4, 0.6, 0.2, 0.8, 0.05, 0.95),dim=c(2,2,2), dimnames=list(mlg = State, "mlw"= State, "f"=State))
# cptMLGR <- matrix(c(0.6,0.4,0.95,0.05,0.2,0.8,0.55,0.45),ncol=2,nrow = 2, dimnames=list("mlg"= State, "nblg"=State))
cptMLGR <- array(c(0.6,0.4,0.95,0.05,0.2,0.8,0.55,0.45), dim=c(2,2,2), dimnames=list(mlgr=State, "mlg"= State, "nblg"=State))
# cptMLW <-matrix(c(0.95, 0.05, 0.1, 0.9, 0.2, 0.8, 0.01, 0.99), ncol=2,nrow = 2,byrow = TRUE, dimnames=list("r"= State, "s"=State))
cptMLW <-array(c(0.95, 0.05, 0.1, 0.9, 0.2, 0.8, 0.01, 0.99), dim=c(2,2,2), dimnames=list(mlw=State, "r"= State, "s"=State))
# add child into first slot of dimnames
cptF <- c(0.05, 0.95, 0.4, 0.6, 0.9, 0.1, 0.99, 0.01, 0.9, 0.1, 0.95, 0.05, 0.95, 0.05, 0.99, 0.01)
dim(cptF) <- c(2, 2, 2, 2)
dimnames(cptF) <- list("f" = State, "s"=State, "r"=State, "mlw"=State)
# add missing node f into list
cpt <- list(s = cptS, r = cptR, mlw = cptMLW,nblw= cptNBLW, mlg= cptMLG, nblg= cptNBLG, mlgr= cptMLGR, f=cptF)
# Construct BN network with Conditional Probability Table
S.net <- custom.fit(dag, dist=cpt)
轉載請註明出處，本文鏈接：https://www.uj5u.com/qukuanlian/311556.html