library(CRFutil)
# Graph formula:
grphf <- ~A:B:C
adj <- ug(grphf, result="matrix")
n.states <- 2
fitc <- make.crf(adj, n.states)
fitc <- make.features(fitc)
fitc <- make.par(fitc, 6)
# Parameterization:
fitc$node.par[1,1,] <- 1
fitc$node.par[2,1,] <- 2
fitc$node.par[3,1,] <- 2
fitc$edge.par[[1]][1,1,1] <- 3
fitc$edge.par[[1]][2,2,1] <- 3
fitc$edge.par[[2]][1,1,1] <- 2
fitc$edge.par[[2]][2,2,1] <- 4
fitc$edge.par[[3]][1,1,1] <- 5
fitc$edge.par[[3]][2,2,1] <- 6
s1<-1
s2<-2
f0 <- function(y){ as.numeric(c((y==1),(y==2)))} # Feature function
configs <- rbind(
c(1,1,1),
c(1,2,1),
c(2,2,1),
c(2,2,2)
)
# Make up a test parameter vector with theta_1 = 1, theta_2 = 2, etc:
theta <- c(1,2,3,4,5,6)
fitc$par <- theta
jk <- make.pots(parms=fitc$par, crf = fitc,
rescaleQ=F, replaceQ=T, format = "regular", printQ=T)
# Gradient functionality testing:
cfg <- configs[4,]
cfg
conditional.phi.mat.test(config = cfg, condition.element.number = 1, crf = fitc, ff = f0, printQ=T)
#
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