library(CRFutil)
library(plot.matrix)
# Graph:
grphf <- make.lattice(num.rows = 5, num.cols = 5, cross.linksQ = T)
adj <- ug(grphf, result="matrix") # adjacency (connection) matrix
node.names <- colnames(adj)
# Check the graph:
gp <- ug(grphf, result = "graph")
plot(gp)
dev.off()
# Make up random parameters for the graph and simulate some data from it:
known.model.info <- sim.field.random(adjacentcy.matrix=adj, num.states=2, num.sims=25, seed=1)
samps <- known.model.info$samples
known.model <- known.model.info$model
# Fit an MRF to the sample with the intention of obtaining the estimated parameter vector theta
# Use the standard parameterization (one parameter per node, one parameter per edge):
s1 <- "white" # State 1 name
s2 <- "black" # State 2 name
# s1 <- 1 # State 1 name
# s2 <- 2 # State 2 name
fit <- fit_mle_params(grphf, samps,
parameterization.typ = "standard",
opt.method = "L-BFGS-B",
#opt.method = "CG",
inference.method = infer.trbp,
state.nmes = c(s1,s2),
num.iter = 1,
mag.grad.tol = 1e-3,
plotQ = T)
# Prep for computing config probs:
logZ <- infer.trbp(fit)$logZ
logZ
f0 <- function(y){ as.numeric(c((y==s1),(y==s2))) }
# Make up a configuration:
X <- sample(x = c(s1,s2), size = fit$n.nodes, replace = T)
# See what the configuration looks like
Xmat <- t(array(X, c(sqrt(fit$n.nodes),sqrt(fit$n.nodes))))
Xmat
par(mar=c(5.1, 4.1, 4.1, 4.1))
plot(Xmat, col=c("white", "black"))
# Compute the configuration probability: \Pr({\bf X}) = \frac{1}{Z} e^{E({\bf X})}
EX <- config.energy(config = X,
edges.mat = fit$edges,
one.lgp = fit$node.energies,
two.lgp = fit$edge.energies, # make sure use same order as edges!
ff = f0)
EX
EX - logZ # log(Pr(X))
exp(EX - logZ) # Pr(X)
# Try some of the sampled configurations. They probably have higher Pr(X)'s:
X <- samps[1,]
X[which(X == 1)] <- s1
X[which(X == 2)] <- s2
X
# See what the configuration looks like
Xmat <- t(array(X, c(sqrt(fit$n.nodes),sqrt(fit$n.nodes))))
Xmat
plot(Xmat, col=c("white", "black"))
# Pr(Xmat):
EX <- config.energy(config = X,
edges.mat = fit$edges,
one.lgp = fit$node.energies,
two.lgp = fit$edge.energies, # make sure use same order as edges!
ff = f0)
EX
EX - logZ # log(Pr(X))
exp(EX - logZ) # Pr(X)
# Most likely config?
#decode.trbp(fit, verbose = T)
decode.junction(fit)
decode.greedy(fit)
#decode.exact(fit)
#decode.tree(fit)
decode.lbp(fit)
# Pr of most likely configuration??:
X <- decode.junction(fit)
X
X[which(X == 1)] <- s1
X[which(X == 2)] <- s2
X
# See what the configuration looks like
Xmat <- t(array(X, c(sqrt(fit$n.nodes),sqrt(fit$n.nodes))))
Xmat
plot(Xmat, col=c("white", "black"))
# Pr(Most likely config): Should be relatively high compared to other configs
EX <- config.energy(config = X,
edges.mat = fit$edges,
one.lgp = fit$node.energies,
two.lgp = fit$edge.energies, # make sure use same order as edges!
ff = f0)
EX
EX - logZ # log(Pr(X))
exp(EX - logZ) # Pr(X)
# This model is (just) small enough that we can examine the estimated joint distribution and compare
# it with the true joint distribution:
# Compute the joint dist from the estimated theta:
gR.dist.info.mle.model <- distribution.calc(fit, logZ.calc = infer.junction)
gR.dist.info.mle.model$logZ
logZ.mle.model <- gR.dist.info.mle.model$logZ # Uses exact computation for logZ
logZ.mle.model
# Just to see if this works:
infer.exact(fit)$logZ
joint.mle <- as.data.frame(as.table(gR.dist.info.mle.model$state.probs))
dim(joint.mle)
pr.idx <- ncol(joint.mle)
#round(joint.mle[,pr.idx],3)
#plot(joint.mle[,pr.idx], typ="h")
# TRUE joint distribution:
# Compare estimated joint distribution to the true distribution
# Now compute the joint dist from the estimated theta
out.pot.true <- make.pots(parms = known.model$par, crf = known.model, rescaleQ = F, replaceQ = T)
gR.true <- make.gRbase.potentials(known.model, node.names = node.names, state.nmes = c(s1,s2))
known.model$node.potentials <- gR.true$node.potentials
known.model$edge.potentials <- gR.true$edge.potentials
gR.dist.info.true.model <- distribution.calc(known.model, logZ.calc = infer.exact)
logZ.true.model <- gR.dist.info.true.model$logZ
logZ.true.model
joint.true <- as.data.frame(as.table(gR.dist.info.true.model$state.probs))
colnames(joint.true)
colnames(joint.mle) # NODE ORDERING THE SAME ??????
# cbind(
# round(joint.mle[,pr.idx],3),
# round(joint.true[,pr.idx],3)
# )
# Distribution discrepancy measures
# par(mfrow=c(2,1))
# ymax <- max(joint.true[,pr.idx], joint.mle[,pr.idx])
# plot(joint.true[,pr.idx], typ="h", ylim=c(0,ymax))
# plot(joint.mle[,pr.idx], typ="h", ylim=c(0,ymax))
# dev.off()
# plot(abs(joint.true[,pr.idx] - joint.mle[,pr.idx]), typ="h", ylab="abs-diff (Pr-units)")
# plot(joint.true[,pr.idx] - joint.mle[,pr.idx], typ="h", ylab="diff (Pr-units)")
#plot(abs(joint.true[,pr.idx] - joint.mle[,pr.idx])/joint.true[,pr.idx]*100, typ="h", ylab="%-diff (%)")
# Kullback-Leibler distance between true and estimated distribution
KLD(joint.true[,pr.idx], joint.mle[,pr.idx])
# Jensen-Shannon distance between true and estimated distribution (Should be bound between 0 and 1)
PD <- joint.true[,pr.idx]
QD <- joint.mle[,pr.idx]
RD <- 0.5*(joint.true[,pr.idx] + joint.mle[,pr.idx])
JSD <- 0.5 * (KLD(PD,RD,base = 2)$sum.KLD.px.py + KLD(QD,RD, base = 2)$sum.KLD.px.py)
JSD
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