library("shiny")
library("psychonetrics")
library("IsingFit")
library("IsingSampler")
library("qgraph")

# Load networks:
trueNetwork <- read.csv('http://sachaepskamp.com/files/weiadj.csv')[,-1]
trueNetwork <- as.matrix(trueNetwork)
Symptoms <- rownames(trueNetwork) <- colnames(trueNetwork)
Thresholds <- read.csv('http://sachaepskamp.com/files/thr.csv')[,-1]

# Transform to -1 and 1:
trans <- LinTransform(trueNetwork, Thresholds, from = c(0,1),to=c(-1,1))

# Estimate the distribution:
startdistribution <- CURDIST <- IsingLikelihood(trans$graph, trans$thresholds, beta = 1, responses = c(-1,1))

# Compute layout:
Layout <- qgraph(trueNetwork, layout = "spring", DoNotPlot=TRUE)$layout

# Current state (-1 is off, 0 is unknown, 1 is on)
CUR_STATE <- rep(0, ncol(trueNetwork))

# Compute last means:
P <- startdistribution$Probability
nodes <- as.matrix(startdistribution[,-1])
nodes[nodes==-1] <- 0

LAST_MEANS <- as.vector(t(nodes) %*% P)