PGM4 - Compute Marginal

require(reshape)
source("PM1.R")

#FactorMarginalization Sums given variables out of a factor.
ComputeJointDistribution <- function(F)  {
  # Check for empty factor list
  if (length(F) == 0){
    cat('Error: empty factor list');
      Joint = list(var = NA, card = NA, val = NA);    
      return(Joint);
  }

Joint = F[[1]];
for (i in 2:length(F)){
  Joint = FactorProduct(Joint, F[[i]]);
}
return(Joint);
}

#ComputeMarginal Computes the marginal over a set of given variables
ComputeMarginal <- function(V, F, E)  {
  # Check for empty factor list
  if (length(F) == 0){
    cat('Error: empty factor list');
      M = list(var = NA, card = NA, val = NA);    
      return(M);
  }
  M = list(var = NA, card = NA, val = NA);
  joint = ComputeJointDistribution(F);
  joint = list(joint);
  evidence =  ObserveEvidence(joint, E);
  evidence = evidence[[1]];
  evidence$val = evidence$val / sum(evidence$val);

  var = setdiff(evidence$var, V);
  M = FactorMarginalization(evidence, var);
  return(M);
}

V <- c(2, 3);
A <- list(var = c(1), card = c(2), val = c(0.11, 0.89));
B <- list(var = c(2, 1), card = c(2, 2), val = c(0.59, 0.41, 0.22, 0.78));
C <- list(var = c(3, 2), card = c(2, 2), val = c(0.39, 0.61, 0.06, 0.94));
F <- list(A, B, C);
E <- matrix(c(1, 2), byrow = T, nrow = 1)
ComputeMarginal(V, F, E)