choose_int.nv: Choose intervention with your own choice of value function
In neilbramley/acl_source: Functions for active causal learning

Computes the most useful intervention according to a target function. Requires a dataframe called li in the global workspace that encodes the likelihoods of any outcome of any intervention created with likelihood() Requires a dataframe called o which incodes the outcome space (see example)

1	choose_int.nv(ints, g, pDist, FUN, ...)

`ints`	matrix of possible interventions (rows), 1 is on, 0 is free, -1 is off.
`g`	matrix of hypothesis graphs, one line is one graph to be written by row to a matrix.
`pDist`	is the prior distribution over these graphs.
`FUN`	is the objective function of your choice. Additional arguments can then be passed to this objective function

ints<-as.matrix(expand.grid(rep(list(0:2), 3)))
ints[ints==2]<--1
o<-expand.grid(rep(list(0:1 ), sqrt(dim(g)[2])))
dist<-prior('flat',g)
li<-likelihood(g,.1,.8)
choose_int.nv(ints, g, dist, FUN=shannon_entropy)