# Try to transform an equilibrium into a simple rule
#
# Almost nothing implemented yet.
# Not clear that we get large value from this route
example.table.rule = function() {
setwd("D:/libraries/gtree/myproject")
gameId = "UltimatumGame"
tg = get.tg(gameId = gameId,never.load = FALSE)
ise.df = tg$ise.df
eq.li = gambit.solve.eq(tg)
eq = eq.li[[1]]
eq.tables(eq, tg)
eq.table.rules(eq, tg)
}
# Specify the key variables for each action that can be played in a tg
make.tg.action.keys = function(tg) {
actions = unique(tg$ise.df$.var)
}
example.find.perfect.predictor.cols = function() {
T = 100
dat = data_frame(a=sample(0:1, T, replace = TRUE), b=runif(T,-1,1), c=b^2, y=(1-c)>0.5)
df = select(df,b,y)
is.perfect.predictor(df = select(dat,b,y))
is.perfect.predictor(df = select(dat,c,y))
is.monotone.predictor(df = select(dat,b,y))
is.monotone.predictor(df = select(dat,c,y))
accept = quote((payoff_2 - alpha*(payoff_1-payoff_2))>0)
}
find.perfect.predictor.cols = function(df, var) {
restore.point("find.perfect.predictor.cols")
}
# Is a numeric x a monotone predictor for y
is.monotone.predictor = function(x,y, df = as_data_frame(list(x=x,y=y))) {
ord = order(df[[1]])
df = df[ord,]
is.highest = which(!is.true(lead(df[[2]]) == df[[2]]))
n_distinct(df[[2]][is.highest]) == length(is.highest)
}
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