library(StratTourn) library(xtable) library(ggplot2) library(reshape2) library(googleVis)
library(StratTourn) library(xtable) library(Shiny) setwd("D:/libraries/StratTourn") setwd("D:/libraries/StratTourn/studies") tourn.name = "Tourn_Noisy_PD_20141110_054429" tourn = load.tournament(paste0(tourn.name,".tou")) # Data for each match md = tourn$dt md = add.other.var(md,c("strat","u")) md$delta.u = md$u - md$other.u # Names of all strategies strats = unique(md$strat) # Data for each round file = tourn$rs.file rd = fread(file) rd = as.tbl(as.data.frame(rd)) rd = add.other.var(rd,c("strat","u")) rank.dt = strat.rank.from.matches(md) # Names of all strategies strats = unique(rd$strat) # Perhaps select a subset of strategies used.strats = strats used.strats = c("mystrategy.knallhart", "coop1_duoinfernale", "noisesafe.tit.for.tat", "paranoid" , "prof.strat", "erstertest1") ard = rd if (!identical(used.strats,strats)) { rows = rd$strat %in% used.strats & rd$other.strat %in% used.strats rd = ard[rows,] }
end.t = 5 start.t = 1
rd = add.other.var(rd,c("x","cost","type")) rd$cost.pair = paste0(rd$type,"_",rd$other.type) cumd = mutate(group_by(rd,strat,other.strat, cost.pair), sum.single.x=x+other.x) td = summarise(group_by(cumd,strat,other.strat,t,cost.pair), sum.x=mean(sum.single.x), num.obs = length(t)) t.seq = 1:min(max(td$t),end.t) tsd = do(group_by(td, strat, other.strat, cost.pair), get.smoothed.vals(.,xout=t.seq, xvar="t",yvar="sum.x", wvar="num.obs", spar=0.2)) # Order strategies according to their rank rank.strats = rank.dt$strat tsd$strat = factor(tsd$strat, rank.strats, ordered=TRUE) tsd$other.strat = factor(tsd$other.strat, rank.strats, ordered=TRUE)
tsd.big <- summarise(group_by(tsd,strat,other.strat,t), sum.x=mean(sum.x)) ggplot(data=tsd.big, aes(x=t, y =sum.x, colour = strat)) + geom_line(size=I(1)) + facet_grid(other.strat~strat)+ geom_hline(yintercept=100, size=0.5, col="black",alpha=0.5)
ggplot(data=tsd, aes(x=t, y =sum.x, colour = cost.pair, group = cost.pair)) + geom_line() + facet_grid(other.strat~strat)
cat("\n")
The plots show the sum of the offers of both strategies given the cost.pair.
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