# functions related to this project
source("cfneval_lib.R")

# read the data
full_data <- read.rdb('example.rdb')

# the names of the cost functions that we want to see
cfn_names <- c("bystroff", "pred_bys_mean", "pred_pb_mean")

# (or just get all the cost functions in the file; note that there
#  are only 8 different color-blind safe plotting colors)
#
#   cfn_names <- colnames(full_data)[-1]

# select the cost functions we are interested in
data <- full_data[cfn_names]

# evaluate some summary statistics
mean(data)
sd(data)

# the five number summary is:
#    minimum, lower-hinge, median, upper-hinge, maximum
lapply(data, fivenum)

# create a notched boxplot
eps_output("plots/boxplot.eps")
boxplot(data, main="Cost Function Comparison", ylab=expression(tau), notch=TRUE)
graphics.off()

# display a kernel density regression
eps_output("plots/densityreg.eps")
denplot(data, main="Cost Function Comparison", xlab=expression(tau),
	ylim=c(0,3.6), ylab="Density", col=cb_colors, lwd=2)
legend("topleft", legend=cfn_names, fill=cb_colors)
graphics.off()

# display a histogram
eps_output("plots/histogram.eps")
pmulthist(data, main="Cost Function Comparison", xlab=expression(tau),
	  ylab="Density", col=cb_colors[1:length(cfn_names)],
	  breaks=5, yscale=1.1)
legend("topright", legend=cfn_names, fill=cb_colors)
graphics.off()