# $Id: ltplots.R,v 1.1 2007/10/02 10:43:07 kaip Exp $
#
#
# Copyright (c) 2007 Kai Puolamaki <Kai.Puolamaki@iki.fi>
#
# Permission to use, copy, modify, and distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
#
#
# T-61.3050 Machine Learning: Basic Principles
# Stupid example for the 3/2007 lecture
#

# This is just "schematic illustration", therefore choose some functions
# that "look right".

pdf("ltcomplexity.pdf")
plot(c(0,1),c(0,1),type="n",xlab="model complexity",ylab="error",xaxt="n",yaxt="n")
x <- 0:200/200
lines(x,pmax(0,0.5*exp(-5*x)-.01),lty="solid")
lines(x,.1+2*(x-.5)*(x-.5)*exp(x/2),lty="dashed")
legend(x="topright",legend=c("test set","training set"),lty=c("dashed","solid"),bg="white")
dev.off()

pdf("ltdatasize.pdf")
plot(c(0,1),c(0,1),type="n",xlab="training set size",ylab="error",xaxt="n",yaxt="n")
x <- 0:200/200
lines(x,pmax(0,.3-exp(-5*x)),lty="solid")
lines(x,.3+exp(-5*x),lty="dashed")
legend(x="topright",legend=c("test set","training set"),lty=c("dashed","solid"),bg="white")
dev.off()