nn.Rmd

---
title: "Neural Networks"
author: "Kyriakos Chatzidimitriou (kyrcha@gmail.com)"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
    highlight: textmate
    includes:
      after_body: footer.html
    css: style.css
  pdf_document:
    includes:
      before_body: head.tex
      after_body: footer.tex
---

## Neural Networks
```{r}
#library(neuralnet)
#data <- read.table('breast-cancer-wisconsin.data', na.strings = "?", sep=",")

#data <- data[,-1]

#names(data) <- c("ClumpThickness", 
#                "UniformityCellSize", 
#                "UniformityCellShape", 
#                "MarginalAdhesion",
#                "SingleEpithelialCellSize",
#                "BareNuclei",
#                "BlandChromatin",
#                "NormalNucleoli",
#                "Mitoses",
#                "Class")
#set.seed(1234)
#ind <- sample(2, nrow(data), replace=TRUE, prob=c(0.7, 0.3))
#trainData <- data[ind==1,]
#validationData <- data[ind==2,]
#trainData <- trainData[complete.cases(trainData),]
#validationData <- validationData[complete.cases(validationData),]
#nn.bp <- neuralnet(f, data=trainData, hidden=10, err.fct="ce", linear.output=FALSE, #learningrate=0.01)
# get the predictions
#output <- compute(nn.bp, subset(validationData, select=subset))$net.result
# from numeric values between 0 and 1 make them T or F
#output[output > 0.5] <- TRUE
#output[output < 0.5] <- FALSE
#cat("\nConfusion matrix:\n")    
#xtab = table(output, validationData$Class)
#print(xtab)
#cat("\nEvaluation:\n\n")
#accuracy = sum(output == validationData$Class)/length(validationData$Class)
#precision = xtab[1,1]/sum(xtab[,1])
#recall = xtab[1,1]/sum(xtab[1,])
#f = 2 * (precision * recall) / (precision + recall)
#cat(paste("Accuracy:\t", format(accuracy, digits=2), "\n",sep=" "))
#cat(paste("Precision:\t", format(precision, digits=2), "\n",sep=" "))
#cat(paste("Recall:\t\t", format(recall, digits=2), "\n",sep=" "))
#cat(paste("F-measure:\t", format(f, digits=2), "\n",sep=" "))
```