SupportVectorMachine.Rmd

---
title: "R: Support Vector Machine"
author: "Max Chen 2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Library

Load required packages

```{r lib}
# install.packages("mvtnorm")
# install.packages("e1071")
# install.packages("pracma")
suppressMessages(library(mvtnorm))
suppressMessages(library(e1071))
suppressMessages(library(pracma))
```

## Data

Generate sample data

```{r dat}
rndsample = rmvnorm(100,mean=c(3,5),sigma=matrix(c(1,0.5,0.5,2),2,2))
colnames(rndsample) = c("X1","X2")
df = data.frame(rndsample)
par(pty="s")
plot(df,asp=1,xlim=c(-2,8),ylim=c(0,10))
```

## Model

Fit model and predict

```{r svm}
model = svm(x=df,y=NULL,type="one-classification",gamma=0.25,nu=0.05)
print(model)
pred = predict(model,df)
table(pred)
par(pty="s")
plot(df[!pred,],col="red",pch=4,asp=1,xlim=c(-2,8),ylim=c(0,10))
points(df[pred,],col="blue")
```

## Visualize

Plot decision boundary

```{r vis}
XY = meshgrid(seq(-2,8,length=100),seq(0,10,length=100))
XY = data.frame(as.vector(XY$X),as.vector(XY$Y))
colnames(XY) = c("X1","X2")
predXY = predict(model,XY,decision.values=TRUE)
par(pty="s")
plot(XY[!predXY,],col="red",pch=16,asp=1,xlim=c(-2,8),ylim=c(0,10))
points(XY[predXY,],col="blue",pch=16)
points(df)
```