README.md

In Benjamin-Couey/CS599.RPackageTwo: Run basic unsupervised learning algorithms

CS599.RPackageTwo

As a project for CS599 Unsupervised Learning, I built this package of simple unsupervised learning algorithms. Specifically, this package provides a simple implementation of principal component analysis to perform dimensionality reduction and a simple implementation of dynamic programming to perform changepoint detection. Note, while these algorithms work, they are more limited in options and less efficient than those algoirthms found in other, more professional packages.

Installation

This package can be installed from GitHub with the following R command.

remotes::install_github("Benjamin-Couey/CS599.RPackageTwo”)

Usage

First, you need to import the installed package.

library("CS599.RPackageTwo")

After that, you can make use of the PCA and DYNPROG functions.

test.matrix <- as.matrix( iris[ 1:50, 1:4 ] )
PCA.fit <- PCA( test.matrix )
PCA.fit$rotation
PCA.fit$lambda

signal <- c( runif(25, min=0, max=2), runif(25, min=2, max=5) )
DYNPROG.fit <- DYNPROG( signal, 10 )
DYNPROG.fit$cost
DYNPROG.fit$change

Benjamin-Couey/CS599.RPackageTwo documentation built on Dec. 31, 2020, 10:45 a.m.