In Artur-man/PCDSL: Proximity Catch Digraphs: Statistical Learning

knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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PCDSL

PCDSL: The Proximity Catch Digraphs for Statistical Learning

Classification methods based on Proximity Catch Digraphs. Proximity Catch Digraphs (PCDs) are special types of proximity graphs.

Installation

library(devtools)
install_github("Artur-man/PCDSL")

Example

This is a basic example which shows you how to solve a common problem:

# input parameters
ntest <- 100     # test data size for each class
nx <- 300        # training data size of x class (majority)
r <- 0.1         # Imbalance Ratio
de <- 0.5        # delta, the overlapping parameter
dimx <- 2        # number of dimensions

# training the classifier
set.seed(1)
x0 <- matrix(runif(dimx*nx,0,1),nrow=nx)
x1 <- matrix(runif(dimx*nx*r,de,1+de),nrow=nx*r)
x <- rbind(x0,x1)
classes <- rep(1:2,c(nx,nx*r))
graph_pcd <- pcd_classifier(x,classes,map="pe",p_pcd=1)

# testing
tx0 <- matrix(runif(dimx*ntest,0,1),nrow=ntest)
tx1 <- matrix(runif(dimx*ntest,de,1+de),nrow=ntest)
tx <- rbind(tx0,tx1)
tclsdata <- rep(1:2,rep(ntest,2))
predicted_pcd_tx <- pcd_classify(tx,graph_pcd)