pcd_cccd_classify: Classify an unlabelled data set with a PCD of composite...

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

Classify an unlabelled data set with a PCD of composite cover. The class cover is composed of simplical and spherical proximity regions.

Description

Classify an unlabelled data set with a PCD of composite cover. The class cover is composed of simplical and spherical proximity regions.

Usage

pcd_cccd_classify(data, dvalid, graph_pcd)

Arguments

data	An m-by-d matrix of the test data set.
dvalid	The distance matrix for the distances between the test and training data.
graph_pcd	A proximity catch digraph (PCD).

Value

Predicted labels of the test data set.

Examples

# 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_cccd_classifier(x,classes,map="pe",p_pcd=1,p_cccd=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))
library(flexclust)
d_tx_x <- as.matrix(dist2(tx,x))
predicted_pcd_tx <- pcd_cccd_classify(tx,d_tx_x,graph_pcd)

Artur-man/PCDSL documentation built on Feb. 24, 2024, 11:15 p.m.