RPEnsemble (version 0.1)

RPParallel: Chooses a projection from each block in parallel

Description

Makes B1 calls to RPChoose or RPChooseSS in parallel and returns the results as a matrix.

Usage

RPParallel(XTrain, YTrain, XVal, YVal, XTest, d, B1 = 100, B2 = 100, 
base = "LDA", projmethod = "Haar", estmethod = "resub", k = c(3, 5), 
cores = 2, splitsample = FALSE,  ...)

Arguments

XTrain

An n by p matrix containing the training data feature vectors

YTrain

A vector of length n containing the classes (either 1 or 2) of the training data

XVal

An n.val by p matrix containing the validation data feature vectors

YVal

A vector of length n.val of the classes (either 1 or 2) of the validation data

XTest

An n.test by p matrix containing the test data feature vectors

d

The lower dimension of the image space of the projections

B1

The number of blocks

B2

The size of each block

base

The base classifier one of "knn","LDA","QDA" or "other"

k

The options for k if base is "knn"

projmethod

Either "Haar" or "axis"

estmethod

Method for estimating the test errors to choose the projection: either resubstitution "resub" or leave-one-out "loo"

cores

The number of computer cores to use

splitsample

If TRUE will use the validation set (XVal, YVal) to estimate the test error in RPChooseSS. If FALSE will use estmethed to estimate the test error in

...

Optional further arguments if base = "other"

Value

• If splitsample = FALSE, then returns an n+n.test by B1 matrix, each row containing the result of a call to RPChoose. If splitsample = TRUE, then returns an n.val+n.test by B1 matrix, each row containing the result of a call to RPChooseSS.

Details

Makes B1 calls to RPChoose or RPChooseSS in parallel.

References

Cannings, T. I. and Samworth, R. J. (2015) Random projection ensemble classification. http://arxiv.org/abs/1504.04595

See Also

RPChoose, RPChooseSS

Examples

Train <- RPModel(1, 50, 20, 0.5)
Test <- RPModel(1, 100, 20, 0.5)
Out <- RPParallel(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, 
d = 2, B1 = 100, B2 = 10, base = "LDA", projmethod = "Haar", 
estmethod = "resub", cores = 2)
colMeans(Out)