snn (version 1.1)

myknn: K Nearest Neighbor Classifier

Description

Implement the K nearest neighbor classification algorithm to predict the label of a new input using a training data set.

Usage

myknn(train, test, K)

Arguments

train

Matrix of training data sets. An n by (d+1) matrix, where n is the sample size and d is the dimension. The last column is the class label.

test

Vector of a test point. It also admits a matrix input with each row representing a new test point.

K

Number of nearest neighbors considered.

Value

It returns the predicted class label of the new test point. If input is a matrix, it returns a vector which contains the predicted class labels of all the new test points.

Details

The tuning parameter K can be tuned via cross-validation, see cv.tune function for the tuning procedure.

References

Fix, E. and Hodges, J. L., Jr. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties. Randolph Field, Texas, Project 21-49-004, Report No.4.

Examples

	# Training data
	set.seed(1)
	n = 100
	d = 10
	DATA = mydata(n, d)

	# Testing data
	set.seed(2015)
	ntest = 100  
	TEST = mydata(ntest, d)
	TEST.x = TEST[,1:d]
	
	# K nearest neighbor classifier
	myknn(DATA, TEST.x, K = 5)