animation (version 1.1-4)

knn.ani: Demonstrate kNN classification algorithm on the 2D plane...

Description

Demonstrate kNN classification algorithm on the 2D plane Demonstrate the process of k-Nearest Neighbour classification on the 2D plane.

Usage

knn.ani(train, test, cl, k=10, interact=FALSE, tt.col=c("blue", "red"),
    cl.pch=seq_along(unique(cl)), dist.lty=2, dist.col="gray",
    knn.col="green")

Arguments

train

matrix or data frame of training set cases containing only 2 columns

test

matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case. It should also contain only 2 columns. This data set will be ignored if interact = TRUE; see interact below.

cl

factor of true classifications of training set

k

number of neighbours considered.

interact

logical. If TRUE, the user will have to choose a test set for himself using mouse click on the screen; otherwise compute kNN classification based on argument test.

tt.col

a vector of length 2 specifying the colors for the training data and test data.

cl.pch

a vector specifying symbols for each class

dist.lty,dist.col

the line type and color to annotate the distances

knn.col

the color to annotate the k-nearest neighbour points using a polygon

Value

• A vector of class labels for the test set.

Details

For each row of the test set, the $k$ nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote, with ties broken at random. For a single test sample point, the basic steps are:

1. locate the test point
2. compute the distances between the test point and all points in the training set
3. find$k$shortest distances and the corresponding training set points
4. vote for the result (find the maximum in the table for the true classifications)

As there are four steps in an iteration, the total number of animation frames should be 4 * min(nrow(test), ani.options("nmax")) at last.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. http://animation.yihui.name/dmml:k-nearest_neighbour_algorithm

See Also

knn

Examples

## a binary classification problem 
oopt = ani.options(interval = 2, nmax = 10)
x = matrix(c(rnorm(80, mean = -1), rnorm(80, mean = 1)),
ncol = 2, byrow = TRUE) 
y = matrix(rnorm(20, mean = 0, sd = 1.2), ncol = 2) 
knn.ani(train = x, test = y, cl = rep(c("first class", "second class"),
each = 40), k = 30)

x = matrix(c(rnorm(30, mean = -2), rnorm(30, mean = 2),
rnorm(30, mean = 0)), ncol = 2, byrow = TRUE)
y = matrix(rnorm(20, sd = 2), ncol = 2)
knn.ani(train = x, test = y, cl = rep(c("first", "second", "third"),
each = 15), k = 25, cl.pch = c(2, 3, 19), dist.lty = 3)

# an interactive demo: choose the test set by mouse-clicking
ani.options(nmax = 5)
knn.ani(interact = TRUE)

ani.options(ani.height = 500, ani.width = 600, nmax = 10,
interval = 2, title = "Demonstration for kNN Classification",
description = "For each row of the test set, the k nearest (in Euclidean
distance) training set vectors are found, and the classification is
decided by majority vote, with ties broken at random.")
ani.start()
par(mar = c(3, 3, 1, 0.5), mgp = c(1.5, 0.5, 0))
knn.ani()
ani.stop()

ani.options(oopt)