knnDecision: Classification Decision Boundary
In davpinto/fastknn: Build Fast k-Nearest Neighbor Classifiers

Description Usage Arguments Value Author(s) Examples

Decision boundary of the fastknn classifier.

1 2	knnDecision(xtr, ytr, xte, yte, k, method = "dist", normalize = NULL, dpi = 150)

`xtr`	matrix containing the training instances. If `xtr` is not a bidimensional matrix only the first two columns will be considered.
`ytr`	factor array with the training labels.
`xte`	(optional) Matrix containing the test instances. The test points will be plotted over the surface boundary. If missing, the training points will be plotted instead. If `xte` is not a bidimensional matrix only the first two columns will be considered.
`yte`	(optional) Factor array with the test labels.
`k`	number of neighbors considered.
`method`	method used to infer the class membership probabilities of the test instances. See `fastknn` for more details.
`normalize`	variable scaler as in `fastknn`.
`dpi`	a scalar that defines the graph resolution (default = 150). It means that `dpi^2` data points will be generated from the original dataset to draw the decision boundary. So, for large values (>= 300) it may take too much time to plot.

ggplot2 object.

David Pinto.

## Not run: 
library("caTools")
library("fastknn")

data("spirals")

x <- data.matrix(spirals$x)
y <- spirals$y

set.seed(2048)
tr.idx <- which(sample.split(Y = y, SplitRatio = 0.7))
x.tr <- x[tr.idx,]
x.te <- x[-tr.idx,]
y.tr <- y[tr.idx]
y.te <- y[-tr.idx]

knnDecision(xtr = x.tr, ytr = y.tr, xte = x.te, yte = y.te, k = 10)

## End(Not run)