[y, l] = knnfwd(net, x)
[y, l] = knnfwd(net, x) takes a matrix x
of input vectors (one vector per row)
and uses the k-nearest-neighbour rule on the training data contained
in net to
produce
a matrix y of outputs and a matrix l of classification
labels.
The nearest neighbours are determined using Euclidean distance.
The ijth entry of y counts the number of occurrences that
an example from class j is among the k closest training
examples to example i from x.
The matrix l contains the predicted class labels
as an index 1..N, not as 1-of-N coding.
net = knn(size(xtrain, 2), size(t_train, 2), 3, xtrain, t_train); y = knnfwd(net, xtest); conffig(y, t_test);Creates a 3 nearest neighbour model
net and then applies it to
the data xtest. The results are plotted as a confusion matrix with
conffig.
kmeans, knnCopyright (c) Ian T Nabney (1996-9)