📄 knnfwd.htm
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<html><head><title>Netlab Reference Manual knnfwd</title></head><body><H1> knnfwd</H1><h2>Purpose</h2>Forward propagation through a K-nearest-neighbour classifier.<p><h2>Synopsis</h2><PRE>[y, l] = knnfwd(net, x)</PRE><p><h2>Description</h2><CODE>[y, l] = knnfwd(net, x)</CODE> takes a matrix <CODE>x</CODE>of input vectors (one vector per row) and uses the <CODE>k</CODE>-nearest-neighbour rule on the training data containedin <CODE>net</CODE> to produce a matrix <CODE>y</CODE> of outputs and a matrix <CODE>l</CODE> of classificationlabels.The nearest neighbours are determined using Euclidean distance.The <CODE>ij</CODE>th entry of <CODE>y</CODE> counts the number of occurrences thatan example from class <CODE>j</CODE> is among the <CODE>k</CODE> closest trainingexamples to example <CODE>i</CODE> from <CODE>x</CODE>.The matrix <CODE>l</CODE> contains the predicted class labelsas an index 1..N, not as 1-of-N coding.<p><h2>Example</h2><PRE>net = knn(size(xtrain, 2), size(t_train, 2), 3, xtrain, t_train);y = knnfwd(net, xtest);conffig(y, t_test);</PRE>Creates a 3 nearest neighbour model <CODE>net</CODE> and then applies it tothe data <CODE>xtest</CODE>. The results are plotted as a confusion matrix with<CODE>conffig</CODE>.<p><h2>See Also</h2><CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knn.htm">knn</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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