Mercurial > hg > camir-aes2014

diff toolboxes/FullBNT-1.0.7/nethelp3.3/knnfwd.htm @ 0:e9a9cd732c1e tip
first hg version after svn
author: wolffd
date: Tue, 10 Feb 2015 15:05:51 +0000
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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 contained
+in <CODE>net</CODE> to 
+produce 
+a matrix <CODE>y</CODE> of outputs and a matrix <CODE>l</CODE> of classification
+labels.
+The nearest neighbours are determined using Euclidean distance.
+The <CODE>ij</CODE>th entry of <CODE>y</CODE> counts the number of occurrences that
+an example from class <CODE>j</CODE> is among the <CODE>k</CODE> closest training
+examples to example <CODE>i</CODE> from <CODE>x</CODE>.
+The matrix <CODE>l</CODE> contains the predicted class labels
+as 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 to
+the 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>
\ No newline at end of file
author	wolffd
date	Tue, 10 Feb 2015 15:05:51 +0000
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