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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/knnfwd.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/knnfwd.htm b/sourcecodes/bnt-master/nethelp3.3/knnfwd.htm new file mode 100644 index 00000000..6670920f --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/knnfwd.htm @@ -0,0 +1,66 @@ +<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 |