3/22/13 classification - k nearest neighbor classifier example matlab code found at mathworks cannot understand - Stack Overflow

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user574183

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1 Answer

jpjacobs

5,576 9 21

I understood the first example that they have provided because they have clearly explained what

happens in each line. But for the second example which is usually the one that would be used in practice

the most is not explained and iam having a hard time trying to understand it :(. The following are the

code lines that iam having trouble with

training = [mvnrnd([ 1 1], eye(2), 100); ...

mvnrnd([-1 -1], 2*eye(2), 100)];

group = [repmat(1,100,1); repmat(2,100,1)];

and

sample = unifrnd(-5, 5, 100, 2);

and this is the link -> http://www.mathworks.in/help/toolbox/bioinfo/ref/knnclassify.html

Could someone please explain this as this will not only be beneficial to me, but for all others as well.

matlab classification classifier

asked Nov 25 '11 at 15:13

The first line of the code you site constructs a training set of vectors, drawn from a multivariate normal

distribution, centered around [ 1 1] and [-1 -1] respectively, with standard deviations of 1 and 1 for the

sigma x and sigma y for the first class, and 2 and 2 for sigma x and sigma y for the second class. Take

100 of those vectors for each group ( or class).

Then you construct the group vector, which contains group labels: the first 100 are from class 1

( repmat(1,100,1) is actually the same as ones(100,1)) and the second 100 are from class 2

( repmat(2,100,1) == ones(100,1)*2).

The second chunk of code you cite actually just generates a matrix containing 100 random data rows, all

in the range [-5 , 5] having 2 dimensions (so 2 columns). This matrix gets used to test the classification

on.

You might also take the habit of using the matlab help or doc function on functions you don't

know/understand.

answered Nov 25 '11 at 15:38

1 +1 for the help/doc comment. He/She might have done it already, but I like to see some evidence of that inthe question. Jim Clay Nov 25 '11 at 17:01

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