This code sets up a plot of the data such that the

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Unformatted text preview: represent a 3 are red. See t t e il ( and l g n eed ( for information on adding the title and legend. Before using c a s f ( we can set up a lsiy vector that contains the actual labels for our data, to train the classification algorithm. If we don't know the labels for the data, then the element in the g o pvector ru should be an empty string or N N (See grouping data a. ( for more information.) > gop=oe(0,) > ru ns401; > gop2140 =2 > ru(0:0) ; We can now classify our data. > [ls,err PSEIR lg,cef =casf(ape sml,gop 'ier) > cas ro, OTRO, op of] lsiysml, ape ru, lna'; The full details of this line can be examined in the Matlab help file linked above. What we care about are c a s which contains the labels that the algorithm thinks that ls, each data point belongs to, and c e f which contains information about the line that algorithm created to separate the data into each class. of, We can see the efficacy of the algorithm by comparing c a sto g o p ls ru. > sm(ls=gop >u cas=ru) as= n 39 6 This compares the value in c a sto the value in g o p The answer of 369 tells us that the algorithm correctly determined the class of the point 369 times, out of a ls ru. possible 400 data points. This gives us an empirical error rat e of 0.0775. We can see the line produced by LDA using c e f of. > k=cef12.os; > of(,)cnt > l=cef12.ier > of(,)lna; > f=srnf' =%+gx%*' k l1,l2) > pit(0 g%*+gy, , () (); > epo(,[i(ape:1) mxsml(,),mnsml(,),mxsml(,)]; > zltf mnsml(,), a(ape:1) i(ape:2) a(ape:2)) wikicour Stat841&pr intable= yes 12/74 10/09/2013...
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This document was uploaded on 03/07/2014.

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