It is in fact possible to create new operators or overload existing ones for built-in data types in MATLAB. I describe one example of this in my answer to another SO question about modifying the default overflow behavior of integer types. First, you may want to look at what methods currently exist for cell arrays. … Read more
According to the official libsvm documentation (Section 7): LIBSVM implements the “one-against-one” approach for multi-class classification. If k is the number of classes, then k(k-1)/2 classifiers are constructed and each one trains data from two classes. In classification we use a voting strategy: each binary classification is considered to be a voting where votes can … Read more
Here’s a complete example, using the following functions from the Bioinformatics Toolbox: SVMTRAIN, SVMCLASSIFY, CLASSPERF, CROSSVALIND. load fisheriris %# load iris dataset groups = ismember(species,’setosa’); %# create a two-class problem %# number of cross-validation folds: %# If you have 50 samples, divide them into 10 groups of 5 samples each, %# then train with 9 … Read more
MATLAB’s FOR loop is static in nature; you cannot modify the loop variable between iterations, unlike the for(initialization;condition;increment) loop structure in other languages. This means that the following code always prints 1, 2, 3, 4, 5 regardless of the value of B. A = 1:5; for i = A A = B; disp(i); end If … Read more
I’m not sure if you can do exactly what you want, but it is possible to do quite a lot with events. First, this sounds like some sort of numerical calculation of first passage time (a.k.a first hitting time). If these “particles” are stochastic, stop and don’t use ode45 but rather a a method appropriate … Read more
Here is a compact solution without loop, cellfun or arrayfun: chainCode=”11012321170701000700000700766666666666665555555544443344444333221322222322″; numCode = chainCode – ‘0’; % turn to numerical array J=find(diff([numCode(1)-1, numCode])); relMat=[numCode(J); diff([J, numel(numCode)+1])];
reshape is of course the proper solution, as stated by @gnovice. A nice feature of reshape is that it allows this: A = 1:12; B = reshape(A,4,[]); B = 1 5 9 2 6 10 3 7 11 4 8 12 So if you don’t know how many columns there will be, reshape will compute … Read more
It is not a Matlab issue; it is a floating point issue. You’ll get the same result in C++ (or any programming language that conforms to IEEE754 for that matter): #include <iostream> int main(int, char **) { std::cout << (1.1-0.2==0.9) << std::endl; return 0; } output: 0 This is because 1.1 and 0.9 cannot be … Read more
Here is vectorized implementation for computing the euclidean distance that is much faster than what you have (even significantly faster than PDIST2 on my machine): D = sqrt( bsxfun(@plus,sum(A.^2,2),sum(B.^2,2)’) – 2*(A*B’) ); It is based on the fact that: ||u-v||^2 = ||u||^2 + ||v||^2 – 2*u.v Consider below a crude comparison between the two methods: … Read more
Based on the not-working, but well thought, idea of @Ilya Kobelevskiy here the final workaround: function pipeConnection(numIterations,inputFile) for i=1:numIterations while(exist(‘inputfile’,’file’)) load inputfile; % read inputfile -> inputdata output = myFunction(inputdata); delete(‘inputfile’); end % Write output to file % Call external application to process output data % generate new inputfile end; Another convenient solution would be … Read more