I have a dataset with labels and datapoints, problem is that rather then a classification problem I want to get a linair estimator, for example :
dataset=prdataset([2,4,6,8]',[1,2,3,4]') testset=prdataset([3,5,7,9]') classifier=dataset*ldc %should probably be changed? result=testset*classifier
result.data now becomes
ans = 1.0e-307 *
0.2225 0.2225 0.2225 0.2225
0.2225 0.2225 0.2225 0.2225
0.2225 0.2225 0.2225 0.2225
0.2225 0.2225 0.2225 0.2225
which is very wrong.
Ideally it would be [1.5,2.5,3.5,4.5]' or something to close to it. Any idea how to do this in PRtools or in something simulair? This is a linair dependancy but I would also like to be able to play around with other types of dependancies? Also it would be a huge bonus of the system was somewhat clever about NaN values which heavily polute my real dataset. I have already found that linearr class but when I use that I get weirdly sized datasets in return,
dataset=prdataset([2,4,6,8]',[1,2,3,4]') testset=prdataset([3,5,7,9]') classifier=dataset*linearr%should probably be changed? result=testset*classifier
gives me the values
0.1000 -0.3000 -0.7000 -1.1000
-0.5000 -0.5000 -0.5000 -0.5000
-1.1000 -0.7000 -0.3000 0.1000
-1.7000 -0.9000 -0.1000 0.7000
which is again incorrect.
In chat they suggested using .* instead of * that resulted in Error using * Inner matrix dimensions must agree.
Error in linearr (line 42)
beta = prinv(X'*X)*X'*gettargets(x);
Error in prmap (line 139)
[d, varargout{:}] = feval(mapp,a,pars{:});
Error in *
Error in dyadicm (line 81)
v1 = a*v1; % train first mapping
Error in prmap (line 139)
[d, varargout{:}] = feval(mapp,a,pars{:});
Error in *
In the linearr code.
Just to be clear I'm looking for a way to, given a large set of values find the set of polynomials that best describes their relation (where the polynomials that are considered is a parameter of the program, in the example 1st order). So in our example the polynomial is 1/2a+0. In my final version I want to use a larger number of parameters (10-20) and it may require quadratic estimation.