MATLAB: How to cut down the size of an array of data

arraydataMATLABsizing

I have a few arrays, with one column each, of different sizes (200127, 200126, 200120, etc.) In order to preform the calculations I want to do, I need them to all be the same length. Is there a way to cut them all down to the smallest array I have?

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It'd be easier if there weren't different variables, but since they're inconsistent in length that makes it a little more of a pain unless you were to initially use a cell array where each cell column contained one of these arrays.

But, the syntax you're looking for to do the size reduction is

[mn,imn]=min(length(A),length(B),...,length(Z));  % minimum length of all your arrays and which one
A(N+1:end)=[];
B(N+1:end)=[];
C(N+1:end)=[];
 ...   % you get the picture, excepting, of course for whichever variable it is that corresponds to variable imn

Alternatively, you can save instead of cull...

A=A(1:N);
B=B(1:N);
C=C(1:N);
...

this way you don't need to worry about which doesn't have >N elements.

Of course, going on into the problem, you'll undoubtedly want to end up with

Data=[A(1:N) B(1:N) C(1:N) ... Z(1:N)];

so you can now programmatically address all by simply using subscripting expressions instead of having superfluous variables to deal with.

How were these arrays created in the first place? Quite possibly you could have solved all the issues while reading them or some other way before got into this fix.

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Don't name your structure fields using hard-coded numbers like that; instead, use an array:

Data.N(1).XY = rand(10,2);
Data.N(2).XY = rand(15,2);

Then, when you want to plot, you can simply loop over this:

axes; hold on;
for ii = 1:length(Data.N)
  h(ii) = plot(Data.N(ii).XY(:,1), Data.N(ii).XY(:,2));
end

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Rewriting the above to compute for any number of bets would seem simplest--

function [stat,mxIdx] = bestbets(odds)
% return betting odds prediction statistic and max row given odds array
    C = nanmean(odds,2);              %computes averages of rows 
    [D,mxIdx] = max(odds,[],2);       %finds max value of rows
    E = 1/sum(1./D);                  %ACE
    F = 1/sum(1./C);                  % Mean ACE
    G = C./F;                         % True odds
    stat=zeros(size(D));              % preallocate the statistic
    indx=1:length(D);                 % index array 1:N observations
    for i=1:length(D)
      stat(i,:) = 1/sum([1/D(i); 1./G(~(ix==i))])-1;  % compute for each
    end

Above reproduces your check array values for the given dataset.

NB: Above presumes the input odds array has been cleaned up before being passed, the problem about generating a suitable file format is a different discussion than than the algorithm implementation.

The loop could be eliminated with accumarray but left so could more easily get the implementation.

The "trick" is in building the logical addressing vector ~(indx==i) to select the elements from the G array with the exception of the one element being computed.

PS: While the computer doesn't care, as a general best practice I'd suggest using descriptive names for variables rather than just C, D, etc. It'll be bound to help in the long run as you develop into using Matlab and other programming languages more. I'll give high marks for brevity; that is also in my (nsh :) ) opinion an objective as exceedingly long variable names tend to just add clutter more than clarify, but at least some sort of a mnemonic to relate to the purpose is important for legibility.

ERRATUM Corrected order of function line to get function keyword first...

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