cosmo classify nn

function predicted = cosmo_classify_nn(samples_train, targets_train, samples_test, unused)
    % nearest neighbor classifier
    %
    % predicted=cosmo_classify_nn(samples_train, targets_train, samples_test[, opt])
    %
    % Inputs:
    %   samples_train      PxR training data for P samples and R features
    %   targets_train      Px1 training data classes
    %   samples_test       QxR test data
    %   opt                (currently ignored)
    %
    % Output:
    %   predicted          Qx1 predicted data classes for samples_test
    %
    % Example:
    %     ds=cosmo_synthetic_dataset('ntargets',5,'nchunks',15);
    %     test_chunk=1;
    %     te=cosmo_slice(ds,ds.sa.chunks==test_chunk);
    %     tr=cosmo_slice(ds,ds.sa.chunks~=test_chunk);
    %     pred=cosmo_classify_nn(tr.samples,tr.sa.targets,te.samples,struct);
    %     % show targets and predicted labels (100% accuracy)
    %     disp([te.sa.targets pred])
    %     %||      1     1
    %     %||      2     2
    %     %||      3     3
    %     %||      4     4
    %     %||      5     5
    %
    % See also: cosmo_crossvalidate, cosmo_crossvalidation_measure
    %
    % #   For CoSMoMVPA's copyright information and license terms,   #
    % #   see the COPYING file distributed with CoSMoMVPA.           #

    [ntrain, nfeatures] = size(samples_train);
    [ntest, nfeatures_] = size(samples_test);
    ntrain_ = numel(targets_train);

    if nfeatures ~= nfeatures_ || ntrain_ ~= ntrain
        error('illegal input size');
    end

    % allocate space for output
    predicted = zeros(ntest, 1);

    for k = 1:ntest
        % for each sample in the test set:
        %
        % - compute its squared euclidean distance to each sample in
        %   the train set, and store this in a vector
        %   squared_distances (which must have size ntrain x 1).
        %   For two vectors a=[a_1, a_2, ..., a_N] and b=[b_1, b_2, ..., b_N],
        %   the squared euclidean distance between a and b is:
        %       (a_1 - b_1)^2 + (a_2 - b_2)^2 + ... + (a_N - b_N)^2
        %
        % - assign the class label of the sample in the training set that has
        %   the smallest squared distance.
        %
        % compute difference to each sample in the training set
        delta = bsxfun(@minus, samples_train, samples_test(k, :));

        % compute distance (sqrt is unnecessary because monotonic)
        squared_distances = sum(delta.^2, 2);

        % the following code is equivalent to (but slower than) the code above:
        %   squared_distances=zeros(ntrain,1);
        %   for j=1:ntrain
        %       elementwise_delta=samples_train(j,:)-samples_test(j,:);
        %       squared_elementwise_delta=elementwise_delta.^2;
        %       squared_distance=sum(squared_elementwise_delta);
        %       squared_distances(j)=squared_distance;
        %   end

        [unused, i] = min(squared_distances);
        predicted(k) = targets_train(i);
    end