MATLAB: 10 fold cross Validation without using existing functions

Question

I have a matrix (I guess in MatLab you call it a struct) or data structure:

  data: [150x4 double]
labels: [150x1 double]

here is out my matrix.data looks like assume I do load my file with the name of matrix:

5.1000    3.5000    1.4000    0.2000
4.9000    3.0000    1.4000    0.2000
4.7000    3.2000    1.3000    0.2000
4.6000    3.1000    1.5000    0.2000
5.0000    3.6000    1.4000    0.2000
5.4000    3.9000    1.7000    0.4000
4.6000    3.4000    1.4000    0.3000
5.0000    3.4000    1.5000    0.2000
4.4000    2.9000    1.4000    0.2000
4.9000    3.1000    1.5000    0.1000
5.4000    3.7000    1.5000    0.2000
4.8000    3.4000    1.6000    0.2000
4.8000    3.0000    1.4000    0.1000
4.3000    3.0000    1.1000    0.1000
5.8000    4.0000    1.2000    0.2000
5.7000    4.4000    1.5000    0.4000
5.4000    3.9000    1.3000    0.4000
5.1000    3.5000    1.4000    0.3000
5.7000    3.8000    1.7000    0.3000
5.1000    3.8000    1.5000    0.3000

And here is my matrix.labels look like

 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1

I am trying to create 10 cross fold validation without using any of the existing functions in MatLab and due to my very limited MatLab knowledge I am having trouble going forward with from what I have. Any help would be great.

This is what I have so far, and I am sure this probably not the matlab way, but I am very new to matlab.

function[output] = fisher(dataFile, number_of_folds)
    data = load(dataFile);
    %create random permutation indx
    idx = randperm(150);
    output = data.data(idx(1:15),:);
end

Answer 1

Here is my take for this cross validation. I create dummy data using magic(10) also I create labels randomly. Idea is following , we get our data and labels and combine them with random column. Consider following dummy code.

>> data = magic(4)

data =

    16     2     3    13
     5    11    10     8
     9     7     6    12
     4    14    15     1

>> dataRowNumber = size(data,1)

dataRowNumber =

     4

>> randomColumn = rand(dataRowNumber,1)

randomColumn =

    0.8147
    0.9058
    0.1270
    0.9134


>> X = [ randomColumn data]

X =

    0.8147   16.0000    2.0000    3.0000   13.0000
    0.9058    5.0000   11.0000   10.0000    8.0000
    0.1270    9.0000    7.0000    6.0000   12.0000
    0.9134    4.0000   14.0000   15.0000    1.0000

If we sort X according column 1, we sort our data randomly. This will give us cross validation randomness. Then next thing is to divide X according to cross validation percentage. Accomplishing this for one case easy enough. Lets consider %75 percent is train case and %25 percent is test case. Our size here is 4, then 3/4 = %75 and 1/4 is %25.

testDataset = X(1,:)
trainDataset = X(2:4,:)

But accomplishing this a bit harder for N cross folds. Since we need to make this N times. For loop is necessary for this. For 5 cross folds. I get , in first f

1. 1st fold : 1 2 for test, 3:10 for train
2. 2nd fold : 3 4 for test, 1 2 5:10 for train
3. 3rd fold : 5 6 for test, 1:4 7:10 for train
4. 4th fold : 7 8 for test, 1:6 9:10 for train
5. 5th fold : 9 10 for test, 1:8 for train

Following code is an example for this process:

data = magic(10);
dataRowNumber = size(data,1);
labels= rand(dataRowNumber,1) > 0.5;
randomColumn = rand(dataRowNumber,1);

X = [ randomColumn data labels];


SortedData = sort(X,1);

crossValidationFolds = 5;
numberOfRowsPerFold = dataRowNumber / crossValidationFolds;

crossValidationTrainData = [];
crossValidationTestData = [];
for startOfRow = 1:numberOfRowsPerFold:dataRowNumber
    testRows = startOfRow:startOfRow+numberOfRowsPerFold-1;
    if (startOfRow == 1)
        trainRows = [max(testRows)+1:dataRowNumber];
        else
        trainRows = [1:startOfRow-1 max(testRows)+1:dataRowNumber];
    end
    crossValidationTrainData = [crossValidationTrainData ; SortedData(trainRows ,:)];
    crossValidationTestData = [crossValidationTestData ;SortedData(testRows ,:)];

end