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 ma

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