MATLAB: Using a 2 dimensional feature

doublehomeworkMATLAB

I am current student and new to Matlab. For an assignment I was told that I was not using the 2 dimensional feature for a .m file that I loaded. How do I do this and how do I figure it out? Where are my mistakes?
partTwoData is a 10000×2 double.
My variables made are coming up as x1
2019-01-18_21-35-40.png
load ('partTwoData.mat');
numberOfClassOneSamples = size(classOne, 1);
numberOfClassTwoSamples = size(classTwo, 1);
%Generate a random partition of the data, 
randomizedIndices = randsample(numberOfClassOneSamples, numberOfClassOneSamples);
%splitting each of the classes into 60% training and 40%    
classOneTrainingIndices = randomizedIndices(1:6000);
classOneTestIndices= randomizedIndices(6001:10000); 
%for classTwo
classTwoTrainingIndices = randomizedIndices(1:6000);
classTwoTestIndices= randomizedIndices(6001:10000); 
classOneTrainingData = classOne(classOneTrainingIndices);
classOneTestData = classOne(classOneTestIndices); 
classTwoTrainingData = classTwo(classTwoTrainingIndices);
classTwoTestData = classTwo(classTwoTestIndices);
numberOfTestSamples = 4000; 
numberOfTrainingSamples = 6000;
%class one test samples
for i=1:numberOfTestSamples
    for j=1:numberOfTrainingSamples
        euclideanDistanceClassOne(j) = sqrt ( (classOneTestData(i) - classOneTrainingData(j))^2);
    end
    
    for j=1 : numberOfTrainingSamples
        euclideanDistanceClassTwo(j) = sqrt( (classOneTestData(i) - classTwoTrainingData(j))^2);
    end
    
    if(min(euclideanDistanceClassOne) < min(euclideanDistanceClassTwo))
        %classify as class one

        predict(i) = 1;
    else
        predict(i) = 2;
    end
    
end
correctClassOne = sum(predict==1);
incorrectClassOne = sum(predict==2);
%Class Two Test Samples, still j, because we are stepping through the training sample. 
for i=1:numberOfTestSamples 
    for j=1:numberOfTrainingSamples
         euclideanDistanceClassOne(j) = sqrt((classTwoTestData(i) - classOneTrainingData(j))^2);
    end 
    
    for j=1:numberOfTrainingSamples
       euclideanDistanceClassTwo(j) = sqrt((classTwoTestData(i) - classTwoTrainingData(j))^2);
    end 
    
    if (min(euclideanDistanceClassOne) < min(euclideanDistanceClassTwo))
        %classify as class one
        predict(i)=1; 
    else 
        %classify as class two
        predict(i)=2;
    end 
end 
correctClassTwo = sum(predict == 2);
incorrectClassTwo = sum(predict == 1);
fprintf('Part Two:');
fprintf('Class One Correct Predictions = %d\n', correctClassOne);
fprintf('Class Two Correct Predictions = %d\n', correctClassTwo);
fprintf('Class One Incorrect Predictions = %d\n', incorrectClassOne);
fprintf('Class Two Incorrect Predictions = %d\n', incorrectClassTwo);
fprintf('Total Class One Incorrect Predictions = %d\n', correctClassOne + correctClassTwo);
fprintf('Total Class Two Incorrect Predictions = %d\n', incorrectClassOne + incorrectClassTwo);
fprintf('Total Prediction Accuracy = %f\n', (correctClassOne + correctClassTwo) / (2 * numberOfTestSamples));

Best Answer

Every place that you index classTwoTestData(some_index) you need to change it to classTwoTestData(some_index,:) and likewise for similar variables.
You also need to adjust your euclidean distance calculations. A(index,:) - B(index,:) will be a vector, so you cannot use ^2 for it: you need to use .^2 . You also need to take the sum of the squared components.
Related Question