k最近邻：误差使用-矩阵维数必须符合[MATLAB]？

在执行下面的代码后，我尝试实现用于图像分类的k-最近邻分类器，我得到了以下错误：

使用- 矩阵尺寸必须一致

Sevenn_分类器错误（第19行） tmp=测试点-训练点（1:end-1，：）

认为：

size(test_point) = 1       10000
size(train-point)=  1       60000

这是我的密码：

% load training set and testing set
clear all;
train_set = loadMNISTImages('/home/anelmad/Desktop/TER/mnist_ml2/MNIST_digit_recognition-master/load_data/train-images.idx3-ubyte');
train_label = loadMNISTLabels('/home/anelmad/Desktop/TER/mnist_ml2/MNIST_digit_recognition-master/load_data/train-labels.idx1-ubyte');
test_set = loadMNISTImages('/home/anelmad/Desktop/TER/mnist_ml2/MNIST_digit_recognition-master/load_data/t10k-images.idx3-ubyte');
test_label = loadMNISTLabels('/home/anelmad/Desktop/TER/mnist_ml2/MNIST_digit_recognition-master/load_data/t10k-labels.idx1-ubyte');

% cliassify the testing set 
train_scale = size(train_set);
test_scale = size(test_set);
test_classify_label = zeros(test_scale(1),1);
tic;
for i=1:test_scale(1)
    test_point = test_set(i,:);
    dist = zeros(train_scale(1),1);
    for j=1:train_scale(1)
        % calculate the distance between test point i and train point j
        train_point = train_set(j, :);
        tmp = test_point-train_point;
    dist(j) = sqrt(sum(tmp.*tmp));
    end

    % find the 5-nearest neighbor 
    dist_tmp = sort(dist);
    num = zeros(10, 1);
    for k=1:7
       idx = find(dist==dist_tmp(k));
       num(train_label(idx)+1) = num(train_label(idx)+1)+1;
    end

    % classification
    maxIdx = 0;
    maxNum = -1;
    for k=1:10
      if(num(k)>maxNum)
         maxIdx = k;
         maxNum = num(k);
      end
    end 
    test_classify_label(i) = maxIdx-1;
end
t1 = toc;
% calculate accuracy
num_correct = sum(test_label==test_classify_label);
accuracy = num_correct / test_scale(1);
save -mat 7nn_time.mat t1
save -mat 7nn_accuracy.mat accuracy

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因此，19号线为

tmp=测试点-列车点，对吗？
测试点
和
训练点
的尺寸是多少。显然，减法的尺寸必须一致。到目前为止，您尝试了什么？如果您有1x10000和1x60000，您认为应该如何解决？那么我必须将trainpoint设置为trainpoint=（1:size（test_point））？您是否忘记替换复制代码中的行？尺寸必须匹配。