在Matlab中省去crossvalind [英] Leave one out crossvalind in Matlab

问题描述

我已经提取了用于男性和女性图片的HOG功能，现在，我正在尝试使用留一法" 对我的数据进行分类. 由于在Matlab中编写代码的标准方法是:

I have extracted HOG features for male and female pictures, now, I'm trying to use the Leave-one-out-method to classify my data. Due the standard way to write it in Matlab is:

[Train, Test] = crossvalind('LeaveMOut', N, M);

我应该写些什么而不是N和M? 另外，我应该在循环内还是循环外编写上述代码语句? 这是我的代码，其中有用于男性(80张图像)和女性(80张图像)的培训文件夹，以及用于测试的另一文件夹(10张随机图像).

What I should write instead of N and M? Also, should I write above code statement inside or outside a loop? this is my code, where I have training folder for Male (80 images) and female (80 images), and another one for testing (10 random images).

for i = 1:10  
 [Train, Test] = crossvalind('LeaveMOut', N, 1);
 SVMStruct = svmtrain(Training_Set (Train), train_label (Train));
 Gender = svmclassify(SVMStruct, Test_Set_MF (Test)); 
end 

注意:

• Training_Set:一个包含训练文件夹图像的HOG功能的数组.
• Test_Set_MF:一个包含测试文件夹图像的HOG功能的数组.
• N:训练文件夹中的图像总数.
• SVM应该检测哪些图像是男性，哪些图像是女性.

• Training_Set: an array consist of HOG features of training folder images.
• Test_Set_MF: an array consist of HOG features of test folder images.
• N: total number of images in training folder.
• SVM should detect which images are male and which are female.

推荐答案

我将重点介绍如何将crossvalind用于留一法制方法.

I will focus on how to use crossvalind for the leave-one-out-method.

我假设您想在循环内选择随机集. N是数据向量的长度. M是Test中随机选择的观测值的数量. M分别是Train中遗漏的观察数.这意味着您必须将N设置为训练集的长度.使用M，您可以指定要在Test-输出中分别指定多少个值，而在Train-输出中则要忽略的个数.

I assume you want to select random sets inside a loop. N is the length of your data vector. M is the number of randomly selected observations in Test. Respectively M is the number of observations left out in Train. This means you have to set N to the length of your training-set. With M you can specify how many values you want in your Test-output, respectively you want to left out in your Train-output.

这里是一个示例，从数据集中选择M=2个观测值.

Here is an example, selecting M=2 observations out of the dataset.

dataset = [1 2 3 4 5 6 7 8 9 10];
N = length(dataset);
M = 2;

for i = 1:5
    [Train, Test] = crossvalind('LeaveMOut', N, M);
    % do whatever you want with Train and Test
    dataset(Test) % display the test-entries
end

这将输出:(这是随机生成的，因此不会有相同的结果)

ans =
     1     9
ans =
     6     8
ans =
     7    10
ans =
     4     5
ans =
     4     7

---

根据

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As you have it in your code according to this post, you need to adjust it for a matrix of features:

Training_Set = rand(10,3);     % 10 samples with 3 features each

N = size(Training_Set,1);
M = 2;

for i = 1:5
    [Train, Test] = crossvalind('LeaveMOut', N, 2);
    Training_Set(Train,:) % displays the data to train
end

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