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我正在尝试使用函数 trainAuto 交叉验证我的训练数据，但出现错误::: OpenCV Error: Bad argument (虽然交叉验证一个或多个类已从样本中剔除。尝试减少)做火车。

当我将 trainAuto 功能更改为仅进行训练时，它可以工作，但我需要 trainauto。我需要验证我拥有的每个数据点，即 70 个点。我想要做的是交叉验证我的 trainingData 数组中的每个点。我所有的数据点和标签都存储在我的标签[70] 和 trainingData[70][2744] 数组中。当试图交叉验证我的训练数据时，我复制了我的 trainingData[70][2744] 中的每一行，除了应该测试的那一行，那一行被复制到 testData 数组中。这应该做 70 次，所以要进行 70 次测试。这是我的代码，任何帮助将不胜感激。

//my labels and points are stored here
int labels[70] = {};
float trainingData[70][2744] = {};

//this is where the copy of my test, labels and training data should end up for the cross validation
int labelstrain[69] = {};
float trainingDatatrain[69][2744] = {};
float testData[2744] = {};

//looping through everything and extracting test point, labels for the points and training data
for(int o = 0; o < 70; ++o){
     memcpy(testData, trainingData[o], sizeof(testData)); 

    for(int p = 0; p < 70; p++){

        if(o != p){
            labelstrain[p] = labels[p];
            copy(trainingData[p], trainingData[p] + 2744, trainingDatatrain[p]);
        }
        else{
            cout << "label that was left out: " <<labels[p]<< endl; 
        }

}

    //creating matrix of my labels, test point and training data
    Mat labelsMat(69, 1, CV_32SC1, labelstrain);
    Mat trainingDataMat(69, 2744, CV_32FC1, trainingDatatrain);
    Mat testDataMat(1, 2744, CV_32FC1, testData);

    //here is my problem where i get a error. Only train here works fine
    svmLin->trainAuto(ml::TrainData::create(trainingDataMat, ml::ROW_SAMPLE, labelsMat));
    svmPoly->trainAuto(ml::TrainData::create(trainingDataMat, ml::ROW_SAMPLE, labelsMat));
    svmRbf->trainAuto(ml::TrainData::create(trainingDataMat, ml::ROW_SAMPLE, labelsMat));
    svmSig->trainAuto(ml::TrainData::create(trainingDataMat, ml::ROW_SAMPLE, labelsMat));
    svmChi2->trainAuto(ml::TrainData::create(trainingDataMat, ml::ROW_SAMPLE, labelsMat));
    svmInter->trainAuto(ml::TrainData::create(trainingDataMat, ml::ROW_SAMPLE, labelsMat));

    //predicting on my point that is not in my trainingdata
    float predictLin = svmLin->predict(testDataMat);
    float predictPoly = svmPoly->predict(testDataMat);
    float predictRbf = svmRbf->predict(testDataMat);
    float predictSig = svmSig->predict(testDataMat);
    float predictChi2 = svmChi2->predict(testDataMat);
    float predictInter = svmInter->predict(testDataMat);
}
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