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我正在处理 SVM 和一类分类问题。数据是一个 nx3 矩阵，其中每一行都是一个样本，所以我在数据矩阵中有 n 个样本：

0.012873813, 0.094377473, 0.0043269233
0.020184161, 0.10070252,  0.0045584044
0.023954002, 0.10439565,  0.0045248871
0.024797738, 0.11338359,  0.0043057571
0.02122326,  0.106646,    0.0043315911
0.019649299, 0.10178889,  0.0043589743
0.01888592,  0.10269108,  0.0041237115
0.016681647, 0.10080954,  0.0042823157
0.033328395, 0.12347542,  0.0047008549
0.025292512, 0.11120763,  0.0049382718
0.028693195, 0.12776338,  0.0038888888
0.022229074, 0.10848146,  0.0042232275
0.022953529, 0.1088412,   0.0043237805
0.016452817, 0.096003316, 0.004687069
0.025636395, 0.12612548,  0.0039009422
0.02329725,  0.11335891,  0.0044992748
0.019382631, 0.10725249,  0.0045421249
0.026173679, 0.11711644,  0.0041491836

我为训练数据编写的代码如下：

cv::Ptr<cv::ml::SVM> model;
model = cv::ml::SVM::create();
model->setType(SVM::ONE_CLASS);
model->setC(5.00);
model->setKernel(SVM::RBF);
model->setGamma(.000020);
model->setNu(0.025);
model->setDegree(3);
model->setCoef0(0);
model->setP(0);

cv::Mat responses = cv::Mat::ones(samples.rows, 1, CV_32SC1); // Also tried with CV_32F
model->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, (int)1e7, 1e-6));
model->train(samples, cv::ml::ROW_SAMPLE, responses);

当我通过以下方式进行预测时：

model->predict(samples, responses);

它总是返回一个为零的 nx1 向量作为响应。

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