libsvm(C++)始终输出相同的预测

Question

我已经为libsvm实现了一个OpenCV/C++包装器.在对SVM参数(RBF内核)进行网格搜索时,预测始终返回相同的标签.我创建了人工数据集,这些数据集具有非常容易分离的数据(并尝试预测我刚训练过的数据),但仍然会返回相同的标签.

我使用了libsvm的MATLAB实现,并在同一数据集上实现了高精度.我一定是在设置问题时做错了,但我已多次阅读README并且我找不到问题.

以下是我设置libsvm问题的方法,其中数据是OpenCV Mat:

    const int rowSize = data.rows;
    const int colSize = data.cols;

    this->_svmProblem = new svm_problem;
    std::memset(this->_svmProblem,0,sizeof(svm_problem));

    //dynamically allocate the X matrix...
    this->_svmProblem->x = new svm_node*[rowSize];
    for(int row = 0; row < rowSize; ++row)
        this->_svmProblem->x[row] = new svm_node[colSize + 1];

    //...and the y vector
    this->_svmProblem->y = new double[rowSize];
    this->_svmProblem->l = rowSize;

    for(int row = 0; row < rowSize; ++row)
    {
        for(int col = 0; col < colSize; ++col)
        {
            //set the index and the value. indexing starts at 1.
            this->_svmProblem->x[row][col].index = col + 1;
            double tempVal = (double)data.at<float>(row,col);
            this->_svmProblem->x[row][col].value = tempVal;
        }

        this->_svmProblem->x[row][colSize].index = -1;
        this->_svmProblem->x[row][colSize].value = 0;

        //add the label to the y array, and feature vector to X matrix
        double tempVal = (double)labels.at<float>(row);
        this->_svmProblem->y[row] = tempVal;
    }


}/*createProblem()*/

这是我如何设置参数,其中svmParams是我自己的C/Gamma结构,如下所示:

    this->_svmParameter = new svm_parameter;
    std::memset(this->_svmParameter,0,sizeof(svm_parameter));
    this->_svmParameter->svm_type = svmParams.svmType;
    this->_svmParameter->kernel_type = svmParams.kernalType;
    this->_svmParameter->C = svmParams.C;
    this->_svmParameter->gamma = svmParams.gamma;
    this->_svmParameter->nr_weight = 0;
    this->_svmParameter->eps = 0.001;
    this->_svmParameter->degree = 1;
    this->_svmParameter->shrinking = 0;
    this->_svmParameter->probability = 1;
    this->_svmParameter->cache_size = 100;  

我使用提供的param /问题检查功能,不返回任何错误.

然后我这样训练:

this->_svmModel = svm_train(this->_svmProblem, this->_svmParameter);

然后像这样预测:

float pred = (float)svm_predict(this->_svmModel, x[i]);

如果有人能指出我在哪里出错了,我会非常感激.谢谢!

编辑:

使用此代码我打印了问题的内容

for(int i = 0; i < rowSize; ++i)
    {
        std::cout << "[";
        for(int j = 0; j < colSize + 1; ++j)
        {
            std::cout << " (" << this->_svmProblem->x[i][j].index << ", " << this->_svmProblem->x[i][j].value << ")";
        }
        std::cout << "]" << " <" << this->_svmProblem->y[i] << ">" << std::endl;
    }

这是输出:

[ (1, -1) (2, 0) (-1, 0)] <1>
[ (1, -0.92394) (2, 0) (-1, 0)] <1>
[ (1, -0.7532) (2, 0) (-1, 0)] <1>
[ (1, -0.75977) (2, 0) (-1, 0)] <1>
[ (1, -0.75337) (2, 0) (-1, 0)] <1>
[ (1, -0.76299) (2, 0) (-1, 0)] <1>
[ (1, -0.76527) (2, 0) (-1, 0)] <1>
[ (1, -0.74631) (2, 0) (-1, 0)] <1>
[ (1, -0.85153) (2, 0) (-1, 0)] <1>
[ (1, -0.72436) (2, 0) (-1, 0)] <1>
[ (1, -0.76485) (2, 0) (-1, 0)] <1>
[ (1, -0.72936) (2, 0) (-1, 0)] <1>
[ (1, -0.94004) (2, 0) (-1, 0)] <1>
[ (1, -0.92756) (2, 0) (-1, 0)] <1>
[ (1, -0.9688) (2, 0) (-1, 0)] <1>
[ (1, 0.05193) (2, 0) (-1, 0)] <3>
[ (1, -0.048488) (2, 0) (-1, 0)] <3>
[ (1, 0.070436) (2, 0) (-1, 0)] <3>
[ (1, 0.15191) (2, 0) (-1, 0)] <3>
[ (1, -0.07331) (2, 0) (-1, 0)] <3>
[ (1, 0.019786) (2, 0) (-1, 0)] <3>
[ (1, -0.072793) (2, 0) (-1, 0)] <3>
[ (1, 0.16157) (2, 0) (-1, 0)] <3>
[ (1, -0.057188) (2, 0) (-1, 0)] <3>
[ (1, -0.11187) (2, 0) (-1, 0)] <3>
[ (1, 0.15886) (2, 0) (-1, 0)] <3>
[ (1, -0.0701) (2, 0) (-1, 0)] <3>
[ (1, -0.17816) (2, 0) (-1, 0)] <3>
[ (1, 0.12305) (2, 0) (-1, 0)] <3>
[ (1, 0.058615) (2, 0) (-1, 0)] <3>
[ (1, 0.80203) (2, 0) (-1, 0)] <1>
[ (1, 0.734) (2, 0) (-1, 0)] <1>
[ (1, 0.9072) (2, 0) (-1, 0)] <1>
[ (1, 0.88061) (2, 0) (-1, 0)] <1>
[ (1, 0.83903) (2, 0) (-1, 0)] <1>
[ (1, 0.86604) (2, 0) (-1, 0)] <1>
[ (1, 1) (2, 0) (-1, 0)] <1>
[ (1, 0.77988) (2, 0) (-1, 0)] <1>
[ (1, 0.8578) (2, 0) (-1, 0)] <1>
[ (1, 0.79559) (2, 0) (-1, 0)] <1>
[ (1, 0.99545) (2, 0) (-1, 0)] <1>
[ (1, 0.78376) (2, 0) (-1, 0)] <1>
[ (1, 0.72177) (2, 0) (-1, 0)] <1>
[ (1, 0.72619) (2, 0) (-1, 0)] <1>
[ (1, 0.80149) (2, 0) (-1, 0)] <1>
[ (1, 0.092327) (2, -1) (-1, 0)] <2>
[ (1, 0.019054) (2, -1) (-1, 0)] <2>
[ (1, 0.15287) (2, -1) (-1, 0)] <2>
[ (1, -0.1471) (2, -1) (-1, 0)] <2>
[ (1, -0.068182) (2, -1) (-1, 0)] <2>
[ (1, -0.094567) (2, -1) (-1, 0)] <2>
[ (1, -0.17071) (2, -1) (-1, 0)] <2>
[ (1, -0.16646) (2, -1) (-1, 0)] <2>
[ (1, -0.030421) (2, -1) (-1, 0)] <2>
[ (1, 0.094346) (2, -1) (-1, 0)] <2>
[ (1, -0.14408) (2, -1) (-1, 0)] <2>
[ (1, 0.090025) (2, -1) (-1, 0)] <2>
[ (1, 0.043706) (2, -1) (-1, 0)] <2>
[ (1, 0.15065) (2, -1) (-1, 0)] <2>
[ (1, -0.11751) (2, -1) (-1, 0)] <2>
[ (1, -0.02324) (2, 1) (-1, 0)] <2>
[ (1, 0.0080356) (2, 1) (-1, 0)] <2>
[ (1, -0.17752) (2, 1) (-1, 0)] <2>
[ (1, 0.011135) (2, 1) (-1, 0)] <2>
[ (1, -0.029063) (2, 1) (-1, 0)] <2>
[ (1, 0.15398) (2, 1) (-1, 0)] <2>
[ (1, 0.097746) (2, 1) (-1, 0)] <2>
[ (1, 0.01018) (2, 1) (-1, 0)] <2>
[ (1, 0.015592) (2, 1) (-1, 0)] <2>
[ (1, -0.062793) (2, 1) (-1, 0)] <2>
[ (1, 0.014444) (2, 1) (-1, 0)] <2>
[ (1, -0.1205) (2, 1) (-1, 0)] <2>
[ (1, -0.18011) (2, 1) (-1, 0)] <2>
[ (1, 0.010521) (2, 1) (-1, 0)] <2>
[ (1, 0.036914) (2, 1) (-1, 0)] <2>

这里,数据以[(index,value)...]标签的格式打印.

我创建的人工数据集只有3个类,所有这些类都可以通过非线性决策边界轻松分离.每行是特征向量(观察),具有2个特征(x coord,y coord).Libsvm要求用-1标签终止每个向量,所以我这样做.

EDIT2:

此编辑适用于我用于训练的C和Gamma值,以及数据缩放.我通常在0和1之间的数据(如这里建议:http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf).我也将扩展这个假数据集并再次尝试,尽管我使用与libsvm的MATLAB实现相同的精确数据集,它可以100%准确地分离这个未缩放的数据.

对于C和Gamma,我也使用指南中建议的值.我创建了两个向量并使用双嵌套循环来尝试所有组合:

std::vector<double> CList, GList;
double baseNum = 2.0;
for(double j = -5; j <= 15; j += 2) //-5 and 15
    CList.push_back(pow(baseNum,j));
for(double j = -15; j <= 3; j += 2) //-15 and 3
    GList.push_back(pow(baseNum,j));

循环看起来像:

    for(auto CIt = CList.begin(); CIt != CList.end(); ++CIt) //for all C's
    {
        double C = *CIt;
        for(auto GIt = GList.begin(); GIt != GList.end(); ++GIt) //for all gamma's
        {
            double gamma = *GIt;
            svmParams.svmType = C_SVC;
            svmParams.kernalType = RBF;
            svmParams.C = C;
            svmParams.gamma = gamma;

        ......training code etc..........

EDIT3:

由于我不断参考MATLAB,我将展示精度差异.这是libsvm产量精度的热图:

这里是MATLAB使用相同参数和相同C/Gamma网格产生的精度图:

以下是用于生成C/Gamma列表的代码,以及我如何训练:

CList = 2.^(-15:2:15);%(-5:2:15);
GList = 2.^(-15:2:15);%(-15:2:3);
cmd = ['-q -s 0 -t 2 -c ', num2str(C), ' -g ', num2str(gamma)];
model = ovrtrain(yTrain,xTrain,cmd);

EDIT4

作为一个完整性检查,我重新格式化了我的假缩放数据集,以符合libsvm的Unix/Linux终端API使用的数据集.我使用MATLAB精度图中的C/Gamma进行训练和预测.预测准确率为100%.因此,我在C++实现中绝对做错了.

EDIT5

我将从Linux终端培训的模型加载到我的C++包装器类中.然后我尝试预测用于训练的相同的精确数据集.C++的准确性仍然很糟糕!但是,我非常接近缩小问题的根源.如果MATLAB/Linux在100%准确度方面都达成一致,那么它所生成的模型已经被证明可以在训练过的同一数据集上产生100%的准确度,现在我的C++包装类在验证模型中表现不佳. ..有三种可能的情况:

1. 我用来将cv :: Mats转换为预测所需的svm_node*的方法存在问题.
2. 我用来预测标签的方法存在问题.
3. 2和3两个!

现在真正检查的代码是我如何创建svm_node.这又是:

svm_node** LibSVM::createNode(INPUT const cv::Mat& data)
{
    const int rowSize = data.rows;
    const int colSize = data.cols;

    //dynamically allocate the X matrix...
    svm_node** x = new svm_node*[rowSize];
    if(x == NULL)
        throw MLInterfaceException("Could not allocate SVM Node Array.");

    for(int row = 0; row < rowSize; ++row)
    {
        x[row] = new svm_node[colSize + 1]; //+1 here for the index-terminating -1
        if(x[row] == NULL)
            throw MLInterfaceException("Could not allocate SVM Node.");
    }

    for(int row = 0; row < rowSize; ++row)
    {
        for(int col = 0; col < colSize; ++col)
        {
            double tempVal = data.at<double>(row,col);
            x[row][col].value = tempVal;
        }

        x[row][colSize].index = -1;
        x[row][colSize].value = 0;
    }

    return x;
} /*createNode()*/

和预测:

cv::Mat LibSVM::predict(INPUT const cv::Mat& data)
{
    if(this->_svmModel == NULL)
        throw MLInterfaceException("Cannot predict; no model has been trained or loaded.");

    cv::Mat predMat;

    //create the libsvm representation of data
    svm_node** x = this->createNode(data);

    //perform prediction for each feature vector
    for(int i = 0; i < data.rows; ++i)
    {
        double pred = svm_predict(this->_svmModel, x[i]);
        predMat.push_back<double>(pred);
    }        

    //delete all rows and columns of x
    for(int i = 0; i < data.rows; ++i)
        delete[] x[i];
    delete[] x;


    return predMat;
}

EDIT6:

对于那些在家里进行调音的人,我在C++中训练了一个模型(使用MATLAB中的最佳C/Gamma),将其保存到文件中,然后尝试通过Linux终端预测训练数据.它得分为100%.我的预测出了点问题.O_0

EDIT7:

我终于找到了这个问题.我找到了巨大的错误跟踪帮助.我打印了用于预测的svm_node**2D数组的内容.它是createProblem()方法的子集.有一部分我没有复制+粘贴到新功能.它是给定特征的索引; 从来没有写过.应该还有1行:

x[row][col].index = col + 1; //indexing starts at 1

现在预测工作正常.

Answer 1

查看您的伽马值会很有用，因为您的数据没有标准化，这会产生巨大的差异。

libsvm 中的 gamma 与超球面半径成反比，因此如果这些球体相对于输入范围太小，则所有内容都将始终被激活，然后模型将始终输出相同的值。

因此，两个建议是 1) 将输入值缩放到范围 [-1,1]。2) 调整伽玛值。