前面提到，很多人看到 libSVM這么多的參數，估計要犯暈了。沒關系，我之前把相關的libSVM參數已經講解了一遍，這里，再給出libSVM的用法。如果你不想花時間去仔細研究libSVM，完全可以參照我的函數來直接調用libSVM完成你的工作。

? ? ? 首先是訓練SVM得到模型；假設，有10個訓練樣本，每個訓練樣本，有12個特征值，即：每個訓練樣本的維數是12，也就是說，訓練樣本構成了一個10*12的矩陣（當然，所有數據應該被歸一化到[-1,1]或[0,1]范圍內），另外，所有訓練樣本，應該有一個明確的類別標簽，以此來表明當前樣本所屬的類別。所以，完整的訓練樣本，應該是10*13的矩陣，這里，我們給出一個10*13的矩陣，并認為，它就是訓練樣本。每個訓練樣本是個行向量，而該矩陣的第一列則代表訓練樣本的“標簽”。即：第一個訓練樣本，類別標簽為“1”，第二個訓練樣本類別標簽為“-1”。第一個訓練樣本的第一個特征值為0.708333，第二個特征值為1，第三個特征值為1...

double inputArr[10][13] = {1,0.708333,1,1,-0.320755,-0.105023,-1,1,-0.419847,-1,-0.225806,0,1, -1,0.583333,-1,0.333333,-0.603774,1,-1,1,0.358779,-1,-0.483871,0,-1,1,0.166667,1,-0.333333,-0.433962,-0.383562,-1,-1,0.0687023,-1,-0.903226,-1,-1,-1,0.458333,1,1,-0.358491,-0.374429,-1,-1,-0.480916,1,-0.935484,0,-0.333333,-1,0.875,-1,-0.333333,-0.509434,-0.347032,-1,1,-0.236641,1,-0.935484,-1,-0.333333,-1,0.5,1,1,-0.509434,-0.767123,-1,-1,0.0534351,-1,-0.870968,-1,-1,1,0.125,1,0.333333,-0.320755,-0.406393,1,1,0.0839695,1,-0.806452,0,-0.333333,1,0.25,1,1,-0.698113,-0.484018,-1,1,0.0839695,1,-0.612903,0,-0.333333,1,0.291667,1,1,-0.132075,-0.237443,-1,1,0.51145,-1,-0.612903,0,0.333333,1,0.416667,-1,1,0.0566038,0.283105,-1,1,0.267176,-1,0.290323,0,1};

另外，我們給出一個待分類的輸入向量，并用行向量形式表示：

double testArr[]={0.25,1,1,-0.226415,-0.506849,-1,-1,0.374046,-1,-0.83871,0,-1};

下面，給出完整的SVM訓練及預測程序：

#include "stdafx.h"#include "svm.h"#include "iostream"#include "fstream"using namespace std; double inputArr[10][13] = {1,0.708333,1,1,-0.320755,-0.105023,-1,1,-0.419847,-1,-0.225806,0,1, -1,0.583333,-1,0.333333,-0.603774,1,-1,1,0.358779,-1,-0.483871,0,-1,1,0.166667,1,-0.333333,-0.433962,-0.383562,-1,-1,0.0687023,-1,-0.903226,-1,-1,-1,0.458333,1,1,-0.358491,-0.374429,-1,-1,-0.480916,1,-0.935484,0,-0.333333,-1,0.875,-1,-0.333333,-0.509434,-0.347032,-1,1,-0.236641,1,-0.935484,-1,-0.333333,-1,0.5,1,1,-0.509434,-0.767123,-1,-1,0.0534351,-1,-0.870968,-1,-1,1,0.125,1,0.333333,-0.320755,-0.406393,1,1,0.0839695,1,-0.806452,0,-0.333333,1,0.25,1,1,-0.698113,-0.484018,-1,1,0.0839695,1,-0.612903,0,-0.333333,1,0.291667,1,1,-0.132075,-0.237443,-1,1,0.51145,-1,-0.612903,0,0.333333,1,0.416667,-1,1,0.0566038,0.283105,-1,1,0.267176,-1,0.290323,0,1};double testArr[]={0.25,1,1,-0.226415,-0.506849,-1,-1,0.374046,-1,-0.83871,0,-1};void DefaultSvmParam(struct svm_parameter *param){param->svm_type = C_SVC;param->kernel_type = RBF;param->degree = 3;param->gamma = 0; // 1/num_featuresparam->coef0 = 0;param->nu = 0.5;param->cache_size = 100;param->C = 1;param->eps = 1e-3;param->p = 0.1;param->shrinking = 1;param->probability = 0;param->nr_weight = 0;param->weight_label = NULL;param->weight = NULL;}void SwitchForSvmParma(struct svm_parameter *param, char ch, char *strNum, int nr_fold, int cross_validation){switch(ch){case 's':{param->svm_type = atoi(strNum);break;}case 't':{param->kernel_type = atoi(strNum);break;}case 'd':{param->degree = atoi(strNum);break;}case 'g':{param->gamma = atof(strNum);break;}case 'r':{param->coef0 = atof(strNum);break;}case 'n':{param->nu = atof(strNum);break;}case 'm':{param->cache_size = atof(strNum);break;}case 'c':{param->C = atof(strNum);break;}case 'e':{param->eps = atof(strNum);break;}case 'p':{param->p = atof(strNum);break;}case 'h':{param->shrinking = atoi(strNum);break;}case 'b':{param->probability = atoi(strNum);break;}case 'q':{break;}case 'v':{cross_validation = 1;nr_fold = atoi(strNum);if (nr_fold < 2){cout<<"nr_fold should > 2!!! file: "<<__FILE__<<" function: ";cout<<__FUNCTION__<<" line: "<<__LINE__<<endl;}break;}case 'w':{++param->nr_weight;param->weight_label = (int *)realloc(param->weight_label,sizeof(int)*param->nr_weight);param->weight = (double *)realloc(param->weight,sizeof(double)*param->nr_weight);param->weight_label[param->nr_weight-1] = atoi(strNum);param->weight[param->nr_weight-1] = atof(strNum);break;}default:{break;}}}void SetSvmParam(struct svm_parameter *param, char *str, int cross_validation, int nr_fold){DefaultSvmParam(param);cross_validation = 0;char ch = ' ';int strSize = strlen(str);for (int i=0; i<strSize; i++){if (str[i] == '-'){ch = str[i+1];int length = 0;for (int j=i+3; j<strSize; j++){if (isdigit(str[j])){length++;}else{break;}}char *strNum = new char[length+1];int index = 0;for (int j=i+3; j<i+3+length; j++){strNum[index] = str[j];index++;}strNum[length] = '/0';SwitchForSvmParma(param, ch, strNum, nr_fold, cross_validation);delete strNum;}}}void SvmTraining(char *option){struct svm_parameter param; struct svm_problem prob; struct svm_model *model;struct svm_node *x_space;int cross_validation = 0;int nr_fold = 0;int sampleCount = 10;int featureDim = 12;prob.l = sampleCount;prob.y = new double[sampleCount];prob.x = new struct svm_node*[sampleCount];x_space = new struct svm_node[(featureDim+1)*sampleCount];SetSvmParam(?m, option, cross_validation, nr_fold);for (int i=0; i<sampleCount; i++){prob.y[i] = inputArr[i][0];}int j = 0;for (int i=0; i<sampleCount; i++){prob.x[i] = &x_space[j];for (int k=1; k<=featureDim; k++){x_space[i*featureDim+k].index = k;x_space[i*featureDim+k].value = inputArr[i][k];}x_space[(i+1)*featureDim].index = -1;j = (i+1)*featureDim + 1; }model = svm_train(&prob, ?m);const char* model_file_name = "C://Model.txt";svm_save_model(model_file_name, model);svm_destroy_model(model);svm_destroy_param(?m);delete[] prob.y;delete[] prob.x;delete[] x_space; }int SvmPredict(const char* modelAdd){struct svm_node *testX;struct svm_model* testModel;testModel = svm_load_model(modelAdd);int featureDim = 12;testX = new struct svm_node[featureDim+1];for (int i=0; i<featureDim; i++){testX[i].index = i+1;testX[i].value = testArr[i];}testX[featureDim].index = -1;double p = svm_predict(testModel, testX);svm_destroy_model(testModel);delete[] testX;if (p > 0.5){return 1;}else{return -1;}}int _tmain(int argc, _TCHAR* argv[]){SvmTraining("-c 100 -t 1 -g 4 -r 1 -d 4");int flag = SvmPredict("c://model.txt");cout<<"flag = "<<flag<<endl;system("pause");return 0;}

出處：http://blog.csdn.net/carson2005/article/details/6539218

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