libSVM应用举例

前面提到，很多人看到 libSVM这么多的参数，估计要犯晕了。没关系，我之前把相关的libSVM参数已经讲解了一遍，这里，再给出libSVM的用法。如果你不想花时间去仔细研究libSVM，完全可以参照我的函数来直接调用libSVM完成你的工作。

首先是训练SVM得到模型；假设，有10个训练样本，每个训练样本，有12个特征值，即：每个训练样本的维数是12，也就是说，训练样本构成了一个10*12的矩阵（当然，所有数据应该被归一化到[-1,1]或[0,1]范围内），另外，所有训练样本，应该有一个明确的类别标签，以此来表明当前样本所属的类别。所以，完整的训练样本，应该是10*13的矩阵，这里，我们给出一个10*13的矩阵，并认为，它就是训练样本。每个训练样本是个行向量，而该矩阵的第一列则代表训练样本的“标签”。即：第一个训练样本，类别标签为“1”，第二个训练样本类别标签为“-1”。第一个训练样本的第一个特征值为0.708333，第二个特征值为1，第三个特征值为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，我们给出一个待分类的输入向量，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，给出完整的SVM训练及预测程序：

#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;}

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

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