double predict_label_val = svm_predict_probability(model, d, prob_estimates); /* 发现此处只在prob_estimates!=NULL时才输出概率? */ if (prob_estimates != NULL) fprintf(output_file, “%lg”, prob_estimates[getPredictVal(predict_label_val)]); fprintf(output_file, ” %lg\n”, predict_lab...
int probability; /* do probability estimates */ 1 do,0 not }; 2)svm_predict_probability函数与svm_predict函数在接口上只有第三个参数prob_estimates不同,其余两个都相同,所以这里只说明第三个参数的作用。prob_estimates里实际上存放的就是估计出的概率,比如说m分类问题,那prob_estimates就是一个1*m列的...
public static double svm_get_svr_probability(svm_model model); public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values); public static double svm_predict(svm_model model, svm_node[] x); public static double svm_predict_probability(svm_model model, svm_...
> svm-train -s0-c100-g0.1-v5data_fileDofive-fold cross validationforthe classifierusingthe parameters C =100andgamma =0.1> svm-train -s0-b1data_file > svm-predict -b1test_file data_file.model output_file Obtain a modelwithprobability informationandpredict test datawithprobability estimates Prec...
./svm-predict svmguide1.t svmguide1.txt.model svmguide1.t.predict Accuracy = 66.925% (2677/4000) (classification) 采用默认参数,并且不进行scale,得到的accuracy=66% ☘️☘️☘️3.4 scale+默认参数进行训练预测 ☘️首先对训练集进行scale,并且将scale的结果存入到svmguide1.scale, scale参...
intprobability;//等于1代表模型的分布概率已知 }; 该结构体定义了libSVM中的用到的SVM参数。其中svm_type可以是C_SVC,NU_SVC,ONE_CLASS,EPSILON_SVR,NU_SVR中的任意一种,代表着SVM的类型; C_SVC: C-SVMclassification NU_SVC: nu-SVMclassification ONE_CLASS: one-class-SVM EPSILON_SVR: epsilon-SVMregre...
libsvm_options: -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet -q : quiet mode (no outputs) Returns: predicted_label: SVM prediction output vector. accuracy: a vector with accuracy, mean squared error, squared correlati...
if(predict_probability) { if (svm_type==NU_SVR || svm_type==EPSILON_SVR) info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model)); else { int *labels=(int *) malloc(...
LIBSVM 主要包括三个部分:svm-train、svm-predict 和 svm-plot。svm-train 用于训练 SVM 模型,svm-predict 用于预测新数据,svm-plot 用于绘制各种图表,以便于观察和分析模型性能。 3.LIBSVM 参数说明 LIBSVM 的参数设置对于模型的性能至关重要。以下是一些常用的参数及其说明: - -train:用于指定训练数据的文件名...
int probability;//等于1代表模型的分布概率已知 }; 该结构体定义了libSVM中的用到的SVM參数。当中svm_type能够是C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR中的随意一种,代表着SVM的类型; C_SVC: C-SVM classification NU_SVC: nu-SVM classification ...