@author Hua Ouyang @file README Support Vector Machines (Multiclass classification; Regression; Density estimation) Files in this folder: README: this file # Source codes: build.py: build scripts svm_main.cc: contains main function of SVM svm.h: contains classification/regression/density estimation main routines smo.h: sequential minimal optimization # Unit test data: c_traindata2.csv: training data for a 2 classes toy problem. 10 samples in each class. c_testdata2.csv: testing data for a 2 classes toy problem. 10 samples in each class. c_traindata3.csv: training data for a 3 classes toy problem. 10 samples in each class. c_testdata3.csv: testing data for a 3 classes toy problem. 10 samples in each class. r_traindata.csv: training data for regression, 100 samples of a 1-dim sinc function. r_testdata.csv: testing data for regression, 100 samples of a 1-dim sinc function. # Generated files: artificialdata.csv: artificial data if no training/testing data file provided svm_model: SVM model file predicted_values: predicted result Explanation of arguments: 0. learner_name (required): the name of the support vecotr learner, values: "svm_c" for classification, "svm_r" for regression, "svm_de" for density estimation 1. mode (required) : the mode of svm_main, values: "cv", "train", "train_test", "test". 2. k_cv (optional): the number of folds for cross validation, only required under "cv" mode. 3. cv_data (optional): file name for cross validation data, only required under "cv" mode. 4. train_data (optional): file name for training data, only required under "train" or "train_test" mode. 5. test_data (optional): file name for testing data, only required under "test" or "train_test" mode. 6. kernel (required): kernel name, values:"linear", "gaussian". 7. sigma (optional): sigma in the gaussian kernel k(x1,x2)=exp(-(x1-x2)^2/(2sigma^2)), only required when using "guassian" kernel. 8. c (for SVM_C,optional): the weight (0~1) that controls compromise between large margins and small margin violations. Default value: 10.0. 9. c_p (for SVM_C,optional): the weight (0~1) for the positive class (y==1). Default value: c. 10. c_n (for SVM_C,optional): the weight (0~1) for the negative class (y==-1). Default value: c. 11. epsilon (for SVM_R,optional): the epsilon in SVM regression. Default value: 0.1. 12. wss (optional): working set selection scheme. 1 for 1st order expansion; 2 for 2nd order expansion. Default value: 1. 13. normalize (optional): where need to do data normalization before training/testing, values: "0" for no normalize, "1" for normalize. Examples: --------------------------------------------------- SVM Classificaation ------------------------------------------------ a1.Support Vector Classification, Stratified cross validation mode svm_main --learner_name=svm_c --mode=cv --k_cv=4 --cv_data=c_traindata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_c --mode=cv --k_cv=4 --cv_data=c_traindata3.csv --kernel=linear --c=10 --normalize=0 a2.Support Vector Classification, Training mode (model will be saved as "svm_model") svm_main --learner_name=svm_c --mode=train --train_data=c_traindata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_c --mode=train --train_data=c_traindata3.csv --kernel=linear --c=10 --normalize=0 a3.Support Vector Classification, Training+testing mode svm_main --learner_name=svm_c --mode=train_test --train_data=c_traindata3.csv --test_data=c_testdata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_c --mode=train_test --train_data=c_traindata3.csv --test_data=c_testdata3.csv --kernel=linear --c=10 --normalize=0 a4.Support Vector Classification, Testing mode (the model file "svm_model" should exist) svm_main --learner_name=svm_c --mode=test --test_data=c_testdata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_c --mode=test --test_data=c_testdata3.csv --kernel=linear --c=10 --normalize=0 --------------------------------------------------- SVM Regression ------------------------------------------------------- b1.Support Vector Regression, Cross validation mode svm_main --learner_name=svm_r --mode=cv --k_cv=4 --cv_data=r_traindata.csv --kernel=gaussian --sigma=0.1 --epsilon=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_r --mode=cv --k_cv=4 --cv_data=r_traindata.csv --kernel=linear --epsilon=0.1 --c=10 --normalize=0 b2.Support Vector Regression, Training mode (model will be saved as "svm_model") svm_main --learner_name=svm_r --mode=train --train_data=r_traindata.csv --kernel=gaussian --sigma=0.1 --epsilon=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_r --mode=train --train_data=r_traindata.csv --kernel=linear --epsilon=0.1 --c=10 --normalize=0 b3.Support Vector Regression, Training+testing mode svm_main --learner_name=svm_r --mode=train_test --train_data=r_traindata.csv --test_data=r_testdata.csv --kernel=gaussian --sigma=0.1 --epsilon=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_r --mode=train_test --train_data=r_traindata.csv --test_data=r_testdata.csv --kernel=linear --epsilon=0.1 --c=10 --normalize=0 b4.Support Vector Regression, Testing mode (the model file "svm_model" should exist) svm_main --learner_name=svm_r --mode=test --test_data=r_testdata.csv --kernel=gaussian --sigma=0.1 --epsilon=0.1 --c=10 --normalize=0 svm_main --learner_name=svm_r --mode=test --test_data=r_testdata.csv --kernel=linear --epsilon=0.1 --c=10 --normalize=0 --------------------------------------------------- SVM Density Estimation -------------------------------------------------- c1... 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