@author Hua Ouyang @file README Multiclass SVM Classifier Files in this folder: README: this file # Source codes: build.py: build scripts svm_main.cc: contains main function of multilcass SVM classifier svm.h: contains multiclassification main routine smo.h: sequential minimal optimization # Unit test data: traindata2.csv: training data for a 2 classes toy problem. 10 samples in each class. testdata2.csv: testing data for a 2 classes toy problem. 10 samples in each class. traindata3.csv: training data for a 3 classes toy problem. 10 samples in each class. testdata3.csv: testing data for a 3 classes toy problem. 10 samples in each class. # Generated files: artificialdata.csv: artificial data if no training/testing data file provided svm_model: SVM model file testlabels: classified labels Explanation of arguments: 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 (required): the weight that controls compromise between large margins and small margin violations. 9. normalize (optional): where need to do data normalization before training/testing, values: "0" for no normalize, "1" for normalize. Examples: 1.Cross validation mode svm_main --mode=cv --k_cv=4 --cv_data=traindata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --mode=cv --k_cv=4 --cv_data=traindata3.csv --kernel=linear --c=10 --normalize=0 2.Training mode (model will be saved as "svm_model") svm_main --mode=train --train_data=traindata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --mode=train --train_data=traindata3.csv --kernel=linear --c=10 --normalize=0 3.Training+testing mode svm_main --mode=train_test --train_data=traindata3.csv --test_data=testdata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --mode=train_test --train_data=traindata3.csv --test_data=testdata3.csv --kernel=linear --c=10 --normalize=0 4.Testing mode (the model file "svm_model" should exist) svm_main --mode=test --test_data=testdata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0 svm_main --mode=test --test_data=testdata3.csv --kernel=linear --c=10 --normalize=0