51 lines
2.7 KiB
Plaintext
51 lines
2.7 KiB
Plaintext
@author Hua Ouyang
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@file README
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Multiclass SVM Classifier
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Files in this folder:
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README: this file
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# Source codes:
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build.py: build scripts
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svm_main.cc: contains main function of multilcass SVM classifier
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svm.h: contains multiclassification main routine
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smo.h: sequential minimal optimization
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# Unit test data:
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traindata2.csv: training data for a 2 classes toy problem. 10 samples in each class.
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testdata2.csv: testing data for a 2 classes toy problem. 10 samples in each class.
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traindata3.csv: training data for a 3 classes toy problem. 10 samples in each class.
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testdata3.csv: testing data for a 3 classes toy problem. 10 samples in each class.
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# Generated files:
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artificialdata.csv: artificial data if no training/testing data file provided
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svm_model: SVM model file
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testlabels: classified labels
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Explanation of arguments:
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1. mode (required) : the mode of svm_main, values: "cv", "train", "train_test", "test".
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2. k_cv (optional): the number of folds for cross validation, only required under "cv" mode.
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3. cv_data (optional): file name for cross validation data, only required under "cv" mode.
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4. train_data (optional): file name for training data, only required under "train" or "train_test" mode.
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5. test_data (optional): file name for testing data, only required under "test" or "train_test" mode.
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6. kernel (required): kernel name, values:"linear", "gaussian".
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7. sigma (optional): sigma in the gaussian kernel k(x1,x2)=exp(-(x1-x2)^2/(2sigma^2)), only required when using "guassian" kernel.
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8. c (required): the weight that controls compromise between large margins and small margin violations.
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9. normalize (optional): where need to do data normalization before training/testing, values: "0" for no normalize, "1" for normalize.
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Examples:
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1.Cross validation mode
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svm_main --mode=cv --k_cv=4 --cv_data=traindata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0
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svm_main --mode=cv --k_cv=4 --cv_data=traindata3.csv --kernel=linear --c=10 --normalize=0
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2.Training mode (model will be saved as "svm_model")
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svm_main --mode=train --train_data=traindata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0
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svm_main --mode=train --train_data=traindata3.csv --kernel=linear --c=10 --normalize=0
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3.Training+testing mode
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svm_main --mode=train_test --train_data=traindata3.csv --test_data=testdata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0
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svm_main --mode=train_test --train_data=traindata3.csv --test_data=testdata3.csv --kernel=linear --c=10 --normalize=0
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4.Testing mode (the model file "svm_model" should exist)
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svm_main --mode=test --test_data=testdata3.csv --kernel=gaussian --sigma=0.1 --c=10 --normalize=0
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svm_main --mode=test --test_data=testdata3.csv --kernel=linear --c=10 --normalize=0
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