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How to run: cd /BR_SVM/python python3 run_svm_predict.py seg3_train.txt
note:
- This demo need 'pandas' to be installed, which is very hard to be installed on Raspberry.
- This BR_SVM/examples/feature_extraction.ipynb is used to Serialize batch training data
- This demo includes a trained model in BR_SVM/examples/seg3_train.txt/seg15_train.txt/seggroup_train.txt
- Please ingore the info 'Accuaracy = ..........'
more details in examples/...txt #training_testing_data_svm_acc_vel_timeseg3.txt is the all data #seg3_test.txt and seg3_train.txt is the original data
#seg3_test.txt.scale and seg3_train.txt.scale is the Scaled data cmd: svm-scale seg3_train.txt > seg3_train.txt.scale
#seg3_train.txt.range is the scale rule cdm: svm-scale -s train.range seg3_train.txt > seg3_train.txt.scale #使用train.range对test进行同样的缩放 svm-scale -r train.range seg3_test.txt > seg3_test.txt.scale
#seg3_train.txt.scale.out and seg3_train.txt.scale.png is the result of the grid
#seg3_train.txt.model cmd: svm-train.exe [options] training_set_file [model_file] 1.rho #决策函数中的常数项的相反数（-b） 2.svm的输出 y = y + model.sv_coef(i)*RBF(u,x);
#seg3_test.txt.predict
is the prediction for the seg3_test.txt.scale
cmd: svm-predict -b 1 test_file data_file.model output_file
#we can use the model information to build the DecisionFunction (created by seg3_train.txt.model)
%% DecisionFunction function plabel = DecisionFunction(x,model)
gamma = model.Parameters(4);
RBF = @(u,v)( exp(-gamma.*sum( (u-v).^2) ) );
len = length(model.sv_coef); y = 0;
for i = 1:len u = model.SVs(i,:); y = y + model.sv_coef(i)*RBF(u,x); end b = -model.rho; y = y + b;
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