Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin All rights reserved.
Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin All rights reserved.
Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and predicted values (pv).
Read SVM model in PICKLE format
Parameters: | svmFileNale – name of the file to read from |
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Save SVM weights and biais in PICKLE format
Parameters: |
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Load a LIBSVM model from model_file_name and return.
Predict data (y, x) with the SVM model m. options:
- -b probability_estimates: whether to predict probability estimates,
0 or 1 (default 0); for one-class SVM only 0 is supported.
-q : quiet mode (no outputs).
The return tuple contains
- p_labels: a list of predicted labels
- p_acc: a tuple including accuracy (for classification), mean-squared error, and squared correlation coefficient (for regression).
- p_vals: a list of decision values or probability estimates (if ‘-b 1’ is specified). If k is the number of classes, for decision values, each element includes results of predicting k(k-1)/2 binary-class SVMs. For probabilities, each element contains k values indicating the probability that the testing instance is in each class.
Note
that the order of classes here is the same as ‘model.label’ field in the model structure.
Read LIBSVM-format data from data_file_name and return labels y and data instances x.
Save a LIBSVM model to the file model_file_name.
svm_train(prob [, options]) -> model | ACC | MSE svm_train(prob, param) -> model | ACC| MSE
Train an SVM model from data (y, x) or an svm_problem prob using ‘options’ or an svm_parameter param. If ‘-v’ is specified in ‘options’ (i.e., cross validation) either accuracy (ACC) or mean-squared error (MSE) is returned. options:
-s svm_type : set type of SVM (default 0)
- 0 – C-SVC (multi-class classification)
- 1 – nu-SVC (multi-class classification)
- 2 – one-class SVM
- 3 – epsilon-SVR (regression)
- 4 – nu-SVR (regression)
-t kernel_type : set type of kernel function (default 2)
- 0 – linear: u’*v
- 1 – polynomial: (gamma*u’*v + coef0)^degree
- 2 – radial basis function: exp(-gamma*|u-v|^2)
- 3 – sigmoid: tanh(gamma*u’*v + coef0)
- 4 – precomputed kernel (kernel values in training_set_file)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)