Functions | |
| def | evaluations |
| def | svm_load_model |
| def | svm_predict |
| def | svm_read_problem |
| def | svm_save_model |
| def | svm_train |
Variables | |
| list | __all__ |
| def svmutil.evaluations | ( | ty, | |
| pv | |||
| ) |
evaluations(ty, pv) -> (ACC, MSE, SCC) Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and predicted values (pv).
Definition at line 57 of file svmutil.py.
| def svmutil.svm_load_model | ( | model_file_name | ) |
svm_load_model(model_file_name) -> model Load a LIBSVM model from model_file_name and return.
Definition at line 36 of file svmutil.py.
| def svmutil.svm_predict | ( | y, | |
| x, | |||
| m, | |||
options = "" |
|||
| ) |
svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)
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.
Definition at line 173 of file svmutil.py.
| def svmutil.svm_read_problem | ( | data_file_name | ) |
svm_read_problem(data_file_name) -> [y, x] Read LIBSVM-format data from data_file_name and return labels y and data instances x.
Definition at line 14 of file svmutil.py.
| def svmutil.svm_save_model | ( | model_file_name, | |
| model | |||
| ) |
svm_save_model(model_file_name, model) -> None Save a LIBSVM model to the file model_file_name.
Definition at line 49 of file svmutil.py.
| def svmutil.svm_train | ( | arg1, | |
arg2 = None, |
|||
arg3 = None |
|||
| ) |
svm_train(y, x [, options]) -> model | ACC | MSE
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)
Definition at line 86 of file svmutil.py.
| list svmutil::__all__ |
00001 ['evaluations', 'svm_load_model', 'svm_predict', 'svm_read_problem', 00002 'svm_save_model', 'svm_train']
Definition at line 9 of file svmutil.py.