Functions
def	evaluations
def	svm_load_model
def	svm_predict
def	svm_read_problem
def	svm_save_model
def	svm_train

Function Documentation

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 48 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 27 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.

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 164 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 5 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 40 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
        1 -- nu-SVC
        2 -- one-class SVM
        3 -- epsilon-SVR
        4 -- nu-SVR
    -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 77 of file svmutil.py.

Functions

Function Documentation