TSVM

class s3l.classification.TSVM.TSVM(kernel='RBF', C1=100, C2=0.1, alpha=0.1, beta=-1, gamma=0)

Bases: s3l.base.InductiveEstimatorWOGraph

TSVM classifier

Parameters:

• kernel ({'Linear', 'RBF'} (default='RBF')) – String identifier for kernel function to use or the kernel function itself. Only ‘Linear’ and ‘RBF’ strings are valid inputs.
• C1 (float (default=100)) – Initial weight for labeled instances.
• C2 (float (default=0.1)) – Initial weight for unlabeled instances.
• alpha (float (default=0.1)) – Balance parameter
• beta (float (default=-1)) – Balance parameter
• gamma (float (default=0)) – Parameter for RBF kernel

Other Parameters:  

model (object) – Best model.

fit(X, y, labeled_idx)

Fit a semi-supervised SVM model

All the input data is provided matrix X (labeled and unlabeled) and corresponding label matrix y with a dedicated marker value for unlabeled samples.

Parameters:

• X (array-like, shape = [n_samples, n_features]) – A {n_samples by n_samples} size matrix will be created from this
• y (array_like, shape = [n_samples]) – n_labeled_samples (unlabeled points are marked as 0)
• labeled_idx (array_like, shape = [n_samples]) – index of n_labeled_samples in X.

Returns:

self

Return type:

returns an instance of self.

predict(X)

Performs inductive inference across the model.

Parameters:X (array_like, shape = [n_samples, n_features]) – Returns:y – Predictions for input data Return type:array_like, shape = [n_samples]

set_params(param)

Parameter setting function.

Parameters:param：dict – Store parameter names and corresponding values {‘name’: value}.