'Detects the soft boundary of the set of samples X.
Parameters
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Set of samples, where n_samples is the number of samples and
n_features is the number of features.
sample_weight : array-like, shape (n_samples,)
Per-sample weights. Rescale C per sample. Higher weights
force the classifier to put more emphasis on these points.
Returns
self : object
Returns self.
Notes
If X is not a C-ordered contiguous array it is copied.'

def fit(self, X, y=None, sample_weight=None, **params):

    super(OneClassSVM, self).fit(X, np.ones(_num_samples(X)), sample_weight=sample_weight, **params)
    return self
