'Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
Parameters
X : {array-like, sparse matrix}, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
[n_samples_test, n_samples_train]
Returns
y_pred : array, shape (n_samples,)
Class labels for samples in X.'

def predict(self, X):

    y = super(OneClassSVM, self).predict(X)
    return np.asarray(y, dtype=np.intp)
