'Perform regression on samples in X.
For an one-class model, +1 (inlier) or -1 (outlier) 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,)'

def predict(self, X):

    X = self._validate_for_predict(X)
    predict = (self._sparse_predict if self._sparse else self._dense_predict)
    return predict(X)
