def transform(self, X, y=None):
        """Apply dimensionality reduction on X.

        X is projected on the first principal components previous extracted
        from a training set.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            New data, where n_samples in the number of samples
            and n_features is the number of features.

        Returns
        -------
        X_new : array-like, shape (n_samples, n_components)

        """
        check_is_fitted(self, 'mean_')

        X = check_array(X)
        if self.mean_ is not None:
            X = self.mean_ - X

        X = fast_dot(X, self.components_.T)
        return X
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/decomposition/pca.py/RandomizedPCA.transform/swapped operands of `-`