def _fit_full(self, X, n_components):
        """Fit the model by computing full SVD on X"""
        n_samples, n_features = X.shape

        if n_components == 'mle':
            if n_samples < n_features:
                raise ValueError("n_components='mle' is only supported "
                                 "if n_samples >= n_features")
        elif not 0 <= n_components <= n_features:
            raise ValueError("n_components=%r must be between 0 and "
                             "n_features=%r with svd_solver='full'"
                             % (n_components, n_features))

        # Center data
        self.mean_ = np.mean(X, axis=0)
        X -= self.mean_

        U, S, V = linalg.svd(X, full_matrices=False)
        # flip eigenvectors' sign to enforce deterministic output
        U, V = svd_flip(U, V)

        components_ = V

        # Get variance explained by singular values
        explained_variance_ = (S ** 2) / n_samples
        total_var = explained_variance_.sum()
        explained_variance_ratio_ = explained_variance_ / total_var

        # Postprocess the number of components required
        if n_components == 'mle':
            n_components = \
                _infer_dimension_(explained_variance_, n_samples, n_features)
        elif 0 < n_components < 1.0:
            # number of components for which the cumulated explained
            # variance percentage is superior to the desired threshold
            ratio_cumsum = explained_variance_ratio_.cumsum()
            n_components = np.searchsorted(ratio_cumsum, n_components) + 1

        # Compute noise covariance using Probabilistic PCA model
        # The sigma2 maximum likelihood (cf. eq. 12.46)
        if n_components < min(n_features, n_samples):
            self.noise_variance_ = explained_variance_[n_components:].mean()
        else:
            self.noise_variance_ = 0.

        self.n_samples_, self.n_features_ = n_samples, n_features
        self.components_ = components_[:n_components]
        self.n_components_ = n_components
        self.explained_variance_ = explained_variance_[:n_components]
        self.explained_variance_ratio_ = \
            explained_variance_ratio_[:n_components]

        return U, S, V
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/decomposition/pca.py/PCA._fit_full/original