def _intercept_dot(w, X):
    """Computes y * np.dot(X, w).

    It takes into consideration if the intercept should be fit or not.

    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.

    """
    c = 0.
    if w.size == X.shape[1] + 1:
        c = w[-1]
        w = w[:-1]

    z = np.dot(X, w) + c
    return w, c, z
def _intercept_dot ( w , X ) : c = 0. if w . size == X . shape [ 1 ] + 1 : c = w [ - 1 ] w = w [ : - 1 ] z = np . dot ( X , w ) + c return w , c , z
Computes y * np.dot(X, w).

    It takes into consideration if the intercept should be fit or not.

    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.

    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.