'Predict response variable of a model given exogenous variables.
Parameters
params : array-like
2d array of fitted parameters of the model. Should be in the
order returned from the model.
exog : array-like
1d or 2d array of exogenous values.  If not supplied, the
whole exog attribute of the model is used. If a 1d array is given
it assumed to be 1 row of exogenous variables. If you only have
one regressor and would like to do prediction, you must provide
a 2d array with shape[1] == 1.
linear : bool, optional
If True, returns the linear predictor dot(exog,params).  Else,
returns the value of the cdf at the linear predictor.
Notes
Column 0 is the base case, the rest conform to the rows of params
shifted up one for the base case.'

def predict(self, params, exog=None, linear=False):

    if (exog is None):
        exog = self.exog
    if (exog.ndim == 1):
        exog = exog[None]
    pred = super(MultinomialModel, self).predict(params, exog, linear)
    if linear:
        pred = np.column_stack((np.zeros(len(exog)), pred))
    return pred
