'Multinomial logit cumulative distribution function.
Parameters
X : array
The linear predictor of the model XB.
Returns
cdf : ndarray
The cdf evaluated at `X`.
Notes
In the multinomial logit model.
.. math:: \frac{\exp\left(\beta_{j}^{\prime}x_{i}\right)}{\sum_{k=0}^{J}\exp\left(\beta_{k}^{\prime}x_{i}\right)}'

def cdf(self, X):

    eXB = np.column_stack((np.ones(len(X)), np.exp(X)))
    return (eXB / eXB.sum(1)[:, None])
