def __call__(self, X):
    """Spits out the hypothesis for each machine given the data


    Parameters:

      X (numpy.ndarray): The input data matrix. This must be a numpy.ndarray
        with 2 dimensions. Every row corresponds to one example of the data
        set, every column, one different feature.


    Returns:

      numpy.ndarray: A 2D numpy.ndarray with as many entries as rows in the
        input 2D array ``X``, representing g(x), the sigmoidal hypothesis. Each
        column on the output array represents the output of one of the logistic
        regression machines in this

    """

    return numpy.hstack([m(add_bias(X)) for m in self.machines])
def __call__ ( self , X ) : return numpy . hstack ( [ m ( add_bias ( X ) ) for m in self . machines ] )
Spits out the hypothesis for each machine given the data


    Parameters:

      X (numpy.ndarray): The input data matrix. This must be a numpy.ndarray
        with 2 dimensions. Every row corresponds to one example of the data
        set, every column, one different feature.


    Returns:

      numpy.ndarray: A 2D numpy.ndarray with as many entries as rows in the
        input 2D array ``X``, representing g(x), the sigmoidal hypothesis. Each
        column on the output array represents the output of one of the logistic
        regression machines in this