human
I am working on the file "Data Generation and Likelihood Fit.ipynb".
The first task is:
VI. Estimation
1°) Dynamic Utility Logit Model
In this section, we implement a Dynamic Utility Logit model, which will be our workbench for the estimation of the data. We wrap in this class the functions defined earlier (to avoid global references) and refactor some portions of the code (to make the model more flexible). The maintenance cost function is kept outside of the scope of the class, and must be supplied at the initialization of the object as an argument.
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class DynamicLogit(object):
    def __init__(self, data, Y, X, p, MF, npars):
        """
        A statistics workbench used to evaluate the cost parameters underlying 
        a bus replacement pattern by a forward-looking agent.
        
        Takes:
            * Data: a Pandas dataframe, which contains:
                -Y: the name of the column containing the dummy exogenous 
                    variable (here, the choice)
                -X: the name of the column containing the endogenous variable 
                    (here, the state of the bus)

            * p: The state-transition vector of endogenous variable.
                    For instance, p = [0, 0.6, 0.4] means that the bus will 
                    transition to the next mileage state with probability 0.6, 
                    and to the second next mileage state with probability 0.4.

            * MF: A function passed as an argument, which is the functional 
                  form for the maintenance cost. This function must accept as
                  a first argument a state s, and as a second argument a vector
                  of parameters.
                  
            * npars: The number of parameters to evalutate (i.e. the number of 
                     parameters of the maintenance cost function, plus 1 for
                     the replacement cost)
        """        

        self.endog = data.loc[:, Y].values
        self.exog = data.loc[:, X].values
        
        self.N = self.endog.shape[0]
        self.S = int(self.exog.max()*2) # Assumes that the true maximum number 
                                         # states is twice the maximum observed
                                         # state.
        
        
        # Check that p is a correct vector of probabilities (i.e. sums to 1)
        p = np.array(p)        
        if p.sum() == 1:
            self.p = p
        else:
            raise ValueError(("The probability of state transitions should add" 
                              " up to 1!"))
        
  ...