MCH does not return same results with fixed rand seed

[eltrompetero]

The following code should always return the same solution, but it does not:

solver = MCH(X,
             sample_size=10_000,
             rng=np.random.RandomState(0),
             calc_observables=calc_observables,
             model=model,
             mch_approximation=mch_approximation)

# Define function for changing learning parameters as we converge.
def learn_settings(i):
    """
    Take in the iteration counter and set the maximum change allowed in any given 
    parameter (maxdlamda) and the multiplicative factor eta, where 
    d(parameter) = (error in observable) * eta.
    
    Additional option is to also return the sample size for that step by returning a 
    tuple. Larger sample sizes are necessary for higher accuracy.
    """
    return {'maxdlamda':exp(-i/5.)*.5,'eta':exp(-i/5.)*.5}

# Run solver.
solver.solve(initial_guess=model.multipliers,
             maxiter=30,
             custom_convergence_f=learn_settings,
             n_iters=500,
             burn_in=1_000);