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Specification of the mixtures of logit
Creation of the Biogeme object for a mixtures of logit models where the integral is approximated using MonteCarlo integration.
- author:
Michel Bierlaire, EPFL
- date:
Thu Apr 13 21:04:47 2023
import biogeme.biogeme as bio
from biogeme import models
from biogeme.expressions import Beta, MonteCarlo, log, bioDraws
from biogeme.data.swissmetro import (
read_data,
TRAIN_TT_SCALED,
TRAIN_COST_SCALED,
SM_TT_SCALED,
SM_COST_SCALED,
CAR_TT_SCALED,
CAR_CO_SCALED,
TRAIN_AV_SP,
SM_AV,
CAR_AV_SP,
CHOICE,
)
R = 2000
def get_biogeme(the_draws: bioDraws, number_of_draws: int) -> bio.BIOGEME:
"""Function returning the Biogeme object as a function of the selected draws
:param the_draws: expression representing the draws.
:param number_of_draws: number of draws to generate.
:return: Biogeme object.
"""
asc_car = Beta('asc_car', 0, None, None, 0)
asc_train = Beta('asc_train', 0, None, None, 0)
b_time = Beta('b_time', 0, None, None, 0)
b_time_s = Beta('b_time_s', 1, None, None, 0)
b_cost = Beta('b_cost', 0, None, None, 0)
# Define a random parameter, normally distributed, designed to be used
# for Monte-Carlo simulation
b_time_rnd = b_time + b_time_s * the_draws
# Definition of the utility functions
v_1 = asc_train + b_time_rnd * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED
v_2 = b_time_rnd * SM_TT_SCALED + b_cost * SM_COST_SCALED
v_3 = asc_car + b_time_rnd * CAR_TT_SCALED + b_cost * CAR_CO_SCALED
# Associate utility functions with the numbering of alternatives
utilities = {1: v_1, 2: v_2, 3: v_3}
# Associate the availability conditions with the alternatives
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}
# The choice model is a logit, with availability conditions
prob = models.logit(utilities, av, CHOICE)
logprob = log(MonteCarlo(prob))
database = read_data()
the_biogeme = bio.BIOGEME(database, logprob, number_of_draws=number_of_draws)
return the_biogeme