biogeme.mdcev.non_monotonic module
Implementation of the “non-monotonic” MDCEV model. See the technical report.
Michel Bierlaire Tue Apr 9 09:25:20 2024
- class biogeme.mdcev.non_monotonic.NonMonotonic(model_name, baseline_utilities, gamma_parameters, mu_utilities, alpha_parameters=None, scale_parameter=None, weights=None)[source]
Bases:
MdcevImplementation of the MDCEV model with non-monotonic utility function.
- Parameters:
model_name (str)
baseline_utilities (dict[int, Expression])
gamma_parameters (dict[int, Expression | None])
mu_utilities (dict[int, Expression])
alpha_parameters (dict[int, Expression] | None)
scale_parameter (Expression | None)
weights (Expression | None)
- calculate_inverse_of_determinant_one_alternative(the_id, consumption)[source]
- Calculate the inverse of the entries for the determinant. For
the outside good, gamma is equal to 0.
- Parameters:
the_id (
int) – identifier of the good.consumption (
Expression) – expression for the consumption.
- Return type:
- calculate_log_determinant_one_alternative(the_id, consumption)[source]
- Calculate the log of the entries for the determinant. For
the outside good, gamma is equal to 0.
- Parameters:
the_id (
int) – identifier of the good.consumption (
Expression) – expression for the consumption.
- Return type:
- calculate_mu_utility(alternative_id, one_observation)[source]
As this function may be called many times with the same input in forecasting mode, we use the lru_cache decorator.
- Return type:
float- Parameters:
alternative_id (int)
one_observation (Database)
- derivative_utility_one_alternative(the_id, the_consumption, epsilon, one_observation)[source]
Used in the optimization problem solved for forecasting tp calculate the dual variable.
- Return type:
float- Parameters:
the_id (int)
the_consumption (float)
epsilon (float)
one_observation (Database)
- lower_bound_dual_variable(chosen_alternatives, one_observation, epsilon)[source]
Method providing model specific lower bound on the dual variable.
- Parameters:
chosen_alternatives (
set[int]) – list of alternatives that are chosen at the optimal solutionone_observation (
Database) – data for one observation.epsilon (
ndarray) – draws from the error term.
- Return type:
float- Returns:
a lower bound and upper bound on the dual variable
- optimal_consumption_one_alternative(the_id, dual_variable, epsilon, one_observation)[source]
Analytical calculation of the optimal consumption if the dual variable is known.
- Return type:
float- Parameters:
the_id (int)
dual_variable (float)
epsilon (float)
one_observation (Database)
- transformed_utility(the_id, the_consumption)[source]
Calculates the utility for one alternative. .
- Return type:
- Parameters:
the_id (int)
the_consumption (Expression)
- utility_expression_one_alternative(the_id, the_consumption, unscaled_epsilon)[source]
Utility expression. Used only for code validation.
- Return type:
- Parameters:
the_id (int)
the_consumption (Expression)
unscaled_epsilon (Expression)