egttools.numerical.numerical_

The numerical module contains optimized functions and classes to simulate evolutionary dynamics in large populations. This module is written in C++.

Functions

calculate_expected_group_success	Calculate the expected group success eta_G under the stationary distribution.
calculate_expected_indicator	Calculate E[f] = sum_s sd(s) * sum_g P(g|s) * f(g) for an arbitrary indicator f.
calculate_expected_indicators	Calculate E[f_k] for multiple indicator functions in a single pass.
calculate_expected_indicators_precomputed	Compute expected indicators from a precomputed indicator matrix (pure C++, GIL released).
calculate_expected_payoff	Calculate the expected payoff averaged over the stationary distribution.
calculate_expected_state_indicator	Calculate E[f] = sum_s sd(s) * f(s) for a single state-level indicator.
calculate_expected_state_indicators	Calculate E[f_k] = sum_s sd(s) * f_k(s) for multiple state-level indicators in one pass.
calculate_expected_state_indicators_precomputed	Fast path: E[f_k] = sum_s sd(s) * indicator_values(s, k) using a precomputed matrix.
calculate_hypergeometric_expected_value	Compute E[f | state] = sum_g P(g | state) * f(g) for a single population state.
calculate_hypergeometric_fitness	Compute the fitness of a focal player (not included in strategies) via hypergeometric sampling.
calculate_nb_states	Calculate the number of possible states in a discrete simplex.
calculate_state	Convert a discrete population configuration into a unique index.
calculate_strategies_distribution	Calculate the average frequency of each strategy given a stationary distribution.
is_arpack_enabled	Check if EGTtools was compiled with ARPACK eigensolver support.
is_blas_lapack_enabled	Check if EGTtools was compiled with BLAS/LAPACK acceleration.
is_boost_enabled	Check if EGTtools was compiled with Boost support.
is_openmp_enabled	Check if EGTtools was compiled with OpenMP support.
is_petsc_enabled	Check if EGTtools was compiled with PETSc/SLEPc MPI eigensolver support.
precompute_group_to_state_indicator_matrix	Build a state-level indicator matrix from group-level callables.
replicator_equation	Compute the replicator dynamics gradient for a two-player matrix game.
replicator_equation_n_player	Compute the replicator dynamics gradient for an N-player game defined by a payoff table.
sample_simplex	Convert a state index into a group composition vector.
sample_simplex_directly	Sample a discrete population state uniformly at random from the simplex.
sample_unit_simplex	Sample a point uniformly at random from the unit simplex.
vectorized_replicator_equation	Vectorized computation of the replicator dynamics for three-strategy two-player games.
vectorized_replicator_equation_n_player	Vectorized computation of the replicator dynamics for three-strategy N-player games.

Classes

GeneralPopulationEvolver	Evolver for a general population structure.
MLSGarcia	Multi-level selection following Garcia & van den Bergh (2011).
MLSTraulsen	Multi-level selection following Traulsen & Nowak (2006).
PairwiseComparison	Analytical pairwise-comparison process for finite populations.
PairwiseComparisonNumerical	Numerical solver for evolutionary dynamics under the pairwise comparison rule.
PairwiseComparisonTransitionOperator	Matrix-free transition operator for the pairwise comparison process.