8.1.1.6.1.4. skimpy.sampling.ga_flux_concentration_sampler¶

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8.1.1.6.1.4.1. Attributes¶

model_gen

8.1.1.6.1.4.2. Classes¶

`ItterableSeries`
`GaFluxConcentrationSampler`	This sampler performs an optimizaion

8.1.1.6.1.4.3. Functions¶

`convex_mating`(ind1, ind2[, eta])
`sample_parameters`(kmodel, tmodel, individual, ...[, ...])	Run sampling on first order model

8.1.1.6.1.4.4. Module Contents¶

class skimpy.ItterableSeries(this_series)¶

data¶

__iter__()¶

skimpy.model_gen¶

class skimpy.GaFluxConcentrationSampler(parameters=None)¶

Bases: skimpy.sampling.flux_concentration_sampler.FluxConcentrationSampler

This sampler performs an optimizaion

class Parameters¶

Bases: tuple

Parameter type specified for the parameters sampling procedure :return:

n_samples¶

n_parameter_samples¶

max_generation¶

seed¶

mutation_probability¶

crossover_scaling¶

max_eigenvalue¶

min_eigenvalue¶

scaling_parameters¶

sample(tmodel, kmodel, simple_parameter_sampler, only_stable=True)¶

Parameters:

compiled_model
flux_dict
concentration_dict
seed
max_generation
mutation_probability
eta

Returns:

fitness(flux_concentration)¶

run_ea(toolbox, stats=None, verbose=False)¶

sample_tfa_model(n_samples)¶

Parameters:

tmodel – pytfa.tmodel
n_samples – integer

Returns:

TODO pd.DataFrame indexed with reaction names and metabolite concentrations

mutate_ind(ind)¶

skimpy.convex_mating(ind1, ind2, eta=0.5)¶

skimpy.sample_parameters(kmodel, tmodel, individual, param_sampler, scaling_parameters, only_stable=True)¶: Run sampling on first order model