Tezos_benchmarks_proto_023_PtSeouLo.Michelson_generationtype generator_config = {target_size : Tezos_benchmark.Base_samplers.range;The target size of the terms, in number of nodes, is sampled uniformly in target_size.
burn_in_multiplier : int;The generators are based on a Markov chain, which must be "heated-up" until it reaches its stationary state. A prefix of samples are therefore thrown away: this is called the burn-in phase. The number of thrown away terms is proportional to burn_in_multiplier and target_size.
}generator_config specifies some parameters to the Tezos_benchmark_alpha.Michelson_mcmc_samplers Michelson code and data generators.
val default_generator_config : generator_configDefault configuration for the generators.
val generator_config_encoding :
generator_config Tezos_base.TzPervasives.Data_encoding.tSamplers
val make_data_sampler :
?verbose:bool ->
Stdlib.Random.State.t ->
generator_config ->
Tezos_benchmark_023_PtSeouLo.Michelson_mcmc_samplers.michelson_datamake_data_sampler constructs a Michelson data sampler based on the infrastructure available in Tezos_benchmark_alpha.Michelson_mcmc_samplers.
val make_code_sampler :
?verbose:bool ->
Stdlib.Random.State.t ->
generator_config ->
Tezos_benchmark_023_PtSeouLo.Michelson_mcmc_samplers.michelson_codemake_code_sampler constructs a Michelson code sampler based on the infrastructure available in Tezos_benchmark_alpha.Michelson_mcmc_samplers.
Samplers is an instance of the direct-style (non-MCMC based) samplers implemented in Tezos_benchmark_alpha.Michelson_samplers.