pytwinnet.optimization¶

class pytwinnet.optimization.GridSearchOptimizer(param_grid, copy_twin=True)[source]¶

Bases: Optimizer

Parameters:

param_grid (Dict[str, Iterable[float]])
copy_twin (bool)

copy_twin: bool = True¶

optimize(twin, objective)[source]¶

Return type:

Dict[str, Any]

Parameters:

twin (DigitalTwin)
objective (Objective)

param_grid: Dict[str, Iterable[float]]¶

class pytwinnet.optimization.MaximizeThroughput[source]¶

Bases: Objective

evaluate(twin)[source]¶

Return type:: float
Parameters:: twin (DigitalTwin)

class pytwinnet.optimization.MinimizePowerConsumption[source]¶

Bases: Objective

evaluate(twin)[source]¶

Return type:: float
Parameters:: twin (DigitalTwin)

class pytwinnet.optimization.Objective[source]¶

Bases: ABC

abstractmethod evaluate(twin)[source]¶

Return type:: float
Parameters:: twin (DigitalTwin)

class pytwinnet.optimization.Optimizer[source]¶

Bases: ABC

abstractmethod optimize(twin, objective)[source]¶

Return type:

Dict[str, Any]

Parameters:

twin (DigitalTwin)
objective (Objective)

class pytwinnet.optimization.RandomSearchOptimizer(ranges_dbm, samples=32, seed=0, copy_twin=True)[source]¶

Bases: Optimizer

Parameters:

ranges_dbm (Dict[str, Tuple[float, float]])
samples (int)
seed (int)
copy_twin (bool)

copy_twin: bool = True¶

optimize(twin, objective)[source]¶

Return type:

Dict[str, Any]

Parameters:

twin (DigitalTwin)
objective (Objective)

ranges_dbm: Dict[str, Tuple[float, float]]¶

samples: int = 32¶

seed: int = 0¶

class pytwinnet.optimization.SimpleGreedyOptimizer(step_db=1.0, max_power_dbm=30.0, iterations=10)[source]¶

Bases: Optimizer

Parameters:

step_db (float)
max_power_dbm (float)
iterations (int)

iterations: int = 10¶

max_power_dbm: float = 30.0¶

optimize(twin, objective)[source]¶

Return type:

Dict[str, Any]

Parameters:

twin (DigitalTwin)
objective (Objective)

step_db: float = 1.0¶

class pytwinnet.optimization.SumThroughputObjective(tx_id, efficiency=1.0)[source]¶

Bases: Objective

Parameters:

tx_id (str)
efficiency (float)

evaluate(twin)[source]¶

Return type:: float
Parameters:: twin (DigitalTwin)

Modules

`bayesopt`
`ga`
`grid_search`
`objective`
`objective_ext`
`optimizer`
`pareto`
`placement`
`placement_parallel`
`random_search`
`simple_greedy`