stk.Add
- class stk.Add(number, filter=<function Add.<lambda>>)[source]
Bases:
FitnessNormalizer
[T
]Adds a number to the fitness values.
Examples
Incrementing Fitness Values by a Set of Values
In this example, assume that each fitness value consists of a
tuple
of numbers, each representing a different property of the molecule, and each contributing to the final fitness value. The properties can be anything, such as energy, number of atoms or diameter.Often, if these properties indicate a low fitness value, you will take their inverse. However, if these properties can have a value of 0, and you try to take their inverse you can end up dividing by 0, which is bad. To avoid this, you can add a number, like 1, to the fitness values before taking their inverse. This normalizer allows you to do this.
Giving a concrete example
import stk import numpy as np building_block = stk.BuildingBlock('BrCCBr', stk.BromoFactory()) record = stk.MoleculeRecord( topology_graph=stk.polymer.Linear( building_blocks=[building_block], repeating_unit='A', num_repeating_units=2, ), ) fitness_values = { record: (0, 0, 0), } normalizer = stk.Add((1, 2, 3)) normalized_fitness_values = normalizer.normalize(fitness_values) assert np.all(np.equal( normalized_fitness_values[record], (1, 2, 3), ))
Selectively Normalizing Fitness Values
Sometimes, you only want to normalize some members of a population, for example if some do not have an assigned fitness value, because the fitness calculation failed for whatever reason. You can use the filter parameter to exclude records from the normalization
import stk import numpy as np building_block = stk.BuildingBlock('BrCCBr', stk.BromoFactory()) record1 = stk.MoleculeRecord( topology_graph=stk.polymer.Linear( building_blocks=[building_block], repeating_unit='A', num_repeating_units=2, ), ) record2 = stk.MoleculeRecord( topology_graph=stk.polymer.Linear( building_blocks=[building_block], repeating_unit='A', num_repeating_units=2, ), ) fitness_values = { record1: (0, 0, 0), record2: None, } normalizer = stk.Add( number=(1, 2, 3), # Only normalize values which are not None. filter=lambda fitness_values, record: fitness_values[record] is not None, ) normalized_fitness_values = normalizer.normalize( fitness_values=fitness_values, ) assert np.all(np.equal( normalized_fitness_values[record1], (1, 2, 3), )) assert normalized_fitness_values[record2] is None
- Parameters:
number (float | list[float]) – The number each fitness value is increased by. Can be a single number or multiple numbers, depending on the form of the fitness value.
filter (Callable[[dict[T, Any], T], bool]) – A function which returns
True
orFalse
. Only molecules which returnTrue
will have fitness values normalized. By default, all molecules will have fitness values normalized. The instance passed to the fitness_values argument ofnormalize()
is passed as the first argument, while the second argument will be passed everyMoleculeRecord
in it, one at a time.
Methods
Normalize some fitness values.