# stk.DivideByMean

class stk.DivideByMean(filter=<function DivideByMean.<lambda>>)[source]

Bases: `FitnessNormalizer`[`T`]

Divides fitness values by the population mean.

While this function can be used if the fitness value of each molecule in the population is a single number, it is most useful when the fitness value is a `tuple` of numbers. In this case, it is necessary to somehow combine the numbers so that a single fitness value is produced. For example, take a fitness value which is the vector holding the properties `[energy, diameter, num_atoms]`. For a given molecule these numbers may be something like `[200,000, 12, 140]`. If we were to sum these numbers, the energy term would dominate the final fitness value. In order to combine these numbers we can divide them by the population averages. For example, if the average energy of molecules in the population is `300,000` the average diameter is `10` and the average number of atoms is `70` then the fitness vector would be scaled to `[0.5, 1.2, 2]`. These numbers are now of a similar magnitude and can be summed to give a reasonable value. After division , each value represents how much better than the population average each property value is. In essence we have removed the units from each parameter.

Examples

Selectively Normalizing Fitness Values

Sometimes you do not want to normalize all the values in a population together. For example, if a failed fitness value calculation resulted in some records having a fitness value of `None`, you would want to ignore these 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: (1., 2., 3.),
record2: None,
}
mean_scaler = stk.DivideByMean(
# Only normalize values which are not None.
filter=lambda fitness_values, record:
fitness_values[record] is not None
)
normalized_fitness_values = mean_scaler.normalize(
fitness_values=fitness_values,
)
assert np.all(np.equal(
normalized_fitness_values[record1],
(1, 1, 1),
))
```
Parameters:

filter (Callable[[dict[T, Any], T], bool]) – A function which returns `True` or `False`. Only molecules which return `True` will have fitness values normalized. By default, all molecules will have fitness values normalized. The instance passed to the fitness_values argument of `normalize()` is passed as the first argument, while the second argument will be passed every `MoleculeRecord` in it, one at a time.

Methods

 `normalize` Normalize some fitness values.
normalize(fitness_values)[source]

Normalize some fitness values.

Parameters:

fitness_values (dict[T, Any]) – The molecules which need to have their fitness values normalized.

Returns:

The new fitness value for each molecule.

Return type:

dict[T, Any]