# Multiply

class Multiply(coefficient, filter=<function Multiply.<lambda>>)[source]

Multiplies the fitness values by some coefficient.

Examples

Multiplying Fitness Values by a Set of Coefficients

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.

If your final fitness value depends on the combination of these properties, you will probably first want to scale them with `DivideByMean`. Once this is done, you may want to multiply each property by some coefficient, which reflects its relative importance to the final fitness value. For example if you multiply the value of one property by `1` and another by `2`, the second will contribute twice as much to the final fitness value, assuming that you get the final fitness value by using the `Sum` normalizer after `Multiply`.

Giving a concrete example

```import stk
import numpy as np

building_block = stk.BuildingBlock(
smiles='BrCCBr',
functional_groups=[stk.BromoFactory()],
)

population = (
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=(building_block, ),
repeating_unit='A',
num_repeating_units=2,
),
).with_fitness_value(
fitness_value=(1, 1, 1),
normalized=False,
),
)

normalizer = stk.Multiply((1, 2, 3))
normalized_population = tuple(normalizer.normalize(population))
normalized_record, = normalized_population
assert np.all(np.equal(
normalized_record.get_fitness_value(),
(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(
smiles='BrCCBr',
functional_groups=[stk.BromoFactory()],
)

population = (
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=(building_block, ),
repeating_unit='A',
num_repeating_units=2,
),
).with_fitness_value(
fitness_value=(1, 1, 1),
normalized=False,
),
# This will have a fitness value of None.
stk.MoleculeRecord(
topology_graph=stk.polymer.Linear(
building_blocks=(building_block, ),
repeating_unit='A',
num_repeating_units=2,
),
),
)

normalizer = stk.Multiply(
coefficient=(1, 2, 3),
# Only normalize values which are not None.
filter=lambda population, record:
record.get_fitness_value() is not None,
)

# Calling normalizer.normalize() will return a new
# population holding the molecule records with normalized
# fitness values.
normalized_population = tuple(normalizer.normalize(
population=population,
))
normalized_record1, normalized_record2 = normalized_population
assert np.all(np.equal(
normalized_record1.get_fitness_value(),
(1, 2, 3),
))
assert normalized_record2.get_fitness_value() is None
```

Methods

 `normalize`(population) Normalize the fitness values in population.
__init__(coefficient, filter=<function Multiply.<lambda>>)[source]

Initialize a `Multiply` instance.

Parameters
normalize(population)[source]

Normalize the fitness values in population.

Parameters

population (`tuple` of `MoleculeRecord`) – The molecules which need to have their fitness values normalized.

Yields

`MoleculeRecord` – A record with a normalized fitness value.