cma.fitness_transformations.ComposedFunction

compose an arbitrary number of functions.

A class instance is a list of functions. Calling the instance executes the composition of these functions (evaluating from right to left as in math notation). Functions can be added to or removed from the list at any time with the obvious effect. To remain consistent (if needed), the list_of_inverses attribute must be updated respectively.

>>> import numpy as np
>>> from cma.fitness_transformations import ComposedFunction
>>> f1, f2, f3, f4 = lambda x: 2*x, lambda x: x**2, lambda x: 3*x, lambda x: x**3
>>> f = ComposedFunction([f1, f2, f3, f4])
>>> assert isinstance(f, list) and isinstance(f, ComposedFunction)
>>> assert f[0] == f1  # how I love Python indexing
>>> assert all(f(x) == f1(f2(f3(f4(x)))) for x in np.random.rand(10))
>>> assert f4 == f.pop()
>>> assert len(f) == 3
>>> f.insert(1, f4)
>>> f.append(f4)
>>> assert all(f(x) == f1(f4(f2(f3(f4(x))))) for x in range(5))

A more specific example:

>>> from cma.fitness_transformations import ComposedFunction
>>> from cma.constraints_handler import BoundTransform
>>> from cma import ff
>>> f = ComposedFunction([ff.elli,
...                       BoundTransform([[0], [1]]).transform])
>>> assert max(f([2, 3]), f([1, 1])) <= ff.elli([1, 1])

Details:

• This class can serve as basis for a more transparent alternative to a scaling_of_variables CMA option or for any necessary transformation of the fitness/objective function (genotype-phenotype transformation).
• The parallelizing call with a list of solutions of the Function class is not inherited. The inheritence from Function is rather declarative than funtional and could be omitted.