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README

Installation

Movado needs python 3.6 or higher. Install it through pip (on many linux systems you need to use pip3 to force python3 installation):

pip3 install --user --no-cache dovado-rtl

Usage

Movado exposes just an annotation which you need to apply to the fitness function you want to approximate:

@approximate(outputs=3)
def fitness(point: List[Number]) -> List[Number]:
    # my fitness logic

class SomeClass:
    def __init__():
        ...
    @approximate(outputs=1)
    def fitness(self, point: List[Number]) -> List[Number]:
        # this is also allowed

The only mandatory parameter to approximate is the number of objectives of your optimization problem, in the above case the fitness function returns a list of 3 floats and takes as input a list of numbers. Fitness Function Structure:

1. the fitness function must take only a list of numbers as input. If we approximate a method in a class self is allowed as first parameter but it must be strictly followed by the list of numbers
2. the fitness function must output a list of numbers with constant length between fitness evaluation

Parameters

Other than outputs several other optional parameters are available

Parameter	Description	Default Value
disabled	if True the approximation	False
	is disabled
stochastic	if True caching of fitness	False
	calls is disabled
estimator	estimator to use	HoeffdingAdaptiveTree
controller	controller to user	Mab