from ..constants import BAD_FITNESS


class Individual:
    fitness_set_negative_to_zero = False # "static" field. Note: when using diversification techniques (e.g. niching or novelty), leaving this as False and allowing negative fitness values requires verifying/improving diversification formulas. Dividing fitness by similarity (or multiplying by diversity) may have undesired consequences when fitness can be both positive and negative (e.g. low similarity may make positive fitness values higher and negative fitness values lower, while the intention would be to improve fitness for low similarity).

    def __init__(self):
        self.genotype = None
        # used for stats. This is raw fitness value, None = not evaluated or invalid genotype
        self.rawfitness: float = None
        # used in selection and can be modified e.g. by diversity or niching techniques
        self.fitness: float = None

    def copyFrom(self, individual):  # "copying constructor"
        self.genotype = individual.genotype
        self.rawfitness = individual.rawfitness
        self.fitness = individual.fitness
        return self

    def copy(self):
        new_copy = Individual()
        new_copy.genotype = self.genotype
        new_copy.rawfitness = self.rawfitness
        new_copy.fitness = self.fitness
        return new_copy

    def set_and_evaluate(self, genotype, evaluate):
        self.genotype = genotype
        fitness = evaluate(genotype)
        if fitness is not BAD_FITNESS:  # BAD_FITNESS indicates that the genotype was not valid or some other problem occurred during evaluation
            if Individual.fitness_set_negative_to_zero and fitness < 0:
                fitness = 0
        self.fitness = self.rawfitness = fitness

    def __str__(self):
        try:
            return "%g\t%g\t'%s'" % (self.rawfitness, self.fitness, self.genotype)
        except:
            return "%g\t'%s'" % (self.rawfitness, self.genotype)

    def __gt__(self, other):
        return self.rawfitness > other.rawfitness