import random

# Import all the data structure prebuilt modules
from structure import Population as create_population
from structure import Chromosome as create_chromosome
from structure import Gene as create_gene

# Structure Methods
from fitness_function  import Fitness_Examples
from initialization    import Initialization_Methods
from termination_point import Termination_Methods

# Parent/Survivor Selection Methods
from parent_selection   import Parent_Selection
from survivor_selection import Survivor_Selection

# Genetic Operator Methods
from mutation  import Mutation_Methods
from crossover import Crossover_Methods

class attributes:
    """SAMPLE TEXT"""

    def __init__(self):
        """Initialize the GA."""

        # Initilization variables
        self.chromosome_length   = 10
        self.population_size     = 10
        self.chromosome_impl     = None
        self.gene_impl           = lambda: random.randint(1, 10)
        self.population          = None
        self.target_fitness_type = 'max'
        self.update_fitness      = True

        # Selection variables
        self.parent_ratio          = 0.1
        self.selection_probability = 0.75
        self.tournament_size_ratio = 0.1

        # Termination variables
        self.current_generation = 0
        self.current_fitness    = 0
        self.generation_goal    = 15
        self.fitness_goal       = None

        # Mutation variables
        self.mutation_rate = 0.10

        # Default EasyGA implimentation structure
        self.initialization_impl   = Initialization_Methods.random_initialization
        self.fitness_function_impl = Fitness_Examples.is_it_5
        self.make_population       = create_population
        self.make_chromosome       = create_chromosome
        self.make_gene             = create_gene

        # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
        self.parent_selection_impl     = Parent_Selection.Rank.tournament
        self.crossover_individual_impl = Crossover_Methods.Individual.single_point
        self.crossover_population_impl = Crossover_Methods.Population.sequential_selection
        self.survivor_selection_impl   = Survivor_Selection.fill_in_best
        self.mutation_individual_impl  = Mutation_Methods.Individual.single_gene
        self.mutation_population_impl  = Mutation_Methods.Population.random_selection

        # The type of termination to impliment
        self.termination_impl = Termination_Methods.fitness_and_generation_based


    # Getter and setters for all varibles
    @property
    def chromosome_length(self):
        """SAMPLE TEXT"""
        return self._chromosome_length

    @chromosome_length.setter
    def chromosome_length(self, value_input):
        if(value_input == 0):
            raise ValueError("Chromosome length must be greater then 0")
        self._chromosome_length = value_input


    @property
    def population_size(self):
        return self._population_size

    @population_size.setter
    def population_size(self, value_input):
        if(value_input == 0):
            raise ValueError("Population length must be greater then 0")
        self._population_size = value_input