304 lines
12 KiB
Python
304 lines
12 KiB
Python
# Import square root function for ga.adapt()
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from math import sqrt
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import random
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import sqlite3
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from copy import deepcopy
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# Import all the data structure prebuilt modules
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from structure import Population as create_population
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from structure import Chromosome as create_chromosome
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from structure import Gene as create_gene
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# Structure Methods
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from fitness_function import Fitness_Examples
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from initialization import Initialization_Methods
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from termination_point import Termination_Methods
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# Parent/Survivor Selection Methods
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from parent_selection import Parent_Selection
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from survivor_selection import Survivor_Selection
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# Genetic Operator Methods
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from mutation import Mutation_Methods
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from crossover import Crossover_Methods
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# Database class
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from database import sql_database
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from sqlite3 import Error
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# Graphing package
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from database import matplotlib_graph
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import matplotlib.pyplot as plt
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class Attributes:
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"""Default GA attributes can be found here. If any attributes have not
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been set then they will fall back onto the default attribute. All
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attributes have been catigorized to explain sections in the ga process."""
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target_fitness_type_dict = {
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'min' : 'min',
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'minimize' : 'min',
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'minimise' : 'min',
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'minimization' : 'min',
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'minimisation' : 'min',
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'max' : 'max',
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'maximize' : 'max',
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'maximise' : 'max',
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'maximization' : 'max',
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'maximisation' : 'max'
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}
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def __init__(self,
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chromosome_length = 10,
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population_size = 10,
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chromosome_impl = None,
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gene_impl = lambda: random.randint(1, 10),
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population = None,
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target_fitness_type = 'max',
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update_fitness = True,
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parent_ratio = 0.10,
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selection_probability = 0.50,
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tournament_size_ratio = 0.10,
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current_generation = 0,
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current_fitness = 0,
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generation_goal = 100,
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fitness_goal = None,
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tolerance_goal = None,
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percent_converged = 0.50,
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chromosome_mutation_rate = 0.15,
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gene_mutation_rate = 0.05,
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adapt_rate = 0.05,
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adapt_probability_rate = 0.05,
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adapt_population_flag = True,
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max_selection_probability = 0.75,
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min_selection_probability = 0.25,
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max_chromosome_mutation_rate = None,
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min_chromosome_mutation_rate = None,
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max_gene_mutation_rate = None,
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min_gene_mutation_rate = None,
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dist = None,
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initialization_impl = Initialization_Methods.random_initialization,
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fitness_function_impl = Fitness_Examples.is_it_5,
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make_population = create_population,
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make_chromosome = create_chromosome,
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make_gene = create_gene,
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parent_selection_impl = Parent_Selection.Rank.tournament,
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crossover_individual_impl = Crossover_Methods.Individual.single_point,
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crossover_population_impl = Crossover_Methods.Population.sequential_selection,
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survivor_selection_impl = Survivor_Selection.fill_in_best,
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mutation_individual_impl = Mutation_Methods.Individual.individual_genes,
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mutation_population_impl = Mutation_Methods.Population.random_avoid_best,
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termination_impl = Termination_Methods.fitness_generation_tolerance,
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Database = sql_database.SQL_Database,
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database_name = 'database.db',
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sql_create_data_structure = """CREATE TABLE IF NOT EXISTS data (
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id INTEGER PRIMARY KEY,
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config_id INTEGER DEFAULT NULL,
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generation INTEGER NOT NULL,
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fitness REAL,
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chromosome TEXT
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); """,
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Graph = matplotlib_graph.Matplotlib_Graph
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):
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# Initilization variables
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self.chromosome_length = deepcopy(chromosome_length)
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self.population_size = deepcopy(population_size)
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self.chromosome_impl = deepcopy(chromosome_impl)
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self.gene_impl = deepcopy(gene_impl)
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self.population = deepcopy(population)
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self.target_fitness_type = deepcopy(target_fitness_type)
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self.update_fitness = deepcopy(update_fitness)
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# Selection variables
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self.parent_ratio = deepcopy(parent_ratio)
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self.selection_probability = deepcopy(selection_probability)
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self.tournament_size_ratio = deepcopy(tournament_size_ratio)
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# Termination variables
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self.current_generation = deepcopy(current_generation)
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self.current_fitness = deepcopy(current_fitness)
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self.generation_goal = deepcopy(generation_goal)
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self.fitness_goal = deepcopy(fitness_goal)
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self.tolerance_goal = deepcopy(tolerance_goal)
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self.percent_converged = deepcopy(percent_converged)
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self.adapt_rate = deepcopy(adapt_rate)
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self.adapt_probability_rate = deepcopy(adapt_probability_rate)
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self.adapt_population_flag = deepcopy(adapt_population_flag)
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# Bounds on probabilities when adapting
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self.max_selection_probability = max_selection_probability
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self.min_selection_probability = min_selection_probability
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self.max_chromosome_mutation_rate = chromosome_mutation_rate if (max_chromosome_mutation_rate is None) else max_chromosome_mutation_rate
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self.min_chromosome_mutation_rate = chromosome_mutation_rate if (min_chromosome_mutation_rate is None) else min_chromosome_mutation_rate
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self.max_gene_mutation_rate = gene_mutation_rate if (max_gene_mutation_rate is None) else max_gene_mutation_rate
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self.min_gene_mutation_rate = gene_mutation_rate if (min_gene_mutation_rate is None) else min_gene_mutation_rate
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# Distance between two chromosomes
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if dist is None:
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self.dist = lambda chromosome_1, chromosome_2:\
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sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
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else:
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self.dist = dist
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# Mutation variables
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self.chromosome_mutation_rate = deepcopy(chromosome_mutation_rate)
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self.gene_mutation_rate = deepcopy(gene_mutation_rate)
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# Default EasyGA implimentation structure
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self.initialization_impl = deepcopy(initialization_impl)
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self.fitness_function_impl = deepcopy(fitness_function_impl)
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self.make_population = deepcopy(make_population)
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self.make_chromosome = deepcopy(make_chromosome)
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self.make_gene = deepcopy(make_gene)
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# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
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self.parent_selection_impl = deepcopy(parent_selection_impl)
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self.crossover_individual_impl = deepcopy(crossover_individual_impl)
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self.crossover_population_impl = deepcopy(crossover_population_impl)
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self.survivor_selection_impl = deepcopy(survivor_selection_impl)
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self.mutation_individual_impl = deepcopy(mutation_individual_impl)
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self.mutation_population_impl = deepcopy(mutation_population_impl)
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# The type of termination to impliment
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self.termination_impl = deepcopy(termination_impl)
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# Database varibles
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self.database = Database()
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self.database_name = deepcopy(database_name)
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self.sql_create_data_structure = deepcopy(sql_create_data_structure)
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# Graphing variables
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self.graph = Graph(self.database)
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def save_population(self):
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"""Saves the current population to the database."""
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self.database.insert_current_population(self)
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def save_chromosome(self, chromosome):
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"""Saves the given chromosome to the database."""
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self.database.insert_current_chromosome(self.current_generation, chromosome)
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def numeric_chromosomes(self):
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"""Sets default numerical based methods"""
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# Adapt every 10th generation
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self.adapt_rate = 0.10
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# Use averaging for crossover
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self.crossover_individual_impl = Crossover_Methods.Individual.Arithmetic.average
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# Euclidean norm
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self.dist = lambda chromosome_1, chromosome_2:\
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sqrt(sum(
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(gene_1.value - gene_2.value) ** 2
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for gene_1, gene_2
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in zip(chromosome_1, chromosome_2)
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))
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def permutation_chromosomes(self):
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"""Sets default permutation based methods"""
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self.crossover_individual_impl = Crossover_Methods.Individual.Permutation.ox1
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self.mutation_individual_impl = Mutation_Methods.Individual.Permutation.swap_genes
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# Count the number of gene pairs they have in common
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def dist(chromosome_1, chromosome_2):
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gene_list_1 = list(chromosome_1)
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gene_list_2 = list(chromosome_2)
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count = 0
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for i in range(len(gene_list_1)-1):
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for j in range(len(gene_list_2)-1):
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if gene_list_1[i] == gene_list_2[j]:
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if gene_list_1[i+1] == gene_list_2[j+1]:
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count += 1
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break
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return count
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self.dist = dist
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# Getter and setters for all required varibles
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@property
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def database_name(self):
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"""Getter function for the database name"""
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return self._database_name
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@database_name.setter
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def database_name(self, value_input):
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"""Setter function with error checking for the database name"""
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# Update the database class of the name change
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self.database._database_name = value_input
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# Set the name in the ga attribute
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self._database_name = value_input
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@property
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def chromosome_length(self):
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"""Getter function for chromosome length"""
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return self._chromosome_length
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@chromosome_length.setter
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def chromosome_length(self, value_input):
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"""Setter function with error checking for chromosome length"""
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# If the chromosome length is less then or equal 0 throw error
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if(not isinstance(value_input, int) or value_input <= 0):
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raise ValueError("Chromosome length must be integer greater then 0")
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self._chromosome_length = value_input
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@property
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def population_size(self):
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"""Getter function for population size"""
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return self._population_size
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@population_size.setter
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def population_size(self, value_input):
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"""Setter function with error checking for population size"""
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# If the population size is less then or equal 0 throw error
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if(not isinstance(value_input, int) or value_input <= 0):
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raise ValueError("Population length must be integer greater then 0")
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self._population_size = value_input
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@property
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def target_fitness_type(self):
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"""Getter function for target fitness type."""
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return self._target_fitness_type
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@target_fitness_type.setter
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def target_fitness_type(self, value_input):
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"""Setter function for target fitness type for
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converting input to min/max."""
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if value_input in self.target_fitness_type_dict.keys():
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self._target_fitness_type = self.target_fitness_type_dict[value_input]
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# Custom input
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else:
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self._target_fitness_type = value_input
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