paulin/EasyGA
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Renamed create methods to make methods for consistency

Browse Source
This commit is contained in:
SimpleArt
2020-12-20 13:57:37 -05:00
parent eaf22c07d2
commit ed67697cee
1 changed files with 10 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
src/attributes.py
View File

@ -6,9 +6,9 @@ import sqlite3
from copy import deepcopy
# 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
from structure import Population as make_population
from structure import Chromosome as make_chromosome
from structure import Gene as make_gene
# Structure Methods
from fitness_function import Fitness_Examples
@ -25,7 +25,7 @@ from crossover import Crossover_Methods
# Database class
from database import sql_database
from sqlite3 import Error
from sqlite3 import Error
# Graphing package
from database import matplotlib_graph
@ -44,7 +44,7 @@ class Attributes:
gene_impl = lambda: random.randint(1, 10),
population = None,
target_fitness_type = 'max',
update_fitness = True,
update_fitness = False,
parent_ratio = 0.10,
selection_probability = 0.50,
tournament_size_ratio = 0.10,
@ -63,14 +63,14 @@ class Attributes:
min_selection_probability = 0.25,
max_chromosome_mutation_rate = None,
min_chromosome_mutation_rate = None,
max_gene_mutation_rate = 1,
min_gene_mutation_rate = 0,
max_gene_mutation_rate = 0.15,
min_gene_mutation_rate = 0.01,
dist = None,
initialization_impl = Initialization_Methods.random_initialization,
fitness_function_impl = Fitness_Examples.is_it_5,
make_population = create_population,
make_chromosome = create_chromosome,
make_gene = create_gene,
make_population = make_population,
make_chromosome = make_chromosome,
make_gene = make_gene,
parent_selection_impl = Parent_Selection.Rank.tournament,
crossover_individual_impl = Crossover_Methods.Individual.single_point,
crossover_population_impl = Crossover_Methods.Population.sequential_selection,