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

Removed weird github thing with old implementation

Browse Source 
Why
This commit is contained in:
RyleyGG
2020-10-04 14:35:19 -04:00
parent 7e587d48d0
commit 4b375659bb
2 changed files with 0 additions and 133 deletions
Download Patch File Download Diff File Expand all files Collapse all files

112
src/EasyGA.py
View File

@ -1,114 +1,3 @@
<<<<<<< Updated upstream
# Import all the data prebuilt modules
from initialization.population_structure.population import population as create_population
from initialization.chromosome_structure.chromosome import chromosome as create_chromosome
from initialization.gene_structure.gene import gene as create_gene
# Import functions for defaults
from initialization.gene_function.gene_random import random_gene
# Import functionality defaults
from initialization.random_initialization import random_initialization
class GA:
def __init__(self):
# Default variables
self.population = None
self.generations = 3
self.chromosome_length = 4
self.population_size = 5
self.mutation_rate = 0.03
# Defualt EastGA implimentation structure
self.gene_function_impl = random_gene
self.gene_input = []
self.gene_input_type = []
while len(self.gene_input_type) != self.chromosome_length:
self.gene_input_type.append(None)
# Set the GA Configuration
self.initialization_impl = random_initialization
#self.mutation_impl = PerGeneMutation(Mutation_rate)
#self.selection_impl = TournamentSelection()
#self.crossover_impl = FastSinglePointCrossover()
#self.termination_impl = GenerationTermination(Total_generations)
#self.evaluation_impl = TestEvaluation()
def initialize(self, gene_input):
self.gene_input = gene_input
#It's possible user may just enter "domain", "float-range", etc. for gene_input_type rather than referring to a specific gene
#In that case, create an array with the same length as the chromosome where each element is just the user's input
if isinstance(self.gene_input_type, str):
gene_input_type_temp = self.gene_input_type
self.gene_input_type = []
while len(self.gene_input_type) != self.chromosome_length:
self.gene_input_type.append(gene_input_type_temp)
#There are two types of gene_input we should expect from the user - "general" or index-dependent
#For example, if a user just enters [1,100], it should be assumed that this is a range/domain that should apply to all genes in the chromosome...
#...rather than assuming that it means gene 1 should be 1 and gene 2 should be 100.
#The check for this is done by checking if any of the values in the user's gene input are lists.
#If lists are included, then values like the ones given above will be converted (i.e. [1, 100, ["up", "left"]] becomes [[1,1], [100,100], ["up", "left"]]) and apply to specific genes
#Else if no lists are included, the gene input will apply to each gene (i.e. for chromosomes with length 3, [1,100] becomes [[1,100],[1,100],[1,100]])
general_gene_input = True
for n in range(len(self.gene_input)):
if isinstance(self.gene_input[n], list):
general_gene_input = False
break
#Converting user's input into standardized format - list of lists where each sublist is the range/domain for a specific gene
if general_gene_input == False:
for x in range(len(self.gene_input)):
if isinstance(self.gene_input[x], list) == False:
self.gene_input[x] = [self.gene_input[x], self.gene_input[x]]
else:
gene_input_temp = self.gene_input
self.gene_input = []
for y in range(self.chromosome_length):
self.gene_input.append(gene_input_temp)
#Setting up the gene_input_type defaults in the standard format
#values including strings are always domain
#values including floats but no strings is a float-range
#values included neither strings or floats is a normal range
for x in range(len(self.gene_input_type)):
try:
if (self.gene_input[x]):
pass
except IndexError:
self.gene_input.append(None)
if self.gene_input_type[x] == None and self.gene_input[x] != None: #If it hasn't been hard-set by the user
for y in range(len(self.gene_input[x])):
if isinstance(self.gene_input[x][y], str):
self.gene_input_type[x] = "domain"
break
elif isinstance(self.gene_input[x][y], float):
self.gene_input_type[x] = "float-range"
elif y == (len(self.gene_input[x]) -1 and self.gene_input_type[x] != "float-range"):
self.gene_input_type[x] = "range"
# Create the first population
self.population = self.initialization_impl(
self.chromosome_length,
self.population_size,
self.gene_function_impl,
self.gene_input,
self.gene_input_type)
def evolve():
# If you just want to evolve through all generations
pass
def evolve_generation(self, number_of_generations):
# If you want to evolve through a number of generations
# and be able to pause and output data based on that generation run.
pass
=======
import random
# Import all the data structure prebuilt modules
from initialization import Population as create_population
@ -187,7 +76,6 @@ class GA:
number_of_generations -= 1
self.current_generation += 1
>>>>>>> Stashed changes
def active(self):
"""Returns if the ga should terminate base on the termination implimented"""

21
src/run_testing.py
View File

@ -1,30 +1,10 @@
import EasyGA
<<<<<<< Updated upstream
=======
import random
>>>>>>> Stashed changes
# Create the Genetic algorithm
ga = EasyGA.GA()
<<<<<<< Updated upstream
#Creating a gene with no fitness
gene1 = ga.make_gene("Im a gene")
gene2 = ga.make_gene("Im also a gene")
#Creating a Chromosome with no genes
chromosome = ga.make_chromosome()
chromosome.add_gene(gene1)
chromosome.add_gene(gene2)
# Creating a populaiton
populaiton = ga.make_population()
populaiton.add_chromosome(chromosome)
print(gene1)
print(chromosome)
print(populaiton)
populaiton.print_all()
=======
ga.gene_impl = [random.randrange,1,10]
# Run Everyhting
@ -32,4 +12,3 @@ ga.evolve()
# Print the current population
ga.population.print_all()
>>>>>>> Stashed changes