Removed weird github thing with old implementation
Why
This commit is contained in:
RyleyGG
112
src/EasyGA.py
112
src/EasyGA.py
@ -1,114 +1,3 @@
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<<<<<<< Updated upstream
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# Import all the data prebuilt modules
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from initialization.population_structure.population import population as create_population
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from initialization.chromosome_structure.chromosome import chromosome as create_chromosome
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from initialization.gene_structure.gene import gene as create_gene
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# Import functions for defaults
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from initialization.gene_function.gene_random import random_gene
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# Import functionality defaults
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from initialization.random_initialization import random_initialization
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class GA:
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def __init__(self):
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# Default variables
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self.population = None
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self.generations = 3
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self.chromosome_length = 4
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self.population_size = 5
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self.mutation_rate = 0.03
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# Defualt EastGA implimentation structure
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self.gene_function_impl = random_gene
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self.gene_input = []
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self.gene_input_type = []
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while len(self.gene_input_type) != self.chromosome_length:
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self.gene_input_type.append(None)
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# Set the GA Configuration
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self.initialization_impl = random_initialization
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#self.mutation_impl = PerGeneMutation(Mutation_rate)
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#self.selection_impl = TournamentSelection()
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#self.crossover_impl = FastSinglePointCrossover()
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#self.termination_impl = GenerationTermination(Total_generations)
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#self.evaluation_impl = TestEvaluation()
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def initialize(self, gene_input):
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self.gene_input = gene_input
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#It's possible user may just enter "domain", "float-range", etc. for gene_input_type rather than referring to a specific gene
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#In that case, create an array with the same length as the chromosome where each element is just the user's input
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if isinstance(self.gene_input_type, str):
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gene_input_type_temp = self.gene_input_type
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self.gene_input_type = []
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while len(self.gene_input_type) != self.chromosome_length:
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self.gene_input_type.append(gene_input_type_temp)
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#There are two types of gene_input we should expect from the user - "general" or index-dependent
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#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...
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#...rather than assuming that it means gene 1 should be 1 and gene 2 should be 100.
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#The check for this is done by checking if any of the values in the user's gene input are lists.
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#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
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#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]])
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general_gene_input = True
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for n in range(len(self.gene_input)):
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if isinstance(self.gene_input[n], list):
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general_gene_input = False
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break
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#Converting user's input into standardized format - list of lists where each sublist is the range/domain for a specific gene
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if general_gene_input == False:
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for x in range(len(self.gene_input)):
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if isinstance(self.gene_input[x], list) == False:
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self.gene_input[x] = [self.gene_input[x], self.gene_input[x]]
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else:
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gene_input_temp = self.gene_input
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self.gene_input = []
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for y in range(self.chromosome_length):
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self.gene_input.append(gene_input_temp)
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#Setting up the gene_input_type defaults in the standard format
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#values including strings are always domain
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#values including floats but no strings is a float-range
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#values included neither strings or floats is a normal range
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for x in range(len(self.gene_input_type)):
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try:
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if (self.gene_input[x]):
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pass
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except IndexError:
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self.gene_input.append(None)
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if self.gene_input_type[x] == None and self.gene_input[x] != None: #If it hasn't been hard-set by the user
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for y in range(len(self.gene_input[x])):
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if isinstance(self.gene_input[x][y], str):
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self.gene_input_type[x] = "domain"
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break
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elif isinstance(self.gene_input[x][y], float):
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self.gene_input_type[x] = "float-range"
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elif y == (len(self.gene_input[x]) -1 and self.gene_input_type[x] != "float-range"):
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self.gene_input_type[x] = "range"
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# Create the first population
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self.population = self.initialization_impl(
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self.chromosome_length,
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self.population_size,
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self.gene_function_impl,
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self.gene_input,
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self.gene_input_type)
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def evolve():
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# If you just want to evolve through all generations
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pass
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def evolve_generation(self, number_of_generations):
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# If you want to evolve through a number of generations
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# and be able to pause and output data based on that generation run.
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pass
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=======
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import random
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import random
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# Import all the data structure prebuilt modules
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# Import all the data structure prebuilt modules
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from initialization import Population as create_population
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from initialization import Population as create_population
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@ -187,7 +76,6 @@ class GA:
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number_of_generations -= 1
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number_of_generations -= 1
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self.current_generation += 1
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self.current_generation += 1
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>>>>>>> Stashed changes
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def active(self):
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def active(self):
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"""Returns if the ga should terminate base on the termination implimented"""
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"""Returns if the ga should terminate base on the termination implimented"""
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@ -1,30 +1,10 @@
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import EasyGA
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import EasyGA
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<<<<<<< Updated upstream
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=======
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import random
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import random
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>>>>>>> Stashed changes
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# Create the Genetic algorithm
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = EasyGA.GA()
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<<<<<<< Updated upstream
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#Creating a gene with no fitness
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gene1 = ga.make_gene("Im a gene")
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gene2 = ga.make_gene("Im also a gene")
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#Creating a Chromosome with no genes
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chromosome = ga.make_chromosome()
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chromosome.add_gene(gene1)
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chromosome.add_gene(gene2)
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# Creating a populaiton
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populaiton = ga.make_population()
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populaiton.add_chromosome(chromosome)
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print(gene1)
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print(chromosome)
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print(populaiton)
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populaiton.print_all()
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=======
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ga.gene_impl = [random.randrange,1,10]
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ga.gene_impl = [random.randrange,1,10]
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# Run Everyhting
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# Run Everyhting
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@ -32,4 +12,3 @@ ga.evolve()
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# Print the current population
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# Print the current population
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ga.population.print_all()
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ga.population.print_all()
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>>>>>>> Stashed changes
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