Merge pull request #7 from danielwilczak101/Dans_devel

Dans devel

src/EasyGA.py

@@ -1,10 +1,9 @@
+import random
 # 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
 
@@ -12,14 +11,14 @@ from initialization.random_initialization import random_initialization
 class GA:
     def __init__(self):
         # Default variables
+        self.domain = None
+        self.new_range = None
         self.population = None
         self.generations = 3
-        self.chromosome_length = 4
+        self.chromosome_length = 3
         self.population_size = 5
         self.mutation_rate = 0.03
         # Defualt EastGA implimentation structure
-        self.gene_function_impl = random_gene
-        # Set the GA Configuration
         self.initialization_impl = random_initialization
         #self.mutation_impl = PerGeneMutation(Mutation_rate)
         #self.selection_impl = TournamentSelection()
@@ -27,13 +26,12 @@ class GA:
         #self.termination_impl = GenerationTermination(Total_generations)
         #self.evaluation_impl = TestEvaluation()
 
-
     def initialize(self):
-        # Create the first population
         self.population = self.initialization_impl(
         self.population_size,
         self.chromosome_length,
-        self.gene_function_impl)
+        self.domain,
+        self.new_range)
 
     def evolve():
         # If you just want to evolve through all generations

src/initialization/focused_initialization.py

@@ -0,0 +1,16 @@
+# Import the data structure
+from .population_structure.population import population as create_population
+from .chromosome_structure.chromosome import chromosome as create_chromosome
+from .gene_structure.gene import gene as create_gene
+
+def focused_initialization(chromosome_length,population_size,gene_function):
+    # Create the population object
+    population = create_population()
+    # Fill the population with chromosomes
+    for i in range(population_size):
+        chromosome = create_chromosome()
+        #Fill the Chromosome with genes
+        for j in range(chromosome_length):
+            chromosome.add_gene(create_gene(gene_function()))
+        population.add_chromosome(chromosome)
+    return population

src/initialization/gene_function/gene_random.py

@@ -1,13 +0,0 @@
-# Imported library
-import random
-
-def check_values(low,high):
-    #Check to make sure its not less then zero
-    assert low > 0 , "The random gene low can not be less then zero"
-    # Check to make sure the high value is not
-    # lower than or equal to low and not 0.
-    assert high > low , "High value can not be smaller then low value"
-    assert high != 0, "High value can not be zero"
-
-def random_gene():
-    return random.randint(1,100)

src/initialization/population_structure/population.py

@@ -36,8 +36,7 @@ class population:
         self.fitness = fitness
 
     def __repr__(self):
-        for index in range(len(self.chromosomes)):
+        return ''.join([chromosome.__repr__() for chromosome in self.chromosomes])
-            return f'{self.chromosomes[index]}'
 
     def print_all(self):
         # Ex .Current population

src/initialization/random_initialization.py

@@ -3,7 +3,7 @@ from .population_structure.population import population as create_population
 from .chromosome_structure.chromosome import chromosome as create_chromosome
 from .gene_structure.gene import gene as create_gene
 
-def random_initialization(chromosome_length,population_size,gene_function):
+def random_initialization(population_size, chromosome_length, domain, new_range):
     # Create the population object
     population = create_population()
     # Fill the population with chromosomes
@@ -11,6 +11,16 @@ def random_initialization(chromosome_length,population_size,gene_function):
         chromosome = create_chromosome()
         #Fill the Chromosome with genes
         for j in range(chromosome_length):
-            chromosome.add_gene(create_gene(gene_function()))
+            if domain != None:
+                # Each chromosome location is specified with its own function
+                chromosome.add_gene(create_gene(domain(j)))
+                # Will break if chromosome_length != lists in domain
+            elif new_range != None:
+                # new_rnage = [range function,lowerbound,upperbound]
+                function = new_range[0]
+                chromosome.add_gene(create_gene(function(new_range[1],new_range[2])))
+            else:
+                #Exit because either were not specified
+                print("Your domain or range were not specified")
         population.add_chromosome(chromosome)
     return population

src/run_testing.py

@@ -1,20 +1,26 @@
 import EasyGA
+import random
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-#Creating a gene with no fitness
+def user_gene_domain(gene_index):
-gene1 = ga.make_gene("Im a gene")
+    """Each gene index is assosiated to its index in the chromosome"""
-gene2 = ga.make_gene("Im also a gene")
+    domain = [
-#Creating a Chromosome with no genes
+    random.randrange(1,100,5),
-chromosome = ga.make_chromosome()
+    random.uniform(10,5),
-chromosome.add_gene(gene1)
+    random.choice(["up","down"])
-chromosome.add_gene(gene2)
+    ]
-# Creating a populaiton
+    return domain[gene_index]
-populaiton = ga.make_population()
+
-populaiton.add_chromosome(chromosome)
+print(user_gene_domain(0))
+
+# If the user wants to use a domain
+ga.domain = user_gene_domain
+# If the user wants to use a custom range
+#ga.new_range = [random.randrange,1,100,None]
+
+ga.initialize()
+
+#ga.population.print_all()
 
-print(gene1)
-print(chromosome)
-print(populaiton)
-populaiton.print_all()