Merge branch 'master' of https://github.com/danielwilczak101/EasyGA

MANIFEST.in

@@ -0,0 +1,19 @@
+graft docs
+graft src
+graft ci
+graft tests
+
+include .bumpversion.cfg
+include .coveragerc
+include .cookiecutterrc
+include .editorconfig
+
+include AUTHORS.rst
+include CHANGELOG.rst
+include CONTRIBUTING.rst
+include LICENSE
+include README.rst
+
+include tox.ini .travis.yml .appveyor.yml .readthedocs.yml .pre-commit-config.yaml
+
+global-exclude *.py[cod] __pycache__/* *.so *.dylib

README.md

@@ -1,6 +1,6 @@
 # EasyGA - A general solution to Genetic Algorithms
 
-The projects has just started
+The project has just started
 
 ## Installation
 
@@ -12,34 +12,48 @@ pip3 install EasyGA
 
 To use the package:
 ```python
-import EasyGA as ga
+import EasyGA
 ```
 
 ## Usage
-
 ```python
-import EasyGA as ga
+import random
+import EasyGA
 
-chromosome = ga.chromosome()
+# The user defined gene function
+def user_gene_function():
+    return random.randint(1, 100)
 
-# Fill the chromosome with genes with Gene Number i'th number 
+# Standard user size requirements:
-for i in range(10):
+Population_size = 10
-    gene_value = f"Gene Number {i}"
+Chromosome_length = 10
-    new_gene = ga.gene("gene_value")
-    chromosome.add_gene(new_gene)
 
-# Chromosome has 10 genes in it
+# Create the Genetic algorithm
-print(len(chromosome.genes))
+ga = EasyGA.GA(Population_size, Chromosome_length,user_gene_function)
+ga.initialize()
 
-# Get the first genes value
+# Looking at the first chromosome in the population:
-print(chromosome.genes[0].get_value())
+ga.population.chromosomes[0].print_chromosome()
 
 ```
 
 # Developing EasyGA
+Download the repository to some folder - If you never used git. Look up a youtube tutorial. It will all make sense.
+```
+git clone https://github.com/danielwilczak101/EasyGA.git
+```
+Then install the repositroy using this command:
+```
+pip install -e .
+```
 
+# Working on developing a devel branch
 To install EASY, along with the tools you need to develop and run tests, run the following in your virtual env:
 
 ```bash
+<<<<<<< HEAD
 $ pip install -e .[dev]
+=======
+$ pip install -e .[devel]
+>>>>>>> 306f98d8664024828f5d094268f3da852e904262
 ```

setup.py

@@ -5,10 +5,11 @@ with open("README.md", "r") as fh:
 
 setup(
     name='EasyGA',
-    version='0.0.7',
+    version='0.0.8',
     description='A ubiquitous or general purpuse GA',
     py_modules=["EasyGA"],
     package_dir={'':'src'},
+    python_requires='>=3.6',
     url="https://github.com/danielwilczak101/EasyGA",
     author="Daniel Wilczak",
     author_email="danielwilczak101@gmail.com",

src/EasyGA.py

@@ -1,6 +1,7 @@
 from initialization.random_initialization import random_initialization
 
 def check_gene(value):
+    #Check to make sure the gene is not empty
     assert value != "" , "Gene can not be empty"
     return value
 
@@ -36,11 +37,15 @@ class chromosome:
 
     def print_chromosome(self):
         for i in range(len(self.genes)):
+            # Print the gene one by one.
+            if(i == len(self.genes) - 1):
+                print(f"[{self.genes[i].get_value()}]", end = '')
+            else:
                 print(f"[{self.genes[i].get_value()}],", end = '')
 
 
 class population:
-    # chromosomes = [chromosome,chromosome,etc]
+    # population = [chromosome,chromosome,etc]
     def __init__(self):
         self.chromosomes = []
 

src/example.py

@@ -1,15 +1,17 @@
 import random
 import EasyGA
 
+# The user defined gene function
 def user_gene_function():
     return random.randint(1, 100)
 
+# Standard user size requirements
 Population_size = 10
 Chromosome_length = 10
 
+# Create the Genetic algorithm
 ga = EasyGA.GA(Population_size, Chromosome_length,user_gene_function)
-
-# Setup the GA's population,chromosomes and genes
 ga.initialize()
 
-print(ga.population.chromosomes[0].print_chromosome())
+# Looking at the first chromosome in the population
+ga.population.chromosomes[0].print_chromosome()