ahmedfgad

import numpy
import ga

"""
The y=target is to maximize this equation ASAP:
    y = w1x1+w2x2+w3x3+w4x4+w5x5+6wx6
    where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7)
    What are the best values for the 6 weights w1 to w6?
    We are going to use the genetic algorithm for the best possible values after a number of generations.
"""

ahmedfgad

import numpy

# This project is extended and a library called PyGAD is released to build the genetic algorithm.
# PyGAD documentation: https://pygad.readthedocs.io
# Install PyGAD: pip install pygad
# PyGAD source code at GitHub: https://github.com/ahmedfgad/GeneticAlgorithmPython

def cal_pop_fitness(equation_inputs, pop):
    # Calculating the fitness value of each solution in the current population.
    # The fitness function caulcuates the sum of products between each input and its corresponding weight.

ahmedfgad

import time
 import RPi.GPIO
 
 # Initializing the GPIO pins. The numbering using is board.
 RPi.GPIO.setmode(RPi.GPIO.BOARD)
 
 # Configuring the GPIO pin number 8 to be an output pin.
 RPi.GPIO.setup(8, RPi.GPIO.OUT)
 
 print("Running Motor.")

ahmedfgad

import os

import pygame, sys



from pygame.locals import *

import pygame.camera

ahmedfgad

import time
 import RPi.GPIO
 import numpy
 import os
 import pygame.camera
 import pygame
 
 #####GPIO#####
 # Initializing the GPIO bins. The numbering using is board.
 RPi.GPIO.setmode(RPi.GPIO.BOARD)

ahmedfgad

def crossover(parents, img_shape, n_individuals=8):
    new_population = numpy.empty(shape=(n_individuals, functools.reduce(operator.mul, img_shape)), dtype=numpy.uint8)
    
    #Previous parents (best elements).
    new_population[0:parents.shape[0], :] = parents

    # Getting how many offspring to be generated. If the population size is 8 and number of parents mating is 4, then number of offspring to be generated is 4.
    num_newly_generated = n_individuals-parents.shape[0]
    # Getting all possible permutations of the selected parents.
    parents_permutations = list(itertools.permutations(iterable=numpy.arange(0, parents.shape[0]), r=2))

ahmedfgad

import os
import sys
import numpy
import scipy.misc
import itertools
import GARI

"""
Reproduce a single image using Genetic Algorithm (GA) by evolving 
single pixel values.

ahmedfgad

import kivy.app
import kivy.uix.label
import kivy.uix.boxlayout

class FirstApp(kivy.app.App):
    def build(self):
        self.label = kivy.uix.label.Label(text="Hello Kivy")
        self.layout = kivy.uix.boxlayout.BoxLayout()
        self.layout.add_widget(widget=self.label)
        return self.layout

ahmedfgad

import kivy.app
import kivy.uix.button
import kivy.uix.boxlayout

class FirstApp(kivy.app.App):
    def build(self):
        self.button1 = kivy.uix.button.Button(text="Button 1")
        self.button2 = kivy.uix.button.Button(text="Button 2")
        self.button3 = kivy.uix.button.Button(text="Button 3")
        self.button4 = kivy.uix.button.Button(text="Button 4")

ahmedfgad

import kivy.app
import kivy.uix.button
import kivy.uix.label
import kivy.uix.textinput
import kivy.uix.boxlayout
import numpy

class TestApp(kivy.app.App):

    def add_nums(self, btn):