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1	Py-PSO-Imp Python program, PSO - Implementation of the Particle swarm optimization, the Machine Learning

2	Py-PSO Python program, PSO - the Particle swarm optimization animate

3	Py-PSO-animate-window Python program, the Particle swarm optimization animate Form

PSO
Machine Learning

Py-PSO-Imp Python program, PSO - Implementation of the Particle swarm optimization, the Machine Learning

Py-PSO-Imp Python program, the Implementation of the Particle swarm optimization (PSO) at the Machine Learning

Create a new Python project In Visual Studio 2022

the Microsoft Visual Studio (... or 2019 or 2022) is a powerful IDE for Python language
Open/Run Microsoft Visual Studio 2022

To view Python templates, search for python.

Select the Python Application template, and select Next.

Create a new Python project In Visual Studio 2022
        On the Configure your new project screen -   (specify a name and file location for the project, and then select Create)
        Project name: Py-PSO-Imp
        Location: C:\Users\...\source\repos    (default location for Visual Studio 2022)

The new project opens in Visual Studio 2022 - (Visual Studio 2022 Compiler - IDE, to compile Python project / file )

The Visual Studio Solution Explorer window shows the project structure

Python Project Properieties - Py-PSO-Imp

Projct Folder: C:\Users\...\source\repos\Py-PSO-Imp
Startup File: Py_PSO_Imp.py
Project file: Py-PSO-Imp.sln

Download Python Project : Py-PSO-Imp.zip - (70.0 KB zip file) Download
Project consist of:
One Python Form - (Py_PSO_Imp.py )

Download Python Project : Py-PSO-Imp.zip - (70.0 KB zip file) Download

Install numpy and matplotlib in Python on Windows Operating System, Using Command Prompt

1- Install numpy in Python on Windows Operating System, Using Command Prompt

pip install numpy

2- Install matplotlib in Python on Windows Operating System, Using Command Prompt

pip install matplotlib

Source Code of Python Project Py-PSO-Imp:

Download this Source Code at python file: Py_PSO_Imp.py - (800 Byte Python file) download

Continue

Py-PSO-Imp Python program, the Implementation of the PSO at the Machine Learning

import numpy as np

import matplotlib.pyplot as plt

def f(x,y):

"Objective function"

return (x-3.14)**2 + (y-2.72)**2 + np.sin(3*x+1.41) + np.sin(4*y-1.73)

# Contour plot: With the global minimum showed as "X" on the plot

x, y = np.array(np.meshgrid(np.linspace(0,5,100), np.linspace(0,5,100)))

z = f(x, y)

print("f(x, y) == ", z)

x_min = x.ravel()[z.argmin()]

y_min = y.ravel()[z.argmin()]

plt.figure(figsize=(8,6))

plt.title("Contour plot: With the global minimum showed")

plt.imshow(z, extent=[0, 5, 0, 5], origin='lower', cmap='viridis', alpha=0.5)

plt.colorbar()

plt.plot([x_min], [y_min], marker='x', markersize=5, color="white")

contours = plt.contour(x, y, z, 10, colors='black', alpha=0.4)

plt.clabel(contours, inline=True, fontsize=8, fmt="%.0f")

plt.show()

Debug of Python Project Py-PSO-Imp, 0 Errors

Output of Python Project Py-PSO-Imp:

1-Info:

2- the Plot -plt.show

Py-PSO Python program, PSO - the Particle swarm optimization animate

Py-PSO Python program, PSO - the Particle swarm optimization animate
1- Print Info ...
2- Steps of PSO: algorithm update and save or Plot it, Imge fille - PSO.gif

Create a new Python project In Visual Studio 2022

the Microsoft Visual Studio (... or 2019 or 2022) is a powerful IDE for Python language
Open/Run Microsoft Visual Studio 2022

To view Python templates, search for python.

Select the Python Application template, and select Next.

Create a new Python project In Visual Studio 2022
        On the Configure your new project screen -   (specify a name and file location for the project, and then select Create)
        Project name: Py-PSO
        Location: C:\Users\...\source\repos    (default location for Visual Studio 2022)

The new project opens in Visual Studio 2022 - (Visual Studio 2022 Compiler - IDE, to compile Python project / file )

The Visual Studio Solution Explorer window shows the project structure

Python Project Properieties - Py-PSO

Projct Folder: C:\Users\...\source\repos\Py-PSO
Startup File: Py_PSO.py
Project file: Py-PSO.sln

Download Python Project : Py-PSO.zip - (5.71 KB zip file) Download
Project consist of:
One Python Form - (Py_PSO.py )

Download Python Project : Py-PSO.zip - (5.71 KB zip file) Download

Install numpy and matplotlib in Python on Windows Operating System, Using Command Prompt

1- Install numpy in Python on Windows Operating System, Using Command Prompt

pip install numpy

2- Install matplotlib in Python on Windows Operating System, Using Command Prompt

pip install matplotlib

Source Code of Python Py-PSO project:

Download this Source Code at python file: Py_PSO.py - (3.27 KB Python file) download

Continue

Py-PSO Python program, PSO - the Particle swarm optimization animate - (python file :Py_PSO.py)

import sys

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.animation import FuncAnimation

def f(x,y):

#"Objective function"

return (x-3.14)**2 + (y-2.72)**2 + np.sin(3*x+1.41) + np.sin(4*y-1.73)

# Compute and plot the function in 3D within [0,5]x[0,5]

x, y = np.array(np.meshgrid(np.linspace(0,5,100), np.linspace(0,5,100)))

z = f(x, y)

# Find the global minimum

x_min = x.ravel()[z.argmin()]

y_min = y.ravel()[z.argmin()]

# Hyper-parameter of the algorithm

c1 = c2 = 0.1

w = 0.8

# Create particles

n_particles = 20

np.random.seed(100)

X = np.random.rand(2, n_particles) * 5

V = np.random.randn(2, n_particles) * 0.1

# Initialize data

pbest = X

pbest_obj = f(X[0], X[1])

gbest = pbest[:, pbest_obj.argmin()]

gbest_obj = pbest_obj.min()

def update():

#"Function to do one iteration of particle swarm optimization"

global V, X, pbest, pbest_obj, gbest, gbest_obj

# Update params

r1, r2 = np.random.rand(2)

V = w * V + c1*r1*(pbest - X) + c2*r2*(gbest.reshape(-1,1)-X)

X = X + V

obj = f(X[0], X[1])

pbest[:, (pbest_obj >= obj)] = X[:, (pbest_obj >= obj)]

pbest_obj = np.array([pbest_obj, obj]).min(axis=0)

gbest = pbest[:, pbest_obj.argmin()]

gbest_obj = pbest_obj.min()

# Set up base figure: The contour map

fig, ax = plt.subplots(figsize=(8,6))

fig.set_tight_layout(True)

img = ax.imshow(z, extent=[0, 5, 0, 5], origin='lower', cmap='viridis', alpha=0.5)

fig.colorbar(img, ax=ax)

ax.plot([x_min], [y_min], marker='x', markersize=5, color="white")

contours = ax.contour(x, y, z, 10, colors='black', alpha=0.4)

ax.clabel(contours, inline=True, fontsize=8, fmt="%.0f")

pbest_plot = ax.scatter(pbest[0], pbest[1], marker='o', color='black', alpha=0.5)

p_plot = ax.scatter(X[0], X[1], marker='o', color='blue', alpha=0.5)

p_arrow = ax.quiver(X[0], X[1], V[0], V[1], color='blue', width=0.005,

angles='xy', scale_units='xy', scale=1)

gbest_plot = plt.scatter([gbest[0]], [gbest[1]], marker='*', s=100, color='black',

alpha=0.4)

ax.set_xlim([0,5])

ax.set_ylim([0,5])

def animate(i):

#"Steps of PSO: algorithm update and save or Plot it"

title = 'Steps of PSO {:02d}'.format(i)

# Update params

update()

# Set picture

ax.set_title(title)

pbest_plot.set_offsets(pbest.T)

p_plot.set_offsets(X.T)

p_arrow.set_offsets(X.T)

p_arrow.set_UVC(V[0], V[1])

gbest_plot.set_offsets(gbest.reshape(1,-1))

return ax, pbest_plot, p_plot, p_arrow, gbest_plot

print("PSO found best solution at f({})={}".format(gbest, gbest_obj))

print("Global optimal at f({})={}".format([x_min,y_min], f(x_min,y_min)))

print("len(sys.argv) = ", len(sys.argv))

print("sys.argv[1] = ", sys.argv[0])

#-------------

if __name__ == '__main__':

# FuncAnimation will call the 'update' function for each frame; here

# animating over 10 frames, with an interval of 200ms between frames.

anim = FuncAnimation(fig, animate,

frames=list(range(1,50)), interval=500, blit=False, repeat=True)

if len(sys.argv) == 1:

anim.save("PSO.gif", dpi=120, writer="imagemagick")

print("PSO.gif - saved ")

else:

# plt.show() will just loop the animation forever.

plt.show()

print("PSO.gif - show/plot ")

Debug of Py-PSO project, 0 Errors

Output of Py-PSO project:

1-Info:

2- PSO.gif file saved

Py-PSO-animate-window Python program, the Particle swarm optimization animate Form

Display the Values of the Statistical Hypothesis Tests
Particle Swarm Optimization - PSO:
* the Image Saved - PSO-animate-w.gif
* Show/Plot the image, PSO-animate-w.gif

Create a new Python project In Visual Studio 2022

the Microsoft Visual Studio (... or 2019 or 2022) is a powerful IDE for Python language
Open/Run Microsoft Visual Studio 2022

To view Python templates, search for python.

Select the Python Application template, and select Next.

Create a new Python project In Visual Studio 2022
        On the Configure your new project screen -   (specify a name and file location for the project, and then select Create)
        Project name: Py-PSO-animate-window
        Location: C:\Users\...\source\repos    (default location for Visual Studio 2022)

The new project opens in Visual Studio 2022 - (Visual Studio 2022 Compiler - IDE, to compile Python project / file )

The Visual Studio Solution Explorer window shows the project structure

Python Project Properieties - Py-PSO-animate-window

Projct Folder: C:\Users\...\source\repos\Py-PSO-animate-window
Startup File: Py_PSO_animate_window
Project file: Py-PSO-animate-window.sln

Download Python Project : Py-PSO-animate-window.zip - (5.67 KB zip file) Download
Project consist of:
One Python Form - (Py_PSO_animate_window )

Download Python Project : Py-PSO-animate-window.zip - (5.67 KB zip file) Download

Install numpy, matplotlib and statsmodels in Python on Windows Operating System, Using Command Prompt

1- Install numpy in Python on Windows Operating System, Using Command Prompt

pip install numpy

2- Install matplotlib in Python on Windows Operating System, Using Command Prompt

pip install matplotlib

3- Install statsmodels in Python on Windows Operating System, Using Command Prompt - pip install statsmodels

Source Code, Py-PSO-animate-window project:

Download this Source Code at python file: Py_PSO_animate_window - (12.3 KB Python file) download

Continue

Py-PSO-animate-window Python program, the Particle swarm optimization animate Form - (python file :Py_PSO_animate_window)


	# This Python file uses the following encoding: utf-8 import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation import tkinter as tk from tkinter import * def on_closing(): root.destroy() root = Tk() root.title("The Machine Learning, Particle Swarm Optimization - PSO, Window Form") root.eval('tk::PlaceWindow . center') window_height = 700 window_width = 1000 screen_width = root.winfo_screenwidth() screen_height = root.winfo_screenheight() x_cordinate = int((screen_width/2) - (window_width/2)) y_cordinate = int((screen_height/2) - (window_height/2)) root.geometry("{}x{}+{}+{}".format(window_width, window_height, x_cordinate, y_cordinate)) root.resizable(0, 0) #Labels lbselected1=Label(root,text="1- Statistical Hypothesis Tests",font=("Times New Roman", 12,"bold"), foreground="#000080") lbselected1.place(x=17, y=3) #lbselected2=Label(root,text=" -------------------------------------------------------------",font=("Times New Roman", 10), foreground="#000080") #lbselected2.place(x=17, y=24) lbline1=Label(root,text="you will discover estimation statistics that may be used as an alternative to statistical hypothesis tests.",font=("Times New Roman", 10)) lbline1.place(x=17, y=30) lbline2=Label(root,text="Statistical hypothesis tests can be used to indicate whether the difference between two samples is due to random chance, but cannot comment on the size of the difference.",font=("Times New Roman", 10)) lbline2.place(x=17, y=50) lbline3=Label(root,text="A group of methods referred to as \textit{new statistics} are seeing increased use instead",font=("Times New Roman", 10)) lbline3.place(x=17, y=70) lbline4=Label(root,text=" of or in addition to p-values in order to quantify the magnitude of effects and the amount of uncertainty for ",font=("Times New Roman", 10)) lbline4.place(x=17, y=90) lbline4a=Label(root,text=" estimated values. This group of statistical methods is referred to as estimation statistics.",font=("Times New Roman", 10)) lbline4a.place(x=17, y=110) lbline5=Label(root,text="Estimation statistics is a term to describe three main classes of methods. The three main classes of methods include:",font=("Times New Roman", 10)) lbline5.place(x=17, y=130) lbline6=Label(root,text=" 1- Effect Size. Methods for quantifying the size of an effect given a treatment or intervention.",font=("Times New Roman", 10)) lbline6.place(x=17, y=150) lbline7=Label(root,text=" 2- Interval Estimation. Methods for quantifying the amount of uncertainty in a value.",font=("Times New Roman", 10)) lbline7.place(x=17, y=170) lbline8=Label(root,text=" 3- Meta-Analysis. Methods for quantifying the findings across multiple similar studies.",font=("Times New Roman", 10)) lbline8.place(x=17, y=190) lbline9=Label(root,text="Of the three, perhaps the most useful methods in applied machine learning are interval estimation.",font=("Times New Roman", 10)) lbline9.place(x=17, y=210) lbline10=Label(root,text="There are three main types of intervals. They are:",font=("Times New Roman", 10)) lbline10.place(x=17, y=230) lbline11=Label(root,text=" 1- Tolerance Interval: The bounds or coverage of a proportion of a distribution with a specific level of confidence.",font=("Times New Roman", 10)) lbline11.place(x=17, y=250) lbline12=Label(root,text=" 2- Confidence Interval: The bounds on the estimate of a population parameter.",font=("Times New Roman", 10)) lbline12.place(x=17, y=270) lbline13=Label(root,text=" 3- Prediction Interval: The bounds on a single observation.",font=("Times New Roman", 10)) lbline13.place(x=17, y=290) lbline14=Label(root,text="A simple way to calculate a confidence interval for a classification algorithm is to calculate the binomial proportion confidence interval,",font=("Times New Roman", 10)) lbline14.place(x=17, y=310) lbline15=Label(root,text=" which can provide an interval around a model's estimated accuracy or error.",font=("Times New Roman", 10)) lbline15.place(x=17, y=330) lbline16=Label(root,text="This can be implemented in Python using the conf_int() statsmodels function.",font=("Times New Roman", 10)) lbline16.place(x=17, y=350) lbline17=Label(root,text="The function takes the count of successes (or failures), the total number of trials, and the significance level as arguments and returns ",font=("Times New Roman", 10)) lbline17.place(x=17, y=370) lbline18=Label(root,text=" the lower and upper bound of the confidence interval.",font=("Times New Roman", 10)) lbline18.place(x=17, y=390) lblinex=Label(root,text="------------------------------------------",font=("Times New Roman", 12)) lblinex.place(x=350, y=410) lbline19=Label(root,text="The example below demonstrates this function in a hypothetical case where a model made 88 correct predictions out of a dataset ",font=("Times New Roman", 10)) lbline19.place(x=17, y=430) lbline20=Label(root,text=" with 100 instances and we are interested in the 95% confidence interval (provided to the function as a significance of 0.05).",font=("Times New Roman", 10)) lbline20.place(x=17, y=450) #The example below demonstrates this function in a hypothetical case where a model made 88 correct predictions out of a dataset with 100 instances and we are interested in the 95% confidence interval (provided to the function as a significance of 0.05). #Run the example and review the confidence interval on the estimated accuracy. # Display the Code, to Calculate the Confidence Interval Frame1 = tk.Frame(root) Frame1.place(relx=0.01, rely=0.67, relheight=0.16, relwidth=0.48) Frame1.configure(relief="groove", borderwidth="0", background="#ffffff", highlightbackground="#ffffff", highlightcolor="black", width=870) Text11 = tk.Text(Frame1, borderwidth="0") # to Calculate the Confidence Interval # Create label l = Label(Frame1, text = "the Code, to Calculate the Confidence Interval", background="#ffffff") l.config(font =("Times New Roman", 12)) Fact = """from statsmodels.stats.proportion import proportion_confint # calculate the interval lower, upper = proportion_confint(88, 100, 0.05) print('lower=%.3f, upper=%.3f' % (lower, upper))""" #Run the example and review the confidence interval on the estimated accuracy. l.pack() Text11.pack() # Insert The Fact. Text11.insert(tk.END, Fact) Frame2 = tk.Frame(root) Frame2.place(relx=0.52, rely=0.67, relheight=0.16, relwidth=0.47) Frame2.configure(relief="groove", borderwidth="0", background="#ffffff", highlightbackground="#ffffff", highlightcolor="black", width=870) #The example below demonstrates this function in a hypothetical case where a model made 88 correct predictions out of a dataset with 100 instances and we are interested in the 95% confidence interval (provided to the function as a significance of 0.05). #Run the example and review the confidence interval on the estimated accuracy. # to Calculate Correlation Coefficient from statsmodels.stats.proportion import proportion_confint # calculate the interval lower, upper = proportion_confint(88, 100, 0.05) print('lower=%.3f, upper=%.3f' % (lower, upper)) # Create label l2 = Label(Frame2, text = "the Values to Calculate the Confidence Interval", background="#ffffff") l2.config(font =("Times New Roman", 12)) l2.pack() lower1 = tk.StringVar() upper1 = tk.StringVar() #lower, upper = proportion_confint(88, 100, 0.05) lower1 = lower upper1 = upper mylabellower0 = Label(Frame2, text ="Lower =", font=("Times New Roman", 12), relief = FLAT, background="#ffffff") mylabellower0.place(x=40, y=40) mylabellower1 = tk.Label(Frame2, width=20, text=lower1) mylabellower1.place(x=170, y=40) mylabelupper0 = Label(Frame2, text ="Upper =", font=("Times New Roman", 12), relief = FLAT, background="#ffffff") mylabelupper0.place(x=40, y=80) mylabelupper1 = tk.Label(Frame2, width=20, text=upper1) mylabelupper1.place(x=170, y=80) import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation import tkinter as tk from tkinter import * def f (x,y): #"Objective function" return (x-3.14 )2 + (y-2.72 )2 + np.sin(3x+1.41) + np.sin(4y-1.73) # Compute and plot the function in 3D within [0,5]x[0,5] x, y = np.array(np.meshgrid(np.linspace(0 ,5 ,100 ), np.linspace(0 ,5 ,100 ))) z = f(x, y) # Find the global minimum x_min = x . ravel()[z.argmin()] y_min = y . ravel()[z.argmin()] # Hyper-parameter of the algorithm c1 = c2 = 0.1 w = 0.8 # Create particles n_particles = 20 np . random .seed(100 ) X = np . random . rand(2, n_particles) * 5 V = np . random . randn(2, n_particles) * 0.1 # Initialize data pbest = X pbest_obj = f(X[0], X[1]) gbest = pbest[:, pbest_obj.argmin()] gbest_obj = pbest_obj.min() def update(): #"Function to do one iteration of particle swarm optimization" global V, X, pbest, pbest_obj, gbest, gbest_obj # Update params r1, r2 = np . random . rand(2 ) V = w * V + c1r1(pbest - X) + c2r2(gbest. reshape(-1 ,1 )-X) X = X + V obj = f(X[0], X[1]) pbest[:, (pbest_obj >= obj)] = X[:, (pbest_obj >= obj)] pbest_obj = np .array([pbest_obj, obj]) .min(axis=0 ) gbest = pbest[:, pbest_obj.argmin()] gbest_obj = pbest_obj.min() # Set up base figure: The contour map fig, ax = plt.subplots(figsize= (8 ,6 )) fig.set_tight_layout(True ) img = ax .imshow(z, extent= [0 , 5 , 0 , 5 ], origin= 'lower' , cmap= 'viridis' , alpha=0.5 ) fig.colorbar(img, ax=ax) ax .plot([x_min], [y_min], marker= 'x' , markersize=5 , color="white" ) contours = ax .contour(x, y, z, 10 , colors= 'black' , alpha=0.4 ) ax .clabel(contours, inline=True , fontsize=8 , fmt="%.0f" ) pbest_plot = ax .scatter(pbest[0 ], pbest[1 ], marker= 'o' , color= 'black' , alpha=0.5 ) p_plot = ax .scatter(X[0], X[1], marker= 'o' , color= 'blue' , alpha=0.5 ) p_arrow = ax .quiver(X[0], X[1], V[0], V[1], color= 'blue' , width=0.005 , angles= 'xy' , scale_units= 'xy' , scale=1 ) gbest_plot = plt.scatter([gbest[0 ]], [gbest[1 ]], marker= '*' , s=100, color= 'black' , alpha=0.4 ) ax .set_xlim([0 ,5 ]) ax .set_ylim([0 ,5 ]) def animate (i): #"Steps of PSO animate: algorithm update and show in plot" title = 'PSO Animate Window {:02d} ' .format(i) # Update params update() # Set picture ax . set_title(title) pbest_plot. set_offsets(pbest.T) p_plot. set_offsets(X.T) p_arrow . set_offsets(X.T) p_arrow . set_UVC(V[0], V[1]) gbest_plot. set_offsets(gbest. reshape(1 ,-1 )) return ax, pbest_plot, p_plot, p_arrow, gbest_plot ##### #1-anim .save() will just loop the animation forever. def AnimSav(): anim = FuncAnimation(fig, animate, frames= list ( range (1 ,50 )), interval=500 , blit=False, repeat=False ) #repeat=True ) anim .save("PSO-animate-w.gif", dpi=120 , writer="imagemagick") #2-plt.show() will just loop the animation forever. def AnimShow(): anim = FuncAnimation(fig, animate, frames= list ( range (1 ,50 )), interval=500 , blit=False, repeat=False ) #repeat=True ) # 2- plt.show() will just loop the animation forever. plt.show() lbselectedpso=Label(root,text="2- Particle Swarm Optimization - PSO:",font=("Times New Roman", 12,"bold"), foreground="#000080") lbselectedpso.place(x=17, y=585) lbselectedpso=Label(root,text=" It Is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.",font=("Times New Roman", 10,"bold"), foreground="#000080") lbselectedpso.place(x=17, y=605) option= Button(root, text ="Save the image, PSO-animate-w.gif", command = AnimSav, bd =1, font=("Times New Roman",12), bg = "#118ab2",fg="#eae2b7") option.place(x=40, y=630, width = 500, height = 25) option1= Button(root, text ="Show/Plot the image, PSO-animate-w.gif", command = AnimShow, bd =1, font=("Times New Roman",12), bg = "#118ab2",fg="#eae2b7") option1.place(x=40, y=670, width = 500, height = 25) #Button close = Button(root, text = "Done", command = on_closing) close.place(x=850, y=650, height = 30, width = 120) root.mainloop()

Debug Py-PSO-animate-window project, 0 Errors

Output of Py-PSO-animate-window project

1- Py-PSO-animate-window Form

2- the Image Saved ,PSO-animate-w.gif at C:\Users\...\source\repos\Py-PSO-animate-window\Py-PSO-animate-window

3- Show/Plot the image, PSO-animate-w.gif

4- Resultat of Print Values of Output Py-PSO-animate-window project

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PLAY, 5 GAMES	Andree Zouein Foundation	GIGI L'AMOROSO Fashion and Jewellery	Josette Zoueïn. Psychanalyste Psychologue
Multi-Functional 4 Wheel Drive Robot Car	CARLOS SLIM HELU Site, New Design	Saadeh Bejjane Architecture	Hermez Steel, Steel Decor Artwork

Chucri Simon Zouein, Computer engineer
echkzouein@gmail.com