Python program to find distance measure - Hamming ,Euclidean , Manhattan, Minkowski

# Calculating distance between bit strings

# Hamming Distance
def hamming_distance(a, b):
return sum(abs(e1 - e2) for e1, e2 in zip(a, b)) / len(a)
r1 = [1, 0, 0, 0, 0, 0, 1]
r2 = [1, 0, 0, 0, 0, 1, 0]
dist = hamming_distance(r1, r2)
print(dist)

#Euclidean Distance
from math import sqrt
def euclidean_distance(a, b):
return sqrt(sum((e1-e2)**2 for e1, e2 in zip(a,b)))
r1 = [1, 0, 0, 0, 0, 0, 1]
r2 = [1, 0, 0, 0, 0, 1, 0]
dist = euclidean_distance(r1, r2)
print(dist)

#Manhattan Distance
from math import sqrt
def manhattan_distance(a, b):
return sum(abs(e1-e2) for e1, e2 in zip(a,b))
r1 = [1, 0, 0, 0, 0, 0, 1]
r2 = [1, 0, 0, 0, 0, 1, 0]
dist = manhattan_distance(r1, r2)
print(dist)

#Minkowski Distance
from math import sqrt
def minkowski_distance(a, b, p):
return sum(abs(e1-e2)**p for e1, e2 in zip(a,b))**(1/p)
r1 = [1, 0, 0, 0, 0, 0, 1]
r2 = [1, 0, 0, 0, 0, 1, 0]
dist = minkowski_distance(r1, r2, 1) #  p=1: Manhattan distance.
print(dist)
dist = minkowski_distance(r1, r2, 2) #  p=2: Euclidean distance.
print(dist)

Counter using Tkinter GUI in Python

 import tkinter as tk

counter = 0

def counter_label(label):

def count():

global counter

counter += 1

label.config(text=str(counter))

label.after(1000, count)

count()

root = tk.Tk()

root.title("Counting Seconds")

label = tk.Label(root, fg="green")

label.pack()

counter_label(label)

button = tk.Button(root, text='Stop', width=25, command=root.destroy)

button.pack()

root.mainloop()

Generate random numbers with normal distribution

 1.Generate normal distribution data of Size 2 × 3

   from numpy import random
   x = random.normal(size=(2, 3))

2. Generate normal distribution with mean 250 and standard deviation 10

    from numpy import random
    x = random.normal(loc=250, scale=10, size=(2, 3))

Python Random Module

 import random

random.random() 

random.uniform(1, 10)

random.randint(1, 10)

random.randrange(0, 101, 2)

random.choice('abcdefghij') 

items = [1, 2, 3, 4, 5, 6, 7]

random.shuffle(items)

items

random.sample([1, 2, 3, 4, 5],  3)

Python may get pattern matching syntax

PEP 622 -- Structural Pattern Matching is PEP proposes adding pattern matching statements  to Python in order to create more expressive ways of handling structured heterogeneous data. The authors take a holistic approach, providing both static and runtime specifications.