import hashlib

data ="Hello,world!".encode()
hash_value = hashlib.sha256(data).hexdigest()

print("SHA-256 Hash:", hash_value)

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report

iris=load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

y_pred=model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))

import numpy as np

def initialize_centroids(X, k):
    indices = np.random.choice(len(X), size=k, replace=False)
    return X[indices]

def assign_clusters(X,centroids):
    distances = np.linalg.norm(X[:, np.newaxis] -centroids, axis=2)
    return np.argmin(distances, axis=1)

def update_centroids(X, labels, k):
    return np.array([X[labels== i].mean(axis=0) for i in range(k)])

def has_converged(old_centroids, new_centroids, tolerance=1e-4):
    shifts = np.linalg.norm(new_centroids - old_centroids, axis=1)
    return np.all(shifts < tolerance)

def kmeans(X, k, max_iterations=100):
    centroids=initialize_centroids(X, k)

    for _ in range(max_iterations):
        labels = assign_clusters(X, centroids)
        new_centroids = update_centroids(X, labels, k)

        if has_converged(centroids, new_centroids):
            break

        centroids=new_centroids

    return centroids, labels

import numpy as np
from collections import Counter

def euclidean_distance(a, b):
    return np.sqrt(np.sum((a-b) ** 2))

class KNN:
    def __init__(self, k=3):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        return [self._predict(x) for x in X]

    def _predict(self, x):
        
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        
        k_indics = np.argsort(distances)[:self.k]
        
        k_nearest_labels = [self.y_train[i] for i in k_indics]
 
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]

import random

def fitness(x):
    return x**2


def decode(chromosome, a, b, bits):
    value =int(chromosome, 2)
    return a + (b - a) * value / (2**bits - 1)


def random_chromosome(bits):
    return ''.join(random.choice('01') for_in range(bits))


def selection(population, scores):
    k = 3 
    selected = random.choices(list(zip(population, scores)), k=k)
    selected.sort(key=lambda x: x[1], reverse=True)
    return selected[0][0]

def crossover(p1, p2, rate=0.7):
    if random.random() < rate:
        point = random.randint(1, len(p1) - 1)
        return p1[:point] + p2[point:], p2[:point] + p1[point:]
    return p1, p2


def mutate(chromosome, rate=0.01):
    return ''.join(
        bit if random.random() > rate else ('1' if bit == '0' else '0')
        for bit in chromosome
    )


bits = 10
bounds = (-10, 10)
population_size = 20
generations = 50

population = [random_chromosome(bits) for _ in range(population_size)]


for gen in range(generations):
    decoded = [decode(c, *bounds, bits) for c in population]
    scores=[fitness(d) for d in decoded]
    
    
    best_idx = scores.index(max(scores))
    print(f"{gen}: x = {decoded[best_idx]:.3f}, f(x) = {scores[best_idx]:.3f}")
    
    
    select = [selection(population, scores) for _ in range(population_size)]
    
    
    children = [ ]
    for i in range(0, population_size, 2):
        p1, p2 = select[i], select[i+1]
        c1, c2 =crossover(p1, p2)
        c1, c2 =mutate(c1), mutate(c2)
        children.extend([c1, c2])
    
    population = children