Pgr-5

import numpy as np

import matplotlib.pyplot as plt

from collections import Counter

data = np.random.rand(100)

labels = ["Class1" if x <= 0.5 else "Class2" for x in data[:50]]

def euclidean_distance(x1, x2):

 return abs(x1 - x2)

def knn_classifier(train_data, train_labels, test_point, k):

 distances = [(euclidean_distance(test_point, train_data[i]), train_labels[i]) for i in   range(len(train_data))]

 distances.sort(key=lambda x: x[0])

 k_nearest_neighbors = distances[:k]

 k_nearest_labels = [label for _, label in k_nearest_neighbors]

 return Counter(k_nearest_labels).most_common(1)[0][0]

 train_data = data[:50]

 train_labels = labels

 test_data = data[50:]

 k_values = [1, 2, 3, 4, 5, 20, 30]

 print("--- k-Nearest Neighbors Classification ---")

 print("Training dataset: First 50 points labeled based on the rule (x <= 0.5 -> Class1, x > 0.5 ->

 Class2)")

 print("Testing dataset: Remaining 50 points to be classified\n")

 results = {}

 for k in k_values:
  print(f"Results for k = {k}:")

  classified_labels = [knn_classifier(train_data, train_labels, test_point, k) for test_point in

  test_data]

 results[k] = classified_labels

for i, label in enumerate(classified_labels, start=51):

 print(f"Point x{i} (value: {test_data[i - 51]:.4f}) is classified as {label}")

print("\n")

print("Classification complete.\n")

for k in k_values:

 classified_labels = results[k]

 class1_points = [test_data[i] for i in range(len(test_data)) if classified_labels[i] == "Class1"]

 class2_points = [test_data[i] for i in range(len(test_data)) if classified_labels[i] == "Class2"]

 plt.figure(figsize=(10, 6))

 plt.scatter(train_data, [0] * len(train_data), c=["blue" if label == "Class1" else "red" for label in   train_labels],

 label="Training Data", marker="o")

 plt.scatter(class1_points, [1] * len(class1_points), c="blue", label="Class1 (Test)", marker="x")

 plt.scatter(class2_points, [1] * len(class2_points), c="red", label="Class2 (Test)", marker="x")

 plt.title(f"k-NN Classification Results for k = {k}")

 plt.xlabel("Data Points")

 plt.ylabel("Classification Level")

 plt.legend()

 plt.grid(True)

 plt.show()