5

 

import numpy as np

import matplotlib.pyplot as plt

from collections import Counter

# Generate random data and labels

data = np.random.rand(100)

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

# Euclidean distance function

def euclidean_distance(x1, x2):

return abs(x1 - x2)

# k-NN classifier

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()

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