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()