Mms

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

# Given dataset

data = np.array([

    [1.0, 2.0],

    [1.5, 1.8],

    [5.0, 8.0],

    [8.0, 8.0],

    [1.0, 0.6],

    [9.0, 11.0],

    [8.0, 2.0],

    [10.0, 2.0],

    [9.0, 3.0]

])

# Perform K-Means clustering with 3 clusters

kmeans = KMeans(n_clusters=3, random_state=42)

kmeans.fit(data)

labels = kmeans.labels_

centroids = kmeans.cluster_centers_

# Visualize the clustered data points and centroids

plt.figure(figsize=(8, 6))

colors = ['red', 'blue', 'green']

# Plot data points with cluster colors

for i in range(len(data)):

    plt.scatter(data[i, 0], data[i, 1], color=colors[labels[i]], s=100)

# Plot centroids

plt.scatter(centroids[:, 0], centroids[:, 1], marker='X', s=200, color='black', linewidths=2)

# Add labels and title

plt.title('K-Means Clustering Results', fontsize=14)

plt.xlabel('X-axis', fontsize=12)

plt.ylabel('Y-axis', fontsize=12)

# Add grid and show plot

plt.grid(True, linestyle='--', alpha=0.7)

plt.show()

# Print cluster assignments and centroids

print("Cluster assignments:")

for i, point in enumerate(data):

    print(f"Point {point} belongs to Cluster {labels[i]}")

print("\nCentroids:")

for i, centroid in enumerate(centroids):

    print(f"Cluster {i} centroid: {centroid}")

2:

import numpy as np

# Create ndarrays of different data types

print("Creating ndarrays with different data types:")

# Integer array

int_array = np.array([1, 2, 3, 4, 5], dtype=np.int32)

print(f"Integer array: {int_array}, dtype: {int_array.dtype}")

# Float array

float_array = np.array([1.1, 2.2, 3.3, 4.4, 5.5], dtype=np.float64)

print(f"Float array: {float_array}, dtype: {float_array.dtype}")

# Boolean array

bool_array = np.array([True, False, True, False], dtype=np.bool_)

print(f"Boolean array: {bool_array}, dtype: {bool_array.dtype}")

# String array

str_array = np.array(['apple', 'banana', 'cherry'], dtype=np.str_)

print(f"String array: {str_array}, dtype: {str_array.dtype}\n")

# Create a 2D array for demonstration

matrix = np.array([

    [1, 2, 3, 4],

    [5, 6, 7, 8],

    [9, 10, 11, 12]

])

print("Original 2D array:")

print(matrix)

# Basic indexing

print("\nBasic indexing examples:")

print("Element at row 1, column 2:", matrix[1, 2]) # Returns 7

print("Entire row 0:", matrix[0]) # Returns [1, 2, 3, 4]

print("Entire column 1:", matrix[:, 1]) # Returns [2, 6, 10]

# Slicing examples

print("\nSlicing examples:")

print("First two rows:")

print(matrix[:2]) # Returns rows 0 and 1

print("\nLast two columns:")

print(matrix[:, -2:]) # Returns last two columns

print("\nSub-matrix (rows 1-2, columns 1-3):")

print(matrix[1:3, 1:3]) # Returns [[6, 7], [10, 11]]

# Fancy indexing

print("\nFancy indexing (selecting specific rows and columns):")

print("Rows 0 and 2, columns 1 and 3:")

print(matrix[[0, 2]][:, [1, 3]]) # Returns [[2, 4], [10, 12]]

# Boolean indexing

print("\nBoolean indexing (elements greater than 5):")

print(matrix[matrix > 5]) # Returns [6, 7, 8, 9, 10, 11, 12]

,,,

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

# 1. Sample data

data = np.array([

    [1.0, 2.0], [1.5, 1.8], [5.0, 8.0],

    [8.0, 8.0], [1.0, 0.6], [9.0, 11.0],

    [8.0, 2.0], [10.0, 2.0], [9.0, 3.0]

])

# 2. Apply KMeans

kmeans = KMeans(n_clusters=3, random_state=0).fit(data)

labels = kmeans.labels_

centroids = kmeans.cluster_centers_

# 3. Plot clusters and centroids

plt.scatter(data[:, 0], data[:, 1], c=labels, s=100, cmap='viridis', label='Points')

plt.scatter(centroids[:, 0], centroids[:, 1], c='black', s=200, marker='X', label='Centroids')

plt.title('K-Means Clustering')

plt.legend()

plt.grid(True)

plt.show()

# 4. Print results

for i, point in enumerate(data):

    print(f"Point {point} → Cluster {labels[i]}")

print("\nCentroids:")

for i, c in enumerate(centroids):

    print(f"Cluster {i}: {c}")

import numpy as np

import matplotlib.pyplot as plt

from sklearn.decomposition import PCA

# Step 1: Create a custom 3D dataset

data = np.array([

    [2.5, 2.4, 1.2],

    [0.5, 0.7, 0.4],

    [2.2, 2.9, 1.5],

    [1.9, 2.2, 1.1],

    [3.1, 3.0, 1.8],

    [2.3, 2.7, 1.4],

    [2.0, 1.6, 0.9],

    [1.0, 1.1, 0.3],

    [1.5, 1.6, 0.7],

    [1.1, 0.9, 0.2]

])

# Step 2: Apply PCA (3D to 2D)

pca = PCA(n_components=2)

reduced_data = pca.fit_transform(data)

# Step 3: Visualize the 2D result

plt.figure(figsize=(8, 6))

plt.scatter(reduced_data[:, 0], reduced_data[:, 1], color='teal', s=100)

plt.title("PCA - Dimensionality Reduction (3D to 2D)", fontsize=14)

plt.xlabel("Principal Component 1", fontsize=12)

plt.ylabel("Principal Component 2", fontsize=12)

plt.grid(True, linestyle='--', alpha=0.6)

plt.show()

# Step 4: Print details

print("Explained Variance Ratio:", pca.explained_variance_ratio_)

print("\nReduced Data:\n", reduced_data)