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Real-time data clustering with randomly and continuously changing output data

The Legendary Beginner answered on May 23, 2023 Popularity 1/10 Helpfulness 1/10

answer Real-time data clustering with randomly and continuously changing output data

Real-time data clustering with randomly and continuously changing output data

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import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Create an empty array to store the data points
X = np.empty((0, 2))

plt.figure()
plt.ion()  # Turn on interactive mode

# Create an empty scatter plot
scatter = plt.scatter([], [], marker='o', facecolors='none', edgecolors='black', s=80)

# Set initial plot limits and labels
x_min, x_max = 0, 10
y_min, y_max = 0, 10
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.title('Real-time Clustering')
plt.xticks(())
plt.yticks(())

num_clusters = 1  # Set the desired number of clusters (updated to 1)

kmeans = KMeans(init='k-means++', n_clusters=num_clusters, n_init=10)

while True:
    # Generate a random data point
    new_point = np.random.rand(1, 2) * 10
    
    # Add the new point to the data array
    X = np.vstack([X, new_point])
    
    # Perform clustering on the updated data
    kmeans.fit(X)
    
    # Clear the current plot
    scatter.remove()
    
    # Plot the updated data points
    scatter = plt.scatter(X[:, 0], X[:, 1], marker='o', facecolors='none', edgecolors='black', s=80)
    
    # Plot the cluster centers
    cluster_centers = kmeans.cluster_centers_
    plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1], marker='o', s=210, linewidths=4, color='black',
                zorder=12, facecolors='black')
    
    # Update plot limits if necessary
    x_min = min(x_min, new_point[0, 0]) - 1
    x_max = max(x_max, new_point[0, 0]) + 1
    y_min = min(y_min, new_point[0, 1]) - 1
    y_max = max(y_max, new_point[0, 1]) + 1
    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)
    
    # Update the plot
    plt.pause(0.5)

Popularity 1/10 Helpfulness 1/10 Language python

Source: www.packt.com

Tags: output python real-time

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Contributed on May 23 2023

The Legendary Beginner

0 Answers Avg Quality 2/10

Real-time data clustering with randomly and continuously changing output data

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