general_trajectories.py

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

# Load the database from the text file
database = []
with open("released_database.txt", 'r') as input_file:
    for line in input_file:
        parts = line.strip().split(',')
        entry = {
            'id': int(parts[0]),
            'date_time': parts[1],
            'longitude': float(parts[2]),
            'latitude': float(parts[3]),
            'true_count':int(parts[4])
        }
        database.append(entry)

# Extract longitude and latitude into a numpy array
locations = np.array([[entry['longitude'], entry['latitude']] for entry in database])

# Define the number of clusters (k)
k = 20

# Apply k-means clustering
kmeans = KMeans(n_clusters=k)
kmeans.fit(locations)

# Get cluster labels and centroids
cluster_labels = kmeans.labels_
centroids = kmeans.cluster_centers_
# Get cluster labels and centroids
cluster_labels = kmeans.labels_
centroids = kmeans.cluster_centers_
# Update entries with cluster numbers
for i, entry in enumerate(database):
    entry['cluster_number'] = cluster_labels[i]
traj=[]
# Write updated entries back to the text file
with open("output_database_updated.txt", 'w') as output_file:
    for entry in database:
        # print(entry)
        fields = [entry['id'], entry['date_time'], entry['cluster_number'],entry['true_count']]
        # keys_to_skip = ['id', 'time', 'cluster_number', 'longitude', 'latitude','true_count']
        # for key in entry.keys():
        #     if key not in keys_to_skip:
        #         fields.append(entry[key])
        output_file.write(','.join(map(str, fields)) + '\n')

# print("Updated file 'output_database_updated.txt' with cluster numbers.")
# print("Updated file 'output_database_updated.txt' with cluster numbers.")# Plot clusters
plt.figure(figsize=(10, 8))
for i in range(k):
    cluster_points = locations[cluster_labels == i]
    plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {i+1}')

# Plot centroids
plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', color='black', label='Centroids')
truncate_at = 100
# plt.xlim(truncate_at, 130)  # Setting x-axis limits
# plt.ylim(16, 50)  # Setting y-axis limits
# plt.xlabel('Longitude')
# plt.ylabel('Latitude')
# plt.title('K-means Clustering')
# plt.legend()
# plt.grid(True)
# plt.show()
import random
import datetime
import math
def truncate_float(float_number, decimal_places):
    multiplier = 10 ** decimal_places
    return int(float_number * multiplier) / multiplier


# Function to write trajectories to a file
def write_trajectories_to_file(trajectories, filename):
    with open(filename, 'a') as file:
        for trajectory in trajectories:
            for point in trajectory:
                file.write(f"{point['id']},{point['date_time']},{point['cluster_number']},{point['true_count']}\n")

trajectories = []

# Generate 100 trajectories
for i in range(150, 200):
    trajectory_id = i
    trajectory = []
    
    # Randomly select a cluster
    cluster_id = random.randint(0, k - 1)  # k is the number of clusters
    
    # Randomly select a point from the selected cluster
    cluster_points = locations[cluster_labels == cluster_id]
    
    # Ensure that each trajectory has at least 25 points
    while len(trajectory) < random.randint(25,40):
        random_point = random.choice(cluster_points)
        
        # Generate a random time for the point within a day
        random_time = datetime.datetime.now() + datetime.timedelta(seconds=random.randint(0, 86400))
        
        # Append the point with time and trajectory ID
        trajectory.append({
            'id': trajectory_id,
            'date_time': random_time.strftime('%Y-%m-%d %H:%M:%S'),
            'cluster_number':cluster_id,
              'true_count': 0
        })
        trajectories.append(trajectory)
        
    
  
# Write the trajectories to the file
write_trajectories_to_file(trajectories, "released_database.txt")
write_trajectories_to_file(trajectories, "output_database_updated.txt")