2024-10-01 21:13:40 +05:30

128 lines
4.2 KiB
Python

import pandas as pd
import math
import matplotlib.pyplot as plt
from typing import dict, list
class dbScan:
'''
DBSCAN Algorithm :
Density-Based Spatial Clustering Of Applications With Noise
Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
Functions:
----------
__init__() : Constructor that sets minPts, radius and file
perform_dbscan() : Invoked by constructor and calculates the core and noise points and returns a dictionary.
print_dbscan() : Prints the core and noise points along with stating if the noise are border points or not.
plot_dbscan() : Plots the points to show the core and noise point.
To create a object
------------------
import dbscan
obj = dbscan.dbscan(minpts, radius, file)
obj.print_dbscan()
obj.plot_dbscan()
'''
def __init__(self, minpts : int, radius : int, file : str) -> None:
'''
Constructor
Attributes:
-----------
minpts (int) : Minimum number of points needed to be
within the radius to considered as core
radius (int) : The radius from a given core point where
other core points can be considered as core
file (csv) : CSV file location. Should contain x and y
coordinate value for each point.
Example :
minPts = 4
radius = 1.9
file = 'data_dbscan.csv'
File Structure of CSV Data:
---------------------------
_____
x | y
-----
3 | 7
4 | 6
5 | 5
6 | 4
7 | 3
-----
'''
self.minpts = minpts
self.radius = radius
self.file = file
self.dict1 = self.perform_dbscan()
def perform_dbscan(self) -> dict[int, list[int]]:
'''
>>>perform_dbscan()
Parameters:
-----------
None
Return:
--------
Dictionary with points and the list of points
that lie in its radius
'''
data = pd.read_csv(self.file)
minpts = self.minpts
e = self.radius
dict1 = {}
for i in range(len(data)):
for j in range(len(data)):
dist = math.sqrt(pow(data['x'][j] - data['x'][i],2) + pow(data['y'][j] - data['y'][i],2))
if dist < e:
if i+1 in dict1:
dict1[i+1].append(j+1)
else:
dict1[i+1] = [j+1,]
return dict1
def print_dbscan(self) -> None:
'''
Outputs:
--------
Prints each point and if it is a core or a noise (w/ border)
'''
for i in self.dict1:
print(i," ",self.dict1[i], end=' ---> ')
if len(self.dict1[i]) >= self.minpts:
print("Core")
else:
for j in self.dict1:
if i != j and len(self.dict1[j]) >= self.minpts and i in self.dict1[j]:
print("Noise ---> Border")
break
else:
print("Noise")
def plot_dbscan(self) -> None:
'''
Output:
-------
A matplotlib plot that show points as core and noise along
with the circle that lie within it.
'''
data = pd.read_csv(self.file)
e = self.radius
for i in self.dict1:
if len(self.dict1[i]) >= self.minpts:
plt.scatter(data['x'][i-1], data['y'][i-1], color='red')
circle = plt.Circle((data['x'][i-1], data['y'][i-1]), e, color='blue', fill=False)
plt.gca().add_artist(circle)
plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
else:
plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('DBSCAN Clustering')
plt.legend(['Core','Noise'])
plt.show()