2024-10-01 15:27:03 +00:00

150 lines
4.6 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]]:
"""
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()