mirror of
https://github.com/TheAlgorithms/Python.git
synced 2025-03-15 02:59:50 +00:00
150 lines
4.6 KiB
Python
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()
|