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Update dbscan.py

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tkgowtham 2024-10-01 21:13:40 +05:30 committed by GitHub
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machine_learning/dbscan.py
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@ -1,11 +1,9 @@
import pandas as pd
import math
import matplotlib.pyplot as plt
from typing import Dict, List
class dbscan:
"""
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
@ -20,20 +18,22 @@ class dbscan:
To create a object
------------------
import dbscan
obj = dbscan.dbscan(minPts, radius, file)
obj = dbscan.dbscan(minpts, radius, file)
obj.print_dbscan()
obj.plot_dbscan()
"""
def __init__(self, minPts: int, radius: int, file: str) -> None:
"""
'''
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.
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
@ -51,99 +51,77 @@ class dbscan:
6 | 4
7 | 3
-----
"""
self.minPts = minPts
'''
self.minpts = minpts
self.radius = radius
self.file = file
self.dict1 = self.perform_dbscan()
def perform_dbscan(self) -> dict[int, list[int]]:
'''
>>>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
"""
Dictionary with points and the list of points
that lie in its radius
'''
data = pd.read_csv(self.file)
minPts = self.minPts
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)
)
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)
if i+1 in dict1:
dict1[i+1].append(j+1)
else:
dict1[i + 1] = [
j + 1,
]
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(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]
):
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.
"""
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
)
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",
)
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.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()