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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 pandas as pd
import math import math
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from typing import Dict, List from typing import dict, list
class dbScan:
'''
class dbscan:
"""
DBSCAN Algorithm : DBSCAN Algorithm :
Density-Based Spatial Clustering Of Applications With Noise Density-Based Spatial Clustering Of Applications With Noise
Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
@ -20,20 +18,22 @@ class dbscan:
To create a object To create a object
------------------ ------------------
import dbscan import dbscan
obj = dbscan.dbscan(minPts, radius, file) obj = dbscan.dbscan(minpts, radius, file)
obj.print_dbscan() obj.print_dbscan()
obj.plot_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 Constructor
Attributes: Attributes:
----------- -----------
minPts (int) : Minimum number of points needed to be within the radius to considered as core minpts (int) : Minimum number of points needed to be
radius (int) : The radius from a given core point where other core points can be considered as core within the radius to considered as core
file (csv) : CSV file location. Should contain x and y coordinate value for each point. 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 : Example :
minPts = 4 minPts = 4
@ -51,99 +51,77 @@ class dbscan:
6 | 4 6 | 4
7 | 3 7 | 3
----- -----
""" '''
self.minPts = minPts self.minpts = minpts
self.radius = radius self.radius = radius
self.file = file self.file = file
self.dict1 = self.perform_dbscan() self.dict1 = self.perform_dbscan()
def perform_dbscan(self) -> dict[int, list[int]]:
'''
>>>perform_dbscan()
def perform_dbscan(self) -> Dict[int, List[int]]:
"""
Parameters: Parameters:
----------- -----------
None None
Return: 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) data = pd.read_csv(self.file)
minPts = self.minPts minpts = self.minpts
e = self.radius e = self.radius
dict1 = {} dict1 = {}
for i in range(len(data)): for i in range(len(data)):
for j in range(len(data)): for j in range(len(data)):
dist = math.sqrt( dist = math.sqrt(pow(data['x'][j] - data['x'][i],2) + pow(data['y'][j] - data['y'][i],2))
pow(data["x"][j] - data["x"][i], 2)
+ pow(data["y"][j] - data["y"][i], 2)
)
if dist < e: if dist < e:
if i + 1 in dict1: if i+1 in dict1:
dict1[i + 1].append(j + 1) dict1[i+1].append(j+1)
else: else:
dict1[i + 1] = [ dict1[i+1] = [j+1,]
j + 1,
]
return dict1 return dict1
def print_dbscan(self) -> None: def print_dbscan(self) -> None:
""" '''
Outputs: Outputs:
-------- --------
Prints each point and if it is a core or a noise (w/ border) Prints each point and if it is a core or a noise (w/ border)
""" '''
for i in self.dict1: for i in self.dict1:
print(i, " ", self.dict1[i], end=" ---> ") print(i," ",self.dict1[i], end=' ---> ')
if len(self.dict1[i]) >= self.minPts: if len(self.dict1[i]) >= self.minpts:
print("Core") print("Core")
else: else:
for j in self.dict1: for j in self.dict1:
if ( if i != j and len(self.dict1[j]) >= self.minpts and i in self.dict1[j]:
i != j
and len(self.dict1[j]) >= self.minPts
and i in self.dict1[j]
):
print("Noise ---> Border") print("Noise ---> Border")
break break
else: else:
print("Noise") print("Noise")
def plot_dbscan(self) -> None: def plot_dbscan(self) -> None:
""" '''
Output: 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) data = pd.read_csv(self.file)
e = self.radius e = self.radius
for i in self.dict1: for i in self.dict1:
if len(self.dict1[i]) >= self.minPts: if len(self.dict1[i]) >= self.minpts:
plt.scatter(data["x"][i - 1], data["y"][i - 1], color="red") plt.scatter(data['x'][i-1], data['y'][i-1], color='red')
circle = plt.Circle( circle = plt.Circle((data['x'][i-1], data['y'][i-1]), e, color='blue', fill=False)
(data["x"][i - 1], data["y"][i - 1]), e, color="blue", fill=False
)
plt.gca().add_artist(circle) plt.gca().add_artist(circle)
plt.text( plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
data["x"][i - 1],
data["y"][i - 1],
"P" + str(i),
ha="center",
va="bottom",
)
else: else:
plt.scatter(data["x"][i - 1], data["y"][i - 1], color="green") plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
plt.text( plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
data["x"][i - 1], plt.xlabel('X')
data["y"][i - 1], plt.ylabel('Y')
"P" + str(i), plt.title('DBSCAN Clustering')
ha="center", plt.legend(['Core','Noise'])
va="bottom",
)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("DBSCAN Clustering")
plt.legend(["Core", "Noise"])
plt.show() plt.show()