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100
machine_learning/dbscan.py
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@ -2,8 +2,10 @@ import math
from typing import dict, list, optional
import matplotlib.pyplot as plt
import pandas as pd
class DbScan:
'''
"""
DBSCAN Algorithm :
Density-Based Spatial Clustering Of Applications With Noise
Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
@ -23,14 +25,28 @@ class DbScan:
obj = dbscan.DbScan(minpts, radius, file)
obj.print_dbscan()
obj.plot_dbscan()
'''
def __init__(self, minpts : int, radius : int, file : optional[str] =
({'x': 3, 'y': 7}, {'x': 4, 'y': 6}, {'x': 5, 'y': 5},
{'x': 6, 'y': 4},{'x': 7, 'y': 3}, {'x': 6, 'y': 2},
{'x': 7, 'y': 2}, {'x': 8, 'y': 4},{'x': 3, 'y': 3},
{'x': 2, 'y': 6}, {'x': 3, 'y': 5}, {'x': 2, 'y': 4})
"""
def __init__(
self,
minpts: int,
radius: int,
file: optional[str] = (
{"x": 3, "y": 7},
{"x": 4, "y": 6},
{"x": 5, "y": 5},
{"x": 6, "y": 4},
{"x": 7, "y": 3},
{"x": 6, "y": 2},
{"x": 7, "y": 2},
{"x": 8, "y": 4},
{"x": 3, "y": 3},
{"x": 2, "y": 6},
{"x": 3, "y": 5},
{"x": 2, "y": 4},
),
) -> None:
'''
"""
Constructor
Args:
@ -58,13 +74,14 @@ class DbScan:
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]]:
'''
"""
Args:
-----------
None
@ -90,7 +107,7 @@ class DbScan:
11 [2, 10, 11, 12]
12 [9, 11, 12]
'''
"""
if type(self.file) is str:
data = pd.read_csv(self.file)
else:
@ -99,16 +116,21 @@ class DbScan:
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)
@ -126,9 +148,9 @@ class DbScan:
10 [1, 10, 11] ---> Noise ---> Border
11 [2, 10, 11, 12] ---> Core
12 [9, 11, 12] ---> Noise ---> Border
'''
"""
for i in self.dict1:
print(i," ",self.dict1[i], end=' ---> ')
print(i, " ", self.dict1[i], end=" ---> ")
if len(self.dict1[i]) >= self.minpts:
print("Core")
else:
@ -142,8 +164,9 @@ class DbScan:
break
else:
print("Noise")
def plot_dbscan(self) -> None:
'''
"""
Output:
-------
A matplotlib plot that show points as core and noise along
@ -151,7 +174,7 @@ class DbScan:
>>> DbScan(4,1.9).plot_dbscan()
Plotted Successfully
'''
"""
if type(self.file) is str:
data = pd.read_csv(self.file)
else:
@ -159,19 +182,30 @@ class DbScan:
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.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()
print("Plotted Successfully")