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machine_learning/dbscan.py
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@ -2,8 +2,10 @@ 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
@ -21,9 +23,10 @@ class 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:
@ -51,13 +54,14 @@ 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]]: def perform_dbscan(self) -> dict[int, list[int]]:
''' """
Parameters: Parameters:
----------- -----------
None None
@ -66,56 +70,81 @@ class DbScan:
-------- --------
Dictionary with points and the list of points Dictionary with points and the list of points
that lie in its radius that lie in its radius
''' """
data = pd.read_csv(self.file) data = pd.read_csv(self.file)
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(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 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] = [j+1,] dict1[i + 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 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") 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 A matplotlib plot that show points as core and noise along
with the circle that lie within it. 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((data['x'][i-1], data['y'][i-1]), e, color='blue', fill=False) circle = plt.Circle(
(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(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: 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(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom') plt.text(
plt.xlabel('X') data["x"][i - 1],
plt.ylabel('Y') data["y"][i - 1],
plt.title('DBSCAN Clustering') "P" + str(i),
plt.legend(['Core','Noise']) ha="center",
va="bottom",
)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("DBSCAN Clustering")
plt.legend(["Core", "Noise"])
plt.show() plt.show()