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Update and rename DBSCAN.py to dbscan.py
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import pandas as pd
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import pandas as pd
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import numpy as np
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import math
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import math
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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from typing import Dict, List
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class DBSCAN:
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class dbscan:
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'''
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'''
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Author : Gowtham Kamalasekar
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LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
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DBSCAN Algorithm :
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DBSCAN Algorithm :
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Density-Based Spatial Clustering Of Applications With Noise
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Density-Based Spatial Clustering Of Applications With Noise
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Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
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Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
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Functions:
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----------
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__init__() : Constructor that sets minPts, radius and file
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perform_dbscan() : Invoked by constructor and calculates the core and noise points and returns a dictionary.
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print_dbscan() : Prints the core and noise points along with stating if the noise are border points or not.
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plot_dbscan() : Plots the points to show the core and noise point.
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To create a object
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------------------
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import dbscan
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obj = dbscan.dbscan(minPts, radius, file)
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obj.print_dbscan()
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obj.plot_dbscan()
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'''
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def __init__(self, minPts : int, radius : int, file : str) -> None:
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'''
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Constructor
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Attributes:
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Attributes:
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-----------
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-----------
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minPts (int) : Minimum number of points needed to be within the radius to considered as core
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minPts (int) : Minimum number of points needed to be within the radius to considered as core
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@ -34,29 +48,21 @@ class DBSCAN:
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6 | 4
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6 | 4
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7 | 3
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7 | 3
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-----
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-----
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Functions:
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----------
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__init__() : Constructor that sets minPts, radius and file
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perform_dbscan() : Invoked by constructor and calculates the core and noise points and returns a dictionary.
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print_dbscan() : Prints the core and noise points along with stating if the noise are border points or not.
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plot_dbscan() : Plots the points to show the core and noise point.
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To create a object
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------------------
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import DBSCAN
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obj = DBSCAN.DBSCAN(minPts, radius, file)
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obj.print_dbscan()
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obj.plot_dbscan()
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'''
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'''
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def __init__(self, minPts, radius, file):
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self.minPts = minPts
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self.minPts = minPts
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self.radius = radius
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self.radius = radius
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self.file = file
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self.file = file
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self.dict1 = self.perform_dbscan()
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self.dict1 = self.perform_dbscan()
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def perform_dbscan(self) -> Dict[int, List[int]]:
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'''
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Parameters:
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-----------
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None
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def perform_dbscan(self):
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Return:
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--------
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Dictionary with points and the list of points that lie in its radius
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'''
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data = pd.read_csv(self.file)
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data = pd.read_csv(self.file)
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minPts = self.minPts
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minPts = self.minPts
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@ -73,8 +79,12 @@ class DBSCAN:
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dict1[i+1] = [j+1,]
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dict1[i+1] = [j+1,]
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return dict1
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return dict1
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def print_dbscan(self) -> None:
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def print_dbscan(self):
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'''
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Outputs:
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--------
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Prints each point and if it is a core or a noise (w/ border)
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'''
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for i in self.dict1:
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for i in self.dict1:
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print(i," ",self.dict1[i], end=' ---> ')
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print(i," ",self.dict1[i], end=' ---> ')
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if len(self.dict1[i]) >= self.minPts:
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if len(self.dict1[i]) >= self.minPts:
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@ -86,8 +96,12 @@ class DBSCAN:
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break
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break
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else:
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else:
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print("Noise")
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print("Noise")
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def plot_dbscan(self) -> None:
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def plot_dbscan(self):
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'''
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Output:
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-------
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A matplotlib plot that show points as core and noise along with the circle that lie within it.
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'''
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data = pd.read_csv(self.file)
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data = pd.read_csv(self.file)
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e = self.radius
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e = self.radius
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for i in self.dict1:
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for i in self.dict1:
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@ -99,10 +113,8 @@ class DBSCAN:
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else:
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else:
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plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
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plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
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plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
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plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
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plt.xlabel('X')
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.ylabel('Y')
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plt.title('DBSCAN Clustering')
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plt.title('DBSCAN Clustering')
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plt.legend(['Core','Noise'])
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plt.legend(['Core','Noise'])
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plt.show()
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plt.show()
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