Update and rename DBSCAN.py to dbscan.py

machine_learning/dbscan.py

@@ -1,17 +1,31 @@
 import pandas as pd
-import numpy as np
 import math
 import matplotlib.pyplot as plt
-
+from typing import Dict, List
-class DBSCAN:
+class dbscan:
     '''
-    Author : Gowtham Kamalasekar
-    LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
-
     DBSCAN Algorithm :
     Density-Based Spatial Clustering Of Applications With Noise
     Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
 
+    Functions:
+    ----------
+        __init__()       : Constructor that sets minPts, radius and file
+        perform_dbscan() : Invoked by constructor and calculates the core and noise points and returns a dictionary.
+        print_dbscan()   : Prints the core and noise points along with stating if the noise are border points or not.
+        plot_dbscan()    : Plots the points to show the core and noise point.
+
+    To create a object
+    ------------------
+    import dbscan
+    obj = dbscan.dbscan(minPts, radius, file)
+    obj.print_dbscan()
+    obj.plot_dbscan()
+    '''
+    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
@@ -34,29 +48,21 @@ class DBSCAN:
             6 | 4
             7 | 3
             -----
-
-    Functions:
-    ----------
-        __init__()       : Constructor that sets minPts, radius and file
-        perform_dbscan() : Invoked by constructor and calculates the core and noise points and returns a dictionary.
-        print_dbscan()   : Prints the core and noise points along with stating if the noise are border points or not.
-        plot_dbscan()    : Plots the points to show the core and noise point.
-
-    To create a object
-    ------------------
-    import DBSCAN
-    obj = DBSCAN.DBSCAN(minPts, radius, file)
-    obj.print_dbscan()
-    obj.plot_dbscan()
         '''
-    
-    def __init__(self, minPts, radius, file):
         self.minPts = minPts
         self.radius = radius
         self.file = file
         self.dict1 = self.perform_dbscan()
+    def perform_dbscan(self) -> Dict[int, List[int]]:
+        '''
+        Parameters:
+        -----------
+        None
 
-    def perform_dbscan(self):
+        Return:
+        --------
+        Dictionary with points and the list of points that lie in its radius
+        '''
         data = pd.read_csv(self.file)
 
         minPts = self.minPts
@@ -73,8 +79,12 @@ class DBSCAN:
                         dict1[i+1] = [j+1,]
 
         return dict1
-
+    def print_dbscan(self) -> None:
-    def print_dbscan(self):
+        '''
+        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:
@@ -86,8 +96,12 @@ class DBSCAN:
                         break
                 else:
                     print("Noise")
-
+    def plot_dbscan(self) -> None:
-    def plot_dbscan(self):
+        '''
+        Output:
+        -------
+        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:
@@ -99,10 +113,8 @@ class DBSCAN:
             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.show()