diff --git a/machine_learning/dbscan.py b/machine_learning/dbscan.py index 2ae3991d0..0a396a866 100644 --- a/machine_learning/dbscan.py +++ b/machine_learning/dbscan.py @@ -1,11 +1,11 @@ import math -from typing import dict, list, optional + import matplotlib.pyplot as plt import pandas as pd - +from typing import Dict, List, Optional class DbScan: - """ + ''' DBSCAN Algorithm : Density-Based Spatial Clustering Of Applications With Noise Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN @@ -25,28 +25,14 @@ 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}, - ), - ) -> None: - """ + ''' + 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: @@ -74,14 +60,13 @@ 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]]: - """ + def perform_dbscan(self) -> Dict[int, List[int]]: + ''' Args: ----------- None @@ -107,30 +92,25 @@ class DbScan: 11 [2, 10, 11, 12] 12 [9, 11, 12] - """ + ''' if type(self.file) is str: - data = pd.read_csv(self.file) + data = pd.read_csv(self.file) else: data = pd.DataFrame(list(self.file)) e = self.radius 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) @@ -148,25 +128,24 @@ 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: for j in self.dict1: if ( - i != j - and len(self.dict1[j]) >= self.minpts + i != j + and len(self.dict1[j]) >= self.minpts and i in self.dict1[j] ): print("Noise ---> Border") break else: print("Noise") - def plot_dbscan(self) -> None: - """ + ''' Output: ------- A matplotlib plot that show points as core and noise along @@ -174,38 +153,27 @@ class DbScan: >>> DbScan(4,1.9).plot_dbscan() Plotted Successfully - """ + ''' if type(self.file) is str: - data = pd.read_csv(self.file) + data = pd.read_csv(self.file) else: data = pd.DataFrame(list(self.file)) 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")