From c3319ba5b6f0c70019310d2e4224fd6e8c1ef7a7 Mon Sep 17 00:00:00 2001
From: G-Dinusha <dinushag997@gmail.com>
Date: Sat, 19 Oct 2024 10:39:05 +0530
Subject: [PATCH] Update kNearestNeighbour.py

---
 ML Algorithms/kNearestNeighbour.py | 77 +++++++++++++++++-------------
 1 file changed, 44 insertions(+), 33 deletions(-)

diff --git a/ML Algorithms/kNearestNeighbour.py b/ML Algorithms/kNearestNeighbour.py
index d2d778d05..5ac348baa 100644
--- a/ML Algorithms/kNearestNeighbour.py	
+++ b/ML Algorithms/kNearestNeighbour.py	
@@ -1,41 +1,52 @@
 import matplotlib.pyplot as plt
 import numpy as np
-import math 
-f=[[10,5],[40,7],[3,2],[5,3]]
-l=['good','good','bad','bad']
-x=[]
-y=[]
-s=len(set(l)) #shows all categories present
+import math
+
+# Input data points
+f = [[10, 5], [40, 7], [3, 2], [5, 3]]
+l = ['good', 'good', 'bad', 'bad']
+
+# Initialize numpy arrays
+x = np.array([point[0] for point in f])
+y = np.array([point[1] for point in f])
+
+print(x, " ", y)
+
+# Plot data points based on labels
 for i in range(len(f)):
-    f1=f[i][0]
-    x=np.append(x,f1)
-    f2=f[i][1]
-    y=np.append(y,f2)
-print(x," ",y)
-for i in range(len(f)):
-    if (l[i]== 'good'):
-        plt.plot(x[i],y[i],'r*')
+    if l[i] == 'good':
+        plt.plot(x[i], y[i], 'r*', label='Good Habits' if i == 0 else "")
     else:
-        plt.plot(x[i],y[i],"y^")
-p = int(input("Enter saving%"))
-q = int(input("Enter no. of good habit"))
-k=int(input("Enter k"))
-plt.plot(p,q,'b*')
-disx=[]
-disy=[]
-dis=[]
-if (k>len(f)):
-    k=len(f)
-for i in range(len(x)):
-    dis=np.append(dis,math.sqrt(((p-x[i])**2)+((q-y[i])**2)))
+        plt.plot(x[i], y[i], 'y^', label='Bad Habits' if i == 0 else "")
+
+# User inputs
+p = int(input("Enter saving%: "))
+q = int(input("Enter no. of good habits: "))
+k = int(input("Enter k: "))
+
+# Plot user input point
+plt.plot(p, q, 'b*', label='User Input')
+
+# Calculate distances
+dis = np.array([math.sqrt(((p - x[i]) ** 2) + ((q - y[i]) ** 2)) for i in range(len(x))])
 print(dis)
+
+# Sort distances and get the k smallest
 dis.sort()
-min1=[]
-for i in range(k):
-    min1=np.append(min1,dis[i])
+min1 = dis[:k]  # Get the k smallest distances
 print(min1)
-sum1=0
-for i in range(len(min1)):
-    sum1=sum1+min1[i]
-print(sum1/len(min1)) #avgmin = sum1/len(min1)
+
+# Calculate average of the smallest distances
+if len(min1) > 0:
+    avg_min = sum(min1) / len(min1)
+    print("Average of the k smallest distances:", avg_min)
+else:
+    print("No distances to calculate average.")
+
+# Add legends and labels
+plt.title('Habits Visualization')
+plt.xlabel('Saving Percentage')
+plt.ylabel('Number of Good Habits')
+plt.legend()
+plt.grid(True)
 plt.show()