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[FIX] #1 Updated content in README.md and comments in test.py #2

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[FIX] #1 Updated content in README.md and comments in test.py #2

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4 changes: 2 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
# KMeansalgorithm
The KMeanss Algorithm is one of the most basic algorithms in unsupervised learninng and is used for classification.Here is a basic implementation of the kmeans algorithm which is used to classify a cluster of data into two groups
# K-Means Algorithm
The K-Means Algorithm is one of the most basic algorithms in unsupervised learning and is used for classification. Here is a basic implementation of the K-Means Algorithm which is used to classify a cluster of data into two groups.
10 changes: 5 additions & 5 deletions test.py
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Expand Up @@ -8,7 +8,7 @@
cluster_2=list()#First cluster

def E_distance(a,b):
return math.pow(math.pow(a[1]-b[1],2)+math.pow(a[0]-b[0],2),0.5) #computing the euclidean Distance between two points
return math.pow(math.pow(a[1]-b[1],2)+math.pow(a[0]-b[0],2),0.5) #Computing the Euclidean Distance between two points
for i in dataset:
if E_distance(i,mean1)<E_distance(i,mean2):
cluster_1.append(i)#Clustering the elements
Expand All @@ -19,15 +19,15 @@ def average(a): #Used for finding the centroid of all the points belonging to
centroid[0]=sum(a[i][0] for i in range(len(a)))/len(a)
centroid[1]=sum(a[i][1] for i in range(len(a)))/len(a)
return centroid
while average(cluster_1)!=mean1 and average(cluster_2)!=mean2:#Conition for stopping the KMeans Algorithms
while average(cluster_1)!=mean1 and average(cluster_2)!=mean2:#Condition for stopping the K-Means Algorithms

mean1=average(cluster_1)#the first new mean/centroid
mean2=average(cluster_2)#the second new mean/centroid
mean1=average(cluster_1)#The first new mean/centroid
mean2=average(cluster_2)#The second new mean/centroid
cluster_1=list()
cluster_2=list()
for i in dataset:
if E_distance(i,mean1)<E_distance(i,mean2):
cluster_1.append(i)#Forming the new cLuster
cluster_1.append(i)#Forming the new cluster
else:
cluster_2.append(i)#Forming the new cluster
print(mean1)
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