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Divisive.py
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Divisive.py
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import numpy as np
import scipy
import matplotlib.pyplot as plt
from scipy.cluster import hierarchy
global g
import time
def subtract(indices,splinter):
l3 = [x for x in indices if x not in splinter]
return l3
def divisive(a,indices,splinter,sub):
if(len(indices)==1):
return
avg=[]
flag=0
for i in indices:
if(i not in splinter):
sum=0
for j in indices:
if(j not in splinter):
sum=sum+a[i][j]
if((len(indices)-len(splinter)-1)==0):
avg.append(sum)
else:
avg.append(sum/(len(indices)-len(splinter)-1))
if(splinter):
k=0
for i in sub:
total=0
for j in splinter:
total=total+a[i][j]
avg[k]=avg[k] - (total/(len(splinter)))
k+=1
positive=[]
for i in range(0,len(avg)):
if(avg[i]>0):
positive.append(avg[i])
flag=1
if(flag==1):
splinter.append(sub[avg.index(max(positive))])
sub.remove(sub[avg.index(max(positive))])
divisive(a,indices,splinter,sub)
else:
splinter.append(indices[avg.index(max(avg))])
sub[:]=subtract(indices,splinter)
divisive(a,indices,splinter,sub)
def original_subset(indices):
sp=np.zeros(shape=(len(indices),len(indices)))
for i in range(0,len(indices)):
for j in range(0,len(indices)):
sp[i][j]=a[indices[i]][indices[j]]
return sp
def original_max(x):
new=original_subset(x)
return new.max()
def diameter(l):
return original_max(l)
def recursive(a,indices,u,v,clusters,g):
clus_s.append(len(indices))
d.append(diameter(indices))
parents[g]=indices
g-=1
divisive(a,indices,u,v)
clusters.append(u)
clusters.append(v)
new=[]
for i in range(len(clusters)):
new.append(clusters[i])
final.append(new)
x=[]
y=[]
store_list=[]
max=-1
f=0
for list in clusters:
if(diameter(list)>max):
if(len(list)!=1):
f=1
max=diameter(list)
store_list=(list)
if(f==0):
return
else:
clusters.remove(store_list)
recursive(a,store_list,x,y,clusters,g)
def augmented_dendrogram(*args, **kwargs):
data = scipy.cluster.hierarchy.dendrogram(*args, **kwargs)
if not kwargs.get('no_plot', False):
for i, d in zip(data['icoord'], data['dcoord']):
x = 0.5 * sum(i[1:3])
y = d[1]
plt.plot(x, y, 'ro')
plt.annotate("%.3g" % y, (x, y), xytext=(0,12),textcoords='offset points',va='top', ha='center')
return data
a=np.load('distance_matrix.npy')
size=len(a)
g=(size-1)*2
parents={}
final=[]
clusters=[]
indices=[]
clus_s=[]
d=[]
Z=np.zeros(shape=(size-1,4))
p=[]
q=[]
ans=[]
for i in range(0,len(a)):
indices.append(i)
for i in range(0,size):
list=[]
list.append(i)
parents[i]=list
start=time.time()
recursive(a,indices,p,q,clusters,g)
print("Clustering done\t" + str(time.time()-start))
for i in range(0,len(d)):
Z[size-i-2][2]=d[i]
Z[size-i-2][3]=clus_s[i]
for i in range(len(final)-1,0,-1):
for j in range(0,len(final[i-1])):
if final[i-1][j] not in final[i]:
ans.append(final[i-1][j])
ans.append(indices)
for i in range(0,len(ans)):
if(len(ans[i])<=2):
Z[i][0]=ans[i][0]
Z[i][1]=ans[i][1]
else:
s=0
add=[]
common=[]
for j in range(len(ans)-1,-1,-1):
if(set(ans[j])<set(ans[i])):
common=ans[j]
break;
x=(subtract(ans[i],common))
for key in parents.keys():
if(parents[key]==common):
Z[i][0]=key
break;
for key in parents.keys():
if(set(parents[key])==set(x)):
Z[i][1]=key
s=1
break;
if(s==0):
print(Z[i][0],Z[i][1],x)
names=[i for i in range(0,size)]
plt.figure(figsize=(25, 25))
plt.title('Hierarchical Clustering Dendrogram (Divisive)')
plt.xlabel('Sequence No.')
plt.ylabel('Distance')
augmented_dendrogram(Z,labels=names,show_leaf_counts=True,p=25,truncate_mode='lastp')
plt.show()