Create fuzzy.py

fuzzy.py

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+import numpy as np
+a=[np.random.randint(4) for i in range(2486)]
+b=[np.random.randint(4) for i in range(2486)]
+c=[np.random.randint(4) for i in range(2486)]
+d=[np.random.randint(4) for i in range(2486)]
+e=[np.random.randint(4) for i in range(2486)]
+f=[np.random.randint(4) for i in range(2486)]
+g=[np.random.randint(4) for i in range(2486)]
+h=[np.random.randint(4) for i in range(2486)]
+j=[np.random.randint(4) for i in range(2486)]
+ks=[np.random.randint(4) for i in range(2486)]
+l=[np.random.randint(4) for i in range(2486)]
+
+
+import pandas as pd
+mn=pd.DataFrame({'a':a,'b':b,'c':c,'d':d,'e':e,'f':f,'g':g,'h':h,'j':j,'k':ks,'l':l})
+mn
+
+mn.to_csv('E:/datasets/csv/fuzzy_dataset.csv')  #this is my location of my data 
+
+m=[]
+for i in mn['a']:
+    if i>=2:
+        m.append(1)
+    else:
+        m.append(0)
+
+
+
+m1=[]
+for i in mn['b']:
+    if i>=2:
+        m1.append(1)
+    else:
+        m1.append(0)
+
+m2=[]
+for i in mn['c']:
+    if i>=2:
+        m2.append(1)
+    else:
+        m2.append(0)
+m3=[]
+for i in mn['d']:
+    if i>=2:
+        m3.append(1)
+    else:
+        m3.append(0)
+
+m4=[]
+for i in mn['e']:
+    if i>=2:
+        m4.append(1)
+    else:
+        m4.append(0)
+m5=[]
+for i in mn['f']:
+    if i>=2:
+        m5.append(1)
+    else:
+        m5.append(0)
+
+m6=[]
+for i in mn['g']:
+    if i>=2:
+        m6.append(1)
+    else:
+        m6.append(0)
+
+m7=[]
+for i in mn['h']:
+    if i>=2:
+        m7.append(1)
+    else:
+        m7.append(0)
+
+m8=[]
+for i in mn['j']:
+    if i>=2:
+        m8.append(1)
+    else:
+        m8.append(0)
+
+m9=[]
+for i in mn['k']:
+    if i>=2:
+        m9.append(1)
+    else:
+        m9.append(0)
+
+m10=[]
+for i in mn['l']:
+    if i>=2:
+        m10.append(1)
+    else:
+        m10.append(0)
+
+mn1=pd.DataFrame({'a':m,'b':m1,'c':m2,'d':m3,'e':m4,'f':m5,'g':m6,'h':m7,'j':m8,'k':m9,'l':m10})
+mn1
+
+l=mn1.sum(axis=0)
+k=mn1.append(l,ignore_index=True)
+k['column']=k.sum(axis=1)
+k
+l=k.sort_values(by=[2486],axis=1,ascending=False)
+l
+kl=l.sort_values(by=['column'],axis=0,ascending=False)
+kl
+clm=kl.drop('column',axis=1)
+clm
+final=clm.drop(2486,axis=0)
+final
+cor=mn1.corr()
+import seaborn as sns
+import matplotlib.pyplot as plt
+plt.figure(figsize=(10,10))
+sns.heatmap(cor,annot=True,cmap='BuGn_r')