Anusha Devulapally

December-01/python3_anusha_devulapally_Sherlock's_quest.py

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+def conv_no(s):
+  p=0
+  s=s[::-1]
+  for i in s:
+    p=i+p*10
+  return p
+
+def sum_left_right(n):
+  digits=[]
+  while(n>0):
+    p=n%10
+    digits.append(p)
+    n=n//10
+  mid=len(digits)//2
+  left=digits[:mid]
+  right=digits[mid:]
+  sum_l=conv_no(left)
+  sum_r=conv_no(right)
+  return sum_l+sum_r
+
+n=int(input())
+if(n%3==0):
+  double=n*n
+  ans= sum_left_right(double)
+  if(ans==n):
+    print("Safe")
+  else:
+    print("Not Safe")
+else:
+  print("Not Safe")
+

December-02/python3_anusha_devulapally_The_Convo.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+"""December 2.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1q0-dU1J4AGP-MDY5MV9UzVfYQ93IEDhg
+"""
+
+tele_dict={2:['a','b','c'],3:['d','e','f'],4:['g','h','i'],5:['j','k','l'],6:['m','n','o'],7:['p','q','r','s'],8:['t','u','v'],9:['w','x','y','z']}
+
+n=int(input())
+#Only 2 character combinations are allowed.
+digits=[]
+digits.append(n//10)
+digits.append(n%10)
+print(digits)
+
+comb=[]
+for i in tele_dict[digits[0]]:
+  for j in tele_dict[digits[1]]:
+    comb.append(i+j)
+print(comb)
+
+#back tracking 
+n=int(input())
+digits=[]
+while(n>0):
+  p=n%10
+  digits.append(p)
+  n=n//10
+digits=digits[::-1]
+
+def backtrack_combi(lists,result,depth,current):
+  if(depth==len(lists)):
+    result.append(current)
+    return
+  for i in range(len(lists[depth])):
+    backtrack_combi(lists,result,depth+1,current+lists[depth][i])
+
+lists=[]
+for i in digits:
+  lists.append(tele_dict[i])
+result=[]
+backtrack_combi(lists,result,0,"")
+print(result)
+

December-03/python3_anusha_devulapally_Meet_and_Greet.py

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+# -*- coding: utf-8 -*-
+"""December 3.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1q0-dU1J4AGP-MDY5MV9UzVfYQ93IEDhg
+"""
+
+# assuming the entered are in order
+no_of_meetings= int(input("enter the no of meetings"))
+total_meetings=[]
+for i in range(no_of_meetings):
+  start_meet=input("enter starting time of the meeting")
+  end_meet=input("enter ending time of the meeting")
+  total_meetings.append([start_meet,end_meet])
+print(total_meetings)
+
+start_office='0900'
+end_office='1700'
+
+def difference(s,e):
+  s_min=(int(s[0])*10+int(s[1]))*60+(int(s[2])*10+int(s[3]))
+  e_min=(int(e[0])*10+int(e[1]))*60+(int(e[2])*10+int(e[3]))
+  return e_min-s_min
+
+prev=start_office
+next=end_office
+result=[]
+for i in range(no_of_meetings):
+  ans=difference(prev,total_meetings[i][0])
+  if(ans>=60):
+    result.append([prev,total_meetings[i][0]])
+  prev=total_meetings[i][1]
+  if(i==no_of_meetings-1):
+    ans=difference(total_meetings[i][1],next)
+    if(ans>=60):
+      result.append([total_meetings[i][1],next])
+
+print(result)
+

December-04/python3_anusha_devulapally_spoiled_or_not.py

@@ -0,0 +1,29 @@
+no=int(input())
+
+m_dates=[]
+for i in range(no):
+  li=list(map(str,input().split()))
+  m_dates.append(li)
+
+print(m_dates)
+
+before=[]
+for i in range(no):
+  before.append(int(input()))
+
+given_date=list(map(str,input().split()))
+
+
+def diff_dates(dt1,dt2):
+  t1=(int(dt1[2])-1)*360+int(dt1[0])+30*(int(dt1[1])-1)
+  t2=(int(dt2[2])-1)*360+int(dt2[0])+30*(int(dt2[1])-1)
+  return abs(t1-t2)
+
+spoiled=0
+for i in range(no):
+  diff=diff_dates(m_dates[i],given_date)
+  if(diff>before[i]):
+    spoiled+=1
+
+print(spoiled)
+

December-05/python3_anusha_devulapally_the_grand_master.py

@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+"""python3_anusha_devulapally_the_grand_master.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1q0-dU1J4AGP-MDY5MV9UzVfYQ93IEDhg
+"""
+
+m,n=map(int,input().split())
+
+x,y=map(int,input().split())
+
+tx,ty=map(int,input().split())
+
+def isvalid(mat,x,y,m,n,t):
+  #li=[]
+  t=t+1
+  if (((x-1)>=0 and (y-2)>=0) and mat[x-1][y-2]==0):
+    mat[x-1][y-2]+=t
+   # li.append((x-1,y-2))
+  if (((x+1)<m and (y-2)>=0) and mat[x+1][y-2]==0):
+    mat[x+1][y-2]+=t
+    #li.append((x+1,y-2))
+  if (((x-2)>=0 and (y-1)>=0) and mat[x-2][y-1]==0):
+    mat[x-2][y-1]+=t
+    #li.append((x-2,y-1))
+  if (((x+2)<m and (y-1)>=0) and mat[x+2][y-1]==0):
+    mat[x+2][y-1]+=t
+    #li.append((x+2,y-1))
+  if (((x-2)>=0 and (y+1)<n) and mat[x-2][y+1]==0):
+    mat[x-2][y+1]+=t
+    #li.append((x-2,y+1))
+  if (((x-1)>=0 and (y+2)<n) and mat[x-1][y+2]==0):
+    mat[x-1][y+2]+=t
+    #li.append((x-1,y+2))
+  if (((x+1)<m and (y+2)<n) and mat[x+1][y+2]==0):
+    mat[x+1][y+2]+=t
+    #li.append((x+1,y+2))
+  if (((x+2)<m and (y+1)<n) and mat[x+2][y+1]==0):
+    mat[x+2][y+1]+=t
+    #li.append((x-2,y+1))
+
+mat=[[0]*n for i in range(m)]
+mat[x-1][y-1]=1
+turn=1
+
+while (mat[tx-1][ty-1]==0):
+  for i in range(m):
+    for j in range(n):
+      if (mat[i][j]==turn):
+        isvalid(mat,i,j,m,n,turn)
+  turn+=1
+print("minmium steps",mat[tx-1][ty-1]-1)
+

December-07/python3_anusha_devulapally_temperature_screening.py

@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+"""December 7.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1q0-dU1J4AGP-MDY5MV9UzVfYQ93IEDhg
+"""
+
+counterA=[]
+counterB=[]
+temp_screening=[]
+
+print("Enter Counter A names, enter 'q' to end")
+k=input()
+while(k!='q'):
+  counterA.append(k)
+  k=input()
+print("Enter Counter B names, enter 'q' to end")
+k=input()
+while(k!='q'):
+  counterB.append(k)
+  k=input()
+
+print(counterA)
+
+print(counterB)
+
+m=len(counterA)
+n=len(counterB)
+i=0
+j=0
+k=0
+while(i<m or j<n):
+  while(k<2):
+    if(j<n):
+      temp_screening.append(counterB[j])
+      j+=1
+    k+=1
+  k=0
+  if(i<m):
+    temp_screening.append(counterA[i])
+    i+=1
+
+print(temp_screening)
+

December-11/python3_anusha_devulapally_jsql.py

@@ -0,0 +1,49 @@
+path="https://raw.githubusercontent.com/anusha-devulapally/A-December-of-Algorithms-2020/main/src/assets/dec%2011%20sample%20input.json"
+
+import requests
+req=requests.get(path).json()
+print(req)
+
+table_name=req['table name']
+table_name
+
+headers=req['headers']
+cols=len(headers)
+head=[]
+for i in range(1,cols+1):
+  head.append([headers[str(i)]['column name'],headers[str(i)]['data type']])
+print(head)
+
+records=req['records']
+col=len(records)
+rec=[]
+for i in range(1,col+1):
+  rec.append(records[str(i)])
+
+print(rec)
+
+print("create table",end=' ')
+print(table_name,end=' ')
+print('(',end='')
+for i in range(len(head)):
+  print(head[i][0],end=' ')
+  print(head[i][1],end='')
+  if(i<len(head)-1):
+    print(",",end=' ')
+print(')',end=';')
+
+for i in range(len(rec)):
+  print("insert into",end=' ')
+  print(table_name,end=' ')
+  print("values",end=' ')
+  print("(",end='')
+  for j in range(len(rec[i])):
+    if("varchar" in head[j][1]):
+      print('"{}"'.format(rec[i][j]),end='')
+    else:
+      print(rec[i][j],end='')  
+    if(j<(len(rec[i])-1)):
+      print(",",end=' ')
+  print(")",end=';')
+  print()
+

December-12/python3_anusha_devulapally_recruitement_drive.py

@@ -0,0 +1,55 @@
+path="https://raw.githubusercontent.com/anusha-devulapally/A-December-of-Algorithms-2020/main/src/assets/RECRUITMENT%20DRIVE%20-%20Sheet1.csv"
+
+import numpy as np 
+import pandas as pd 
+class LogitRegression() : 
+    def __init__( self, learning_rate, iterations ) :         
+        self.learning_rate = learning_rate         
+        self.iterations = iterations   
+    def fit( self, X, Y ) :                 
+        self.m, self.n = X.shape               
+        self.W = np.zeros( self.n )         
+        self.b = 0        
+        self.X = X         
+        self.Y = Y 
+
+        # gradient descent learning 
+
+        for i in range( self.iterations ) :             
+            self.update_weights()             
+        return self
+
+    def update_weights( self ) :            
+        A = 1 / ( 1 + np.exp( - ( self.X.dot( self.W ) + self.b ) ) )         
+        tmp = ( A - self.Y.T )         
+        tmp = np.reshape( tmp, self.m )         
+        dW = np.dot( self.X.T, tmp ) / self.m          
+        db = np.sum( tmp ) / self.m  
+
+        # update weights     
+        self.W = self.W - self.learning_rate * dW     
+        self.b = self.b - self.learning_rate * db 
+
+        return self
+
+    def predict( self, X ) : 
+        k=np.dot(X.T,self.W)+self.b   
+        Z = 1 / ( 1 + np.exp(-(k)))         
+        Y = np.where( Z > 0.5, 1, 0 )         
+        return Y 
+
+def main() : 
+    df = pd.read_csv(path) 
+    X = df.iloc[:,:-1].values 
+    Y = df.iloc[:,-1:].values     
+    model = LogitRegression( learning_rate = 0.3, iterations = 1000 ) 
+    model.fit( X, Y )         
+    x=list(map(float,input().split()))
+    x=np.array(x).reshape(-1,1)
+    y_pred2=model.predict(x)
+    if(y_pred2==1):
+      print("Selected")
+    else:
+      print("Not selected")
+if __name__ == "__main__" :      
+    main()

December-13/python3_anusha_devulapally_Check_your_Spelling_Sara.py

@@ -0,0 +1,23 @@
+
+correct_word=list(map(str,input().split()))
+
+correct_word
+
+misspelt_words=input()
+
+def matchornot(correct,wrong):
+  m=len(correct)
+  n=len(wrong)
+  if(m!=n):
+    return 0
+  total=0
+  for i in range(m):
+    if(correct[i]!=wrong[i]):
+      total+=1
+  return total
+
+for i in correct_word:
+  val=matchornot(i,misspelt_words)
+  if(val==1):
+    print(i)
+