def predictLikes(userLikes):
postIds = pickle.load(open ("postIds","rb"))
indexes = pickle.load(open ("indexes","rb"))
numberOfPosts = len(postIds)
indexesHashMap = pickle.load(open ("indexesHashMap","rb"))
numberOfNeighbors = len(indexesHashMap)
usersVectorsArr,usersArr = createArr(indexesHashMap,numberOfNeighbors)
nearestNumber = 7
neighbors = findNearestNeighbors(userLikes, numberOfNeighbors,nearestNumber,usersVectorsArr,usersArr)
fullUsersHashMap = pickle.load(open ("fullUsersHash","rb"))
averages = [None]*numberOfPosts
for j in range(numberOfPosts):
average = 0
weight = nearestNumber
for i in range(nearestNumber):
neighbor = neighbors[i]
average += fullUsersHashMap[str(neighbor[1])][j]*weight
weight -= 1
averages[j] = (average/28)
#averages[j] = (average/nearestNumber)
print(averages)
testLikes = [None]*numberOfPosts
counter = 0
for i in indexes:
testLikes[i] = userLikes[counter]
counter += 1
newIndexes =[]
lastIndexes=[]
filterNum = 0.0
while (len(newIndexes)<6):
filterNum += 0.1
newIndexes = []
for num in range(numberOfPosts):
if num not in indexes:
if (averages[num]>= (1-filterNum)):
newIndexes.append(num)
#testLikes[num] = 1
#else:
#testLikes[num] = 0
if len(newIndexes)>20:
newIndexes = newIndexes[:20]
print (filterNum)
return newIndexes
sample = list(random.sample(range(1,18315),5))
for numberOfTests in sample:
tmpFullUsersHashmap = deepcopy(fullUsersHashmap)
#print(numberOfTests)
#test user
userLikes = tmpUsersVectorsArr[numberOfTests]
#print(userLikes)
fullTestUser = tmpUsersArr[numberOfTests]
#create temp arr without the test user
tmpUsersVectorsArr.pop(numberOfTests)
testArr = deepcopy(tmpUsersVectorsArr)
tmpUsersArr.pop(numberOfTests)
fullTestArr = deepcopy(tmpUsersArr)
#find nearestNeighbors of the test user
neighbors = findNearestNeighbors(userLikes, numberOfNeighbors-1,nearestNumber,testArr,fullTestArr)
testUserLikes = tmpFullUsersHashmap[fullTestUser[1]]
#summing the average number of likes
#i = 0
#summing = 0
#for key in newUsersHashMap:
# for like in newUsersHashMap[key]:
# summing += like
# if summing>4.5:
# i += summing
# summing = 0
#print (i)
#delete the testUser from the hashmap
del tmpFullUsersHashmap[fullTestUser[1]]
