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
PostLikeUsers = pickle.load(open ("PostLikeUsers","rb"))
arr = pickle.load(open ("arr","rb"))
indexes = pickle.load(open ("indexes","rb"))
print(indexes)
#randomIndex = list(random.sample(range(0,59),25))
usersHashMap = createHashMap(arr,sizeOfTrainingSet,indexes)
print(len(usersHashMap))
#usersHashMap = pickle.load(open ("usersHash","rb"))
#postIds = pickle.load(open ("postIds","rb"))
#PostLikeUsers = pickle.load(open ("PostLikeUsers","rb"))
numberOfPosts = len(postIds)
numberOfNeighbors = len(usersHashMap)
nearestNumber = 7
totalDiff = 0
usersVectorsArr,usersArr = createArr(usersHashMap,numberOfNeighbors)
tmpUsersVectorsArr = deepcopy(usersVectorsArr) #the full data on users
tmpUsersArr = deepcopy(usersArr) #only the likes array
#indexes = list(range(sizeOfTrainingSet))
fullUsersHashmap = pickle.load(open ("fullUsersHash","rb"))
print(len(fullUsersHashmap))
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