def DisplayResults(aramgame):
aramgame = json.loads(aramgame)
teammates, opponents, gameWin = DatabaseActions.GetResults(aramgame)
if gameWin == False:
print "---------------------\nTEAM ONE\n---------------------"
else:
print "---------------------\nTEAM ONE -- WINNERS\n---------------------"
for champion in teammates:
print championdictionary[str(champion)]["championName"]
if gameWin == False:
print "---------------------\nTEAM TWO -- WINNERS\n---------------------"
else:
print "---------------------\nTEAM TWO\n---------------------"
for champion in opponents:
print championdictionary[str(champion)]["championName"]
print "\n\n\n"
def PredictGame(wizard, aramgame):
aramgame = json.loads(aramgame)
teammates, opponents, gameWin = DatabaseActions.GetResults(aramgame)
#writes a vector of which champions were on which team followed by the result
gamevector = [0] * len(championdictionary)
for champion in teammates:
gamevector[championdictionary[str(champion)]["Id"] - 1] = 1
for champion in opponents:
gamevector[championdictionary[str(champion)]["Id"] - 1] = -1
prediction = wizard.activate(gamevector)[0]
#Check to see if we were correct
correct = (True if int(round(prediction)) == gameWin else False)
#For data sorting we will describe all our certainties as affirmative cases (over 50%)
if prediction < 0.5:
prediction = 1 - prediction
return prediction, correct
predictionNet.addConnection(in_to_hidden)
predictionNet.addConnection(hidden_to_out)
predictionNet.addConnection(FullConnection(predictionNet['bias'],hiddenLayer))
predictionNet.addConnection(FullConnection(predictionNet['bias'],outLayer))
predictionNet.sortModules()
trainingSet = SupervisedDataSet(len(championdictionary),1)
#Takes each game and turns it into a vector. -1 is stored if the champion is on the opposing team, 1 if the champion is on the player's team
#and 0 if it wasn't played. The vector is then fed into the Neural Network's Training Set
print "Adding Games to NN"
for game in aramdata.readlines():
aramgame = json.loads(game)
teammates,opponents, gameWin = DatabaseActions.GetResults(aramgame)
#writes a vector of which champions were on which team followed by the result
gamevector = [0]*len(championdictionary)
for champion in teammates:
gamevector[championdictionary[str(champion)]["Id"]-1] = 1
for champion in opponents:
gamevector[championdictionary[str(champion)]["Id"]-1] = -1
#Feeds that result into our Neural Network's training set
trainingSet.appendLinked(gamevector,int(gameWin))
#Creates a Backpropagation trainer and proceeds to train on our set. This step can take a while due to the volume of our data.
print "Training NN"
trainer = BackpropTrainer(predictionNet,trainingSet)
trainer.trainUntilConvergence(dataset = trainingSet, maxEpochs = 300, verbose = True, continueEpochs = 10, validationProportion=0.1)