def cross_validate(args):
# import some functions
encode = Data_Loader().encode
find_error = NFL_Predictor().compareVector
try:
nn = Neural_Network.createFromFile(args.file)
print "Loaded Neural Network with %i hidden nodes" % len(nn.hidden_nodes)
totalCorrect = 0.0
total_tested = 0.0
for y in range(args.start,args.end+1):
classRight = [0, 0, 0, 0, 0, 0]
correct = incorrect = 0
if(args.db == 'u'):
dl = Data_Loader()
teams = dl.getAllTeams(y)
elif(args.db == 'b'):
dl = Data_Loader()
teams = dl.getAllTeams(y)
elif(args.db == 'p'):
dl = Data_Loader('playoffTeams.csv')
teams = dl.getAllTeams(y)
elif(args.db == 'o'):
dl = Data_Loader('balancedData.csv')
teams = dl.getAllTeams(y)
total_tested += len(teams)
total_error = 0.0
for t in teams:
t.result = nn.feed_forward(t.stats)
error = (find_error(t.result, encode(t.classification)))
total_error += error**2
if error < .08:
correct += 1
classRight
if args.v:
print "team %s, results %s, class %s, error %s" % (t.name, t.result, encode(t.classification), error)
if not args.q:
print "%d \t within threshold: %d/%d \t error: %s" % (y, correct, len(teams), str(total_error))
totalCorrect += correct
print "totalCorrect: %i/%i, %.2f%%" % (totalCorrect, total_tested, (totalCorrect/total_tested)*100)
except Exception as e:
print "invalid formatting, consult neural_main.py c --help \nError: %s" % e
'''
nn = Neural_Network.createWithRandomWeights(66,40,6)
# train! with learning rate proportional to # of teams in the situations
inputs = []
targets = []
for y in range(2005,2007):
i,t = DL.getTargets(y)
inputs += i
targets += t
#print targets
nn = nn.train(10000,inputs,targets,1.5)
nn.saveToFile("predictortest.txt")
'''
nn = Neural_Network.createFromFile("predictortest.txt")
teams_2011 = DL.getAllTeams(2011)
pats_2011 = filter(lambda t: t.name == "nwe", teams_2011)[0]
all_other_teams = filter(lambda t: t.name != "nwe", teams_2011)
predictor = NFL_Predictor(nn)
similar = predictor.compareWithPastTeams(all_other_teams, pats_2011, 3)
for t,d in similar:
print t.name + " " + str(d) + "\n"
