def classify(classifier, testFile, domain, decisions, means, vars):
featureLine = ConvertData.getSingleFeatureLineFromFile(testFile, decisions, True)
featureLine = list((np.array(featureLine[:-1]) - means) / vars) + [featureLine[-1]]
datum = orange.Example(domain, featureLine)
classification = classifier(datum)
correct = datum.get_class()
return classification, correct
def test_entityList(self):
context = ConvertData.createContext()
Beatscript.addEntityList(context, "person§Karl, person§Hugo,\tobject§Table,place§Home,\t")
self.assertTrue("Table" in context["Entities"] and context["Entities"]["Table"].type == Config.OBJECT)
self.assertTrue("Karl" in context["Entities"] and context["Entities"]["Karl"].type == Config.PERSON)
self.assertTrue("Hugo" in context["Entities"] and context["Entities"]["Hugo"].type == Config.PERSON)
self.assertTrue("Home" in context["Entities"] and context["Entities"]["Home"].type == Config.PLACE)
self.assertFalse("Peter" in context["Entities"])
self.assertTrue(len(context["Entities"]) == 4)
def get_sliders():
query = """select id, name, url from tbl_slider limit 5"""
db = database()
result = db.select(query)
db.close()
if result is None:
return 'false'
import ConvertData
result = ConvertData.ConvertData().convert_to_json(result)
return result
def getPerformance(classifier, domain, testFiles, means, vars):
correct = 0
weightedDiff = 0
total = 0
for testFile in testFiles:
f = open(testFile, "r")
lines = f.readlines()
f.close()
context = ConvertData.readContext(lines)
blockList = ConvertData.getBlockList(lines, context)
decisions = []
for _ in range(len(blockList)):
shotClass = classification(classifier,context,blockList,domain,decisions,means, vars,shot=True)
correctClass = orange.Value(blockList[-1][-1].shot,domain.class_var)
#shotClass, correctClass = classify(classifier, testFile, domain, decisions, means, vars)
decisions.append(shotClass)
if correctClass == shotClass:
correct += 1
weightedDiff += getDifference(shotClass, correctClass)
total += 1
return float(correct)/ total, float(weightedDiff) / total
def test_readContext(self):
textfile = ["#Film:\tTestfilm",
"#Scene:\tTestszene",
"#FPS:\t25",
"#Context:\tperson§Hugo, person§Alexander the Great",
"#EntityList:\tobject§Rope, place§Room, object§Hugos Thing"]
context = ConvertData.readContext(textfile)
self.assertTrue(context["Film"]=="Testfilm")
self.assertTrue(context["Scene"]=="Testszene")
self.assertTrue(context["FPS"]==25)
self.assertTrue(context["Entities"]["Hugo"].type == Config.PERSON)
self.assertTrue(context["Entities"]["Alexander the Great"].type == Config.PERSON)
self.assertTrue(context["Entities"]["Rope"].type == Config.OBJECT)
self.assertTrue(context["Entities"]["Room"].type == Config.PLACE)
self.assertTrue(context["Entities"]["Hugos Thing"].type == Config.OBJECT)
def test_initialContext(self):
context = ConvertData.createContext()
Beatscript.addInitialContext(context, "person§Peter, person§Hugo,\tobject§Table,place§Home, person§Lord Harthorne,")
Beatscript.addEntityList(context, "person§Karl, person§Hugo,\tobject§Table,place§Home, ")
self.assertTrue(context["Entities"]["Table"] in context["KnownEntities"])
self.assertTrue(context["Entities"]["Hugo"] in context["KnownEntities"])
self.assertTrue(context["Entities"]["Home"] in context["KnownEntities"])
self.assertFalse(context["Entities"]["Karl"] in context["KnownEntities"])
self.assertTrue(len(context["KnownEntities"]) == 5)
for entity in context["KnownEntities"]:
if entity.name not in context["Entities"]:
self.assertTrue(False)
for entityName in context["Entities"]:
if context["Entities"][entityName] not in context["KnownEntities"]:
self.assertTrue(entityName=="Karl" and context["Entities"][entityName].type == Config.PERSON)
def get_admins_list(info):
city_id = info[0]
query = """
select id, name from tbl_admin"""
# where city_id=%d
# """ % city_id
db = database()
result = db.select(query)
db.close()
if result is None:
return 'false'
import ConvertData
result = ConvertData.ConvertData().convert_to_json(result)
return result
def get_ranking(info):
game_id = int(info[0])
city_id = int(info[1])
query = """
select tbl_ranking.id, tbl_user.name, tbl_ranking.rank from tbl_ranking
join tbl_user
on tbl_user.id=tbl_ranking.user_id
where tbl_ranking.game_id=%d
order by tbl_ranking.rank desc
limit 20
""" % (game_id)
db = database()
result = db.select(query)
db.close()
if result is None:
return 'false'
import ConvertData
result = ConvertData.ConvertData().convert_to_json(result)
return result
def conf_interval(data):
N = len(data)
sorted_estimates = np.sort(np.array(data))
conf_interval = (np.abs(sorted_estimates[int(0.025 * N)]), np.abs(sorted_estimates[int(0.975 * N)]))
return conf_interval
data, targets = ReadLensesData.read_data()
data = np.array(data)
targets = np.array(targets)
errors = []
# Peform LOE Cross-Validation
for i in range(0, len(data)):
print(i, " : ", len(data))
data_p = data[np.arange(len(data)) != i]
targets1 = ConvertData.convert_to_binary(targets, 1)
targets2 = ConvertData.convert_to_binary(targets, 2)
targets3 = ConvertData.convert_to_binary(targets, 3)
targets1_p = targets1[np.arange(len(data)) != i]
targets2_p = targets2[np.arange(len(data)) != i]
targets3_p = targets3[np.arange(len(data)) != i]
hard = train_cnf_network(6, data_p, np.array(targets1_p), 90000)
print("Hard: ", hard[2])
soft = train_cnf_network(6, data_p, np.array(targets2_p), 90000)
print("Soft: ", soft[2])
no = train_cnf_network(6, data_p, np.array(targets3_p), 90000)
print("NO: ", no[2])
hard_er = run_cnf_network(6, data.tolist(), targets1, hard[1])
