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])