def index(): form = LoginForm(request.form) if request.method == 'POST' and form.validate(): result = compute(form.Sepal_Length.data, form.Sepal_Width.data, form.Petal_Length.data, form.Petal_Width.data) else: result = None return render_template('login.html',form=form, result=result)
def home(filename): if request.method == 'GET': if filename is not None: labels, distance = compute(filename) print(labels) print(distance) return render_template('index.html', labels=labels, distance=distance) else: return render_template('index.html')
def index(): examId = request.forms.get("exam_id") pdfFile = request.files.get("file") if pdfFile and examId: Path("./saves/").mkdir(parents=True, exist_ok=True) now = datetime.now().strftime("%Y_%m_%d__%H_%M_%S") fileLocation = f"./saves/{now}_{pdfFile.filename}" pdfFile.save(fileLocation) print("Got file in:", fileLocation) computation = main.compute(fileLocation, examId) print(computation) return computation else: return {"error": "Expected fields : file, exam_id"}
def handle_sub_view(): global PathForUploads global tmpimage global EmotionalDist with app.test_request_context(): calculate_features_for_target_image(tmpimage) des_images, accumu_dist, works = compute(tmpimage, EmotionalDist, target_hlfeat) image = cv2.imread(UPLOAD_FOLDER + "/" + tmpimage) if des_images != None: GetTransformedImage(image, des_images, accumu_dist, OUTPUT_FOLDER + "/" + tmpimage) else: gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imwrite(OUTPUT_FOLDER + "/" + tmpimage, gray_image)
def learn(): global films, directors, actors, genres, writers, rateds if not films: films = load_films() liked = json.loads(request.args.get('liked')) liked2 = json.loads(request.args.get('liked1')) films_list = get_film_list() films_to_add = [] print films_list for k in films_list: films_to_add.append(get_film_data(get_film_id(k))) films_to_add = sorted(films_to_add, key=lambda f: f["imdbRating"], reverse=True) for k in films_to_add: liked.append(2) liked2.append(2) films.append(k) films, directors, actors, genres, writers, rateds = extract_vector_features( films) print "len(films)", len(films) print "len(liked)", len(liked) print "len(liked2)", len(liked2) filtered_films = [f for (i, f) in enumerate(films) if liked[i] != 0] filtered_liked = filter(lambda l: l != 0, liked) results1 = compute(filtered_films, directors, actors, genres, writers, rateds, filtered_liked, len(films_list)) #if liked2: # filtered_films = [f for (i,f) in enumerate(films) if liked2[i] != 0] # filtered_liked = filter(lambda l: l != 0, liked2) # results2 = compute(filtered_films, directors, actors, genres, writers, rateds, filtered_liked, len(films_list)) #else: results2 = [] return jsonify(results1=results1, results2=results2, films=films[-len(films_list):])
def nlp(topic): result = '' if topic: result = compute(topic) return result
tamanos = np.arange(41, 801, 20) distancias = np.arange(2, 16, 1) posibles = [] for x in tamanos: posiblesRow = [] for y in distancias: posiblesRow.append((x, y)) posibles.append(posiblesRow) resultados = [] for x in posibles: resultadosRow = [] for y in x: resultado = round(compute(y[0], y[1]), 2) resultadosRow.append(resultado) # print(resultado) resultados.append(resultadosRow) resultados_ = np.array(resultados) def pprintMtrx(matrix): s = [[str(e) for e in row] for row in matrix] lens = [max(map(len, col)) for col in zip(*s)] fmt = '\t'.join('{{:{}}}'.format(x) for x in lens) table = [fmt.format(*row) for row in s] return ('\n'.join(table))
def test1(self): self.assertEqual( main.compute(['0C73', '80C1', 'A2A9', '92F5', '9B57', 0]), '8CB2BCEE')
def test4(self): self.assertEqual( main.compute(['75F5', 'B1AC', '67C1', 'A398', '00BC', 0]), 'C4590000')
def test3(self): self.assertEqual( main.compute(['D75C', 'EE87', 'C568', 'FCB3', '4674', 1]), '7FAF')
def test2(self): self.assertEqual( main.compute(['27C2', '0879', '35F6', '1A4D', '27BC', 1]), '0807')
import main import matplotlib.pyplot as plt import data_creation acis = [] scores = [] for i in range(20) : data_creation.create_data(10,80) aci, score = main.compute() print(aci, score) acis.append(aci) scores.append(score) # plt.plot(scores, acis) plt.scatter(scores, acis) plt.show()