def predict(): X = [item for item in request.form.values()] X = [float(X[i]) if X[i].isnumeric() else np.nan for i in range(8) ] + [str(X[i]) for i in range(8, 15)] X = pd.DataFrame(data=[X], columns=features) item = data.head(1) for i in features: item[i] = X[i] final_features = full_pipeline.transform(item) prediction_svm = convert(SVM_Grid.best_estimator_.predict(final_features)) prediction_knn = convert(KNN_Grid.best_estimator_.predict(final_features)) prediction_rf = convert(RF_Random.predict(final_features)) return render_template('index.html', prediction_svm="(SVM) House's Price should be {}".format(prediction_svm), prediction_knn="(KNN) House's Price should be {}".format(prediction_knn), prediction_rf="(RF) House's Price should be {}".format(prediction_rf) )
def pretty_print(maze): print("") for a in m.convert(maze): string = "" for b in a: string += str(b) print(string) print("")
def get_prediction(payload: StockIn): ticker = payload.ticker prediction_list = predict(ticker) if not prediction_list: raise HTTPException(status_code=400, detail="Model not found.") response_object = {"ticker": ticker, "forecast": convert(prediction_list)} return response_object
def test_find_end_point(self): data = model.convert(model.make_empty_maze(10, 10)) hasEndpoint = False for a in data: for b in a: if b == 2: hasEndpoint = True # print(hasEndpoint) # print(data) self.assertTrue(hasEndpoint, "have found two")
def auth(): email = request.args.get('email') password = request.args.get('password') result = model.authenticate(email, password) if isinstance(result, model.User): return jsonify(model.convert(result)) else: return abort(401)
def do_dffs(dffs, in_models, dffpath, targetpath): print("*** Processing %d DFF's ***" % len(dffs)) for dfffile in dffs: writepath = targetpath + dfffile + ".js" if not overwrite and os.path.exists(writepath): continue txdpath = "" if dfffile.lower() in in_models: txdpath = "data/textures/"+in_models[dfffile.lower()]["TextureName"]+"/" dff = model.convert(dffpath+dfffile.lower()+".dff", txdpath) f = open(writepath, "w") json.dump(dff.data, f) f.close()
def solve_multiple_mazes(solution_alg, loop): maze_data = {} for size in range(5, 35, 5): maze_data[size] = {} empty_maze = m.make_empty_maze(size, size) maze = m.DFS(empty_maze) converted_maze = m.convert(maze) #m.save_maze("maze.csv", converted_maze) #loaded_maze = m.read_maze("maze.csv") times, moves = solve_with_algorithm(solution_alg, converted_maze, loop) maze_data[size]["moves"] = moves[0] maze_data[size]["avg_time"] = m.calc_average(times) maze_data[size]["min_time"] = min(times) maze_data[size]["max_time"] = max(times) view.print_result(maze_data, size) #view.print_times(times) return maze_data
def solveMaze(size): grid = model.convert(model.DFS(model.make_empty_maze(size, size))) start = time.time() model.search(1, 1, grid) end = time.time() #print(grid) count = 0 flattened_list = [y for x in grid for y in x] for n in flattened_list: if n == 3 or n == 2: count += 1 t = end - start lis = [t, count] return lis
def csvPrint(): grid = model.convert(model.DFS(model.make_empty_maze(5, 5))) printer.printFile(grid) #model.search(1, 1, grid) printer.readFile()
from model import train, predict, convert ticker = "AAPL" train(ticker) prediction_list = predict(ticker) result = convert(prediction_list) print(result)
from model import train, predict, convert train() prediction_list = predict() convert(prediction_list) train("GOOG") train("AAPL") train("^GSPC")
$ conda create -n env python=3.8 conda activate env (To deactivate use - conda deactivate env) $ pip install -r requirements.txt $ python >>> from model import train, predict, convert >>> train() >>> prediction_list = predict() >>> convert(prediction_list) train("FB") train("AAPL") train("AMZN") train("NFLX") train("GOOG") train("TSLA")