def autocomplete_ingredients(food_item):
results = []
# search = request.args.get('term')
search = '%' + str(food_item) + '%'
# print search
query = """SELECT Food_Item.item_id FROM Food_Item WHERE Food_Item.name LIKE %s"""
result = functions.get_result(query, [search])
if result is None:
return []
all_ingr = []
for i in range(len(result)):
ingr_list = functions.get_item_ingredients(result[i]["item_id"])
for ingredient in ingr_list:
all_ingr.append(ingredient["name"])
all_ingr_names = list(set(all_ingr))
return json.dumps(all_ingr_names)
def test():
query = "SELECT Food_Item.name FROM Food_Item"
results = functions.get_result(query, [])
newResults = []
for r in results:
newResults.append(str(r['name']))
return render_template('test.html', food_items=json.dumps(newResults))
def new_item():
if not session.get(user_id):
flash(functions.get_flash_message("not_logged_in"))
return redirect(url_for('login'))
back_url = request.form['back_url']
query = "SELECT Food_Item.name FROM Food_Item"
results = functions.get_result(query, [])
new_results = []
for r in results:
new_results.append(str(r['name']))
return render_template('newitem.html', food_items=json.dumps(new_results), back_url=back_url)
# 局所解とバイアスに分ける
solutions, bias = functions.divide_solutions_bias(solutions_data)
# 評価値の結果のリスト
evaluations_result = []
for num_experiment in range(1, 101):
print(num_experiment)
# 対象のデータの読み込み
data = functions.read_csv(read_filename)
del data[0]
data = functions.transform_to_float(data)
# before_index = functions.get_result(data, functions.get_evaluations_list(data, solutions, bias), num_experiment, functions.get_best_solution_index(bias), solutions)[2]
# 次の世代の作成
for num in range(num_execute):
# print('-------')
# print(functions.get_evaluation_value(data[before_index], solutions, bias))
data = next_generation_MGG_improve(data, solutions, bias, num_parents, num_children, num_elite_preservation, num_dimentions)
# print(functions.get_result(data, functions.get_evaluations_list(data, solutions, bias), num_experiment, functions.get_best_solution_index(bias), solutions))
# before_index = functions.get_result(data, functions.get_evaluations_list(data, solutions, bias), num_experiment, functions.get_best_solution_index(bias), solutions)[2]
# 新しい世代をcsvに書き込む
functions.write_csv(write_filename + '_%i' % num_experiment, data)
evaluations = functions.get_evaluations_list(data, solutions, bias)
evaluation_vector = functions.get_result(data, evaluations, num_experiment, functions.get_best_solution_index(bias), solutions)
evaluations_result.append(evaluation_vector)
final_result = functions.get_final_result(evaluations_result)
functions.write_result(result_file, evaluations_result, final_result)
# 局所解ファイルの読み込み
solutions_data = functions.read_csv(solutions_file)
del solutions_data[0]
solutions_data = functions.transform_to_float(solutions_data)
# 局所解とバイアスに分ける
solutions, bias = functions.divide_solutions_bias(solutions_data)
solutions = np.array(solutions)
bias = np.array(bias)
evaluations_result = []
for num_experiment in range(1, 101):
print(num_experiment)
random = make_random_matrix(5000, 100)
# 評価値の結果のリスト
evaluations = functions.get_evaluations_list(random, solutions, bias)
rankings = functions.get_ranking_list(evaluations)
matrix100, evaluations100, rankings100 = take_top_100(
random, evaluations, rankings, solutions, bias)
evaluation_vector = functions.get_result(
matrix100, evaluations100, num_experiment,
functions.get_best_solution_index(bias), solutions)
evaluations_result.append(evaluation_vector)
final_result = functions.get_final_result(evaluations_result)
functions.write_result(result_file, evaluations_result, final_result)
soup = BeautifulSoup(source, 'lxml')
#a better user agent so it doesn't timeout
a = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'
config = Config()
config.browser_user_agent = a
#-----------------------------------------------------------------------
page = 1
while page != explore + 1:
print()
print('Page {}...'.format(page))
print('-' * 80)
soup = BeautifulSoup(requests.get(url, headers=headers).content, 'html.parser')
search_div = soup.find_all(class_='rc') # find all divs that contains search result
titles, links, descriptions = functions.get_result(search_div, titles, links, descriptions)
next_link = soup.select_one('a:contains("Next")')
if not next_link:
break
url = 'https://google.com' + next_link['href']
page += 1
#-----------------------------------------------------------------------
writer.writer(titles,links,cont,config) #writes to file