def generate(bot, update, args):
'''
Generate random message starting from words given (/with command)
'''
argument = ' '.join(args)
B = Board(9)
B.generate_puzzle(1, to_remove=30)
draw_matrix(B.grid, name='puzzle.png')
bot.send_photo(chat_id=update.message.chat_id,
photo=open('puzzle.png', 'rb'))
from sklearn.preprocessing import StandardScaler
import sys
import argparse
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('task', help='Solve or generate')
parser.add_argument('-i', help='Image to solve')
parser.add_argument('--method', help='Which ML algorithm to use - cnn or knn')
parser.add_argument(
'--model', help='Location of the model.json and model.h5 files for CNN')
args = parser.parse_args()
if args.task == 'generate':
B = Board(9)
B.generate_puzzle(1, to_remove=30)
draw_matrix(B.grid, name='puzzle.png')
# solution_img(B.solution(), B.grid, 'solution.png')
sys.exit()
elif args.task == 'solve':
img = imread(args.i, IMREAD_GRAYSCALE)
if args.method == 'knn':
scaler = StandardScaler()
model = KNeighborsClassifier(n_neighbors=3)
x_train, y_train = generate_dataset(250)
x_train = x_train.reshape(250, 784)
scaler.fit(x_train)
model.fit(x_train, y_train)
elif args.method == 'cnn':
json_file = open(args.model + '.json', 'r')
loaded_model_json = json_file.read()
