input_answer = args.input
n_dims = 3
n_dims_half = n_dims / 2
# 配線を表示する
def show_board(_board, show_float=False):
shstr = [' ', ' │ ', '─┘ ', ' └─', '─┐ ', ' ┌─', '───']
idx = 0
for y in range(n_dims_half, board_y + n_dims_half):
for x in range(n_dims_half, board_x + n_dims_half):
if _board[y][x]['type'] == 1:
sys.stdout.write('\033[1;30;47m {} \033[0m'.format(nl.int2str(_board[y][x]['data'], 36)))
else:
# 正しい配線形状
fr_color = '30'
# 途切れてないセル / 途切れてるセル
bg_color = '47'
if show_float:
if _board[y][x]['float'] != None:
bg_color = '43'
sys.stdout.write('\033[1;{};{}m{}\033[0m'.format(fr_color, bg_color, shstr[_board[y][x]['shape']]))
idx = idx + 1
print ''
board_x, board_y, board = nl.read_ansfile(input_answer, n_dims)
show_board(board)
# Learning loop
x_train_raw, y_train_raw = [], []
x_test_raw, y_test_raw = [], []
# トレーニング/テスト ファイルを読み込む
datafiles = glob.glob(DIR_DATA + '/*.txt')
for datafile in datafiles:
path_datafile = datafile.replace('\\', '/').split('/')
datafilename = path_datafile[-1]
datafilename_woext = datafilename[0:-4]
# Training data
if datafilename != testfilename and datafilename_woext != testfilename:
print 'Reading train file: {}/{} ...'.format(DIR_DATA, datafilename)
_board_x, _board_y, _board = nl.read_ansfile(datafile, n_dims)
x_data, y_data = nl.gen_dataset_shape(_board_x, _board_y, _board, n_dims, args.dataset) # 配線形状の分類
#x_data, y_data = nl.gen_dataset_dirsrc(board_x, board_y, board, n_dims) # 配線接続位置の分類 (ソースから)
#x_data, y_data = nl.gen_dataset_dirsnk(board_x, board_y, board, n_dims) # 配線接続位置の分類 (シンクから)
x_train_raw = x_train_raw + x_data
y_train_raw = y_train_raw + y_data
# Test data
else:
print 'Reading test file: {}/{} ...'.format(DIR_DATA, datafilename)
board_x, board_y, board = nl.read_ansfile(datafile, n_dims)
x_data, y_data = nl.gen_dataset_shape(board_x, board_y, board, n_dims, args.dataset) # 配線形状の分類
#x_data, y_data = nl.gen_dataset_dirsrc(board_x, board_y, board, n_dims) # 配線接続位置の分類 (ソースから)
#x_data, y_data = nl.gen_dataset_dirsnk(board_x, board_y, board, n_dims) # 配線接続位置の分類 (シンクから)
if _from == 'east':
return _find_path(_board, x - 1, y, 'east', depth) + cur
elif _from == 'west':
return _find_path(_board, x + 1, y, 'west', depth) + cur
else:
return []
else:
#raise Exception('Never reachable here')
return []
# Testing phase
if (not args.answer):
board_x, board_y, board = nl.read_probfile(input_problem, n_dims)
else:
board_x, board_y, board = nl.read_ansfile(input_problem, n_dims)
x_data, _ = nl.gen_dataset_shape(board_x, board_y, board, n_dims, dataset)
x_test = np.array(x_data, dtype=np.float32)
result = evaluate(x_test)
# board_pr: 予想される配線のボード
# `wrong` に間違った配線形状かどうか記録する
board_pr = copy.deepcopy(board)
idx = 0
for y in range(n_dims_half, board_y + n_dims_half):
for x in range(n_dims_half, board_x + n_dims_half):
if board_pr[y][x]['type'] != 1:
board_pr[y][x]['shape'] = np.argmax(result.data[idx])
if board[y][x]['type'] != board_pr[y][x]['type'] or board[y][x]['shape'] != board_pr[y][x]['shape']: