def get_features(board, zoid_str, controller, dictionaries=True):
""" Given a board (with list of list representation), future zoid,
and controller, this function returns the set of all possible options
and their feature values.
Params:
board - [[int]] - list of list representation of board
zoid_str - string - string value for a zoid (e.g. "T")
controller - {string: int} - dictionary controller for a simulator
If dictionaries=True then it will return it as a series of nested
dictionaries organized as:
return_val[orientation][row][col] = {zoid: '', board: [[]], features: {}}
If dictionaries=False then it will return it as a list of dictionaries,
with one dictionary for each option/feature set:
return_val = [{'zoid': zoid, 'orient': orientation, 'row': row,
'col': col, 'board': [[]], 'features': {}}]
"""
sim = TetrisSimulator(controller=controller)
sim.space = tetris_cow2.convert_old_board(board)
sim.curr_z = all_zoids[zoid_str]
if dictionaries == True:
# Return all features as an organized dictionary structure
all_feats = {}
for orient, pos in sim.get_options():
if orient not in all_feats.keys():
all_feats[orient] = {}
if pos[0] not in all_feats[orient].keys():
all_feats[orient][pos[0]] = {}
zoid = sim.curr_z.get_copy()
board = sim.space.get_cow()
# Not really sure what the value=2 does, but was used in simulator.py
board.imprint_zoid(zoid, orient=orient, pos=pos, value=2)
board_and_feats = {
'zoid': zoid_str,
'board': board.row_space(),
'features': sim.get_features(board, sim.space)
}
all_feats[orient][pos[0]][pos[1]] = board_and_feats
return all_feats
else:
all_feats = []
for orient, pos in sim.get_options():
option = {}
zoid = sim.curr_z.get_copy()
option['zoid'] = zoid_str
option['row'] = pos[0]
option['col'] = pos[1]
option['orient'] = orient
board = sim.space.get_cow()
board.imprint_zoid(zoid, orient=orient, pos=pos, value=2)
option['board'] = board.row_space()
option['features'] = sim.get_features(board, sim.space)
all_feats.append(option)
return all_feats
def predict(space, zoid, controller):
sim = TetrisSimulator(controller = controller)
sim.space = sim.convert_space(space)
sim.curr_z = zoid
sim.get_options()
return sim.control()
player_board = [[0]*10 for i in range(20)]
else:
prev_line = lines[x - 1]
prev_line = prev_line.rstrip().split('\t')
player_board = eval(eval(prev_line[board_ix]))
line = line.rstrip().split('\t')
if line[curr_zoid_ix] != 'NA':
player_zoid = line[curr_zoid_ix]
working_controller = CERLscore
working_features = CERLscore.keys()
sim = TetrisSimulator(controller=working_controller)
sim.space = tetris_cow2.convert_old_board(player_board)
sim.curr_z = all_zoids[player_zoid]
feats = get_features(player_board, player_zoid, working_controller, dictionaries=False)
for f in feats:
lh_val = f['features']['landing_height'] * working_controller['landing_height']
ec_val = f['features']['eroded_cells'] * working_controller['eroded_cells']
rt_val = f['features']['row_trans'] * working_controller['row_trans']
ct_val = f['features']['col_trans'] * working_controller['col_trans']
pit_val = f['features']['pits'] * working_controller['pits']
wells_val = f['features']['cuml_wells'] * working_controller['cuml_wells']
move_score = lh_val + ec_val + rt_val + ct_val + pit_val + wells_val
out_line = ""
out_line += (str(line[ep_num_ix]) + "\t")
