def get_final(self, battle):
mat = self.get_final_matrix(battle)
rstrats0, rstrats1, value = williams_solve_old(mat.tolist(), 100)
strats = [normalize(rstrats0), normalize(rstrats1)]
strat = strats[self.army.armyid]
# logger.debug("Beststrats (A/D/I): {:4.3f}/{:4.3f}/{:4.3f} vs {:4.3f}/{:4.3f}/{:4.3f}, value={}".format(*(strats[0] + strats[1]), value))
battle.battlescreen.yprint(
"Beststrats (A/D/I): {:4.3f}/{:4.3f}/{:4.3f} vs {:4.3f}/{:4.3f}/{:4.3f}, value={}"
.format(*(strats[0] + strats[1]), value),
mode=['AI'])
battle.battlescreen.yprint(
"Nash equilibria (A/D/I): {:4.3f}/{:4.3f}/{:4.3f}".format(*strat),
mode=['AI'])
return rps.FinalOrder(np.random.choice(FINAL_ORDER_LIST, p=strat))
def __calculate_feature_vector(traj, point_index, args, delayed_strokes=None):
# calculating number of features is not pretty because dir, curv and bitmap are actually more than one feature...
num_features = len(args)
if "dir" in args:
num_features += 1
if "curv" in args:
num_features += 1
if "bitmap" in args:
num_features += 8
if len(traj) < 5:
return np.zeros(num_features)
feat_vec = []
if "dir" in args:
feat_vec.extend(__writing_direction(traj, point_index))
if "curv" in args:
feat_vec.extend(__curvature(traj, point_index))
if "penup" in args:
feat_vec.append(__is_penup(traj, point_index))
if "hat" in args:
feat_vec.append(__hat(traj, point_index, delayed_strokes))
if "vic_aspect" in args:
feat_vec.append(__vicinity_aspect(traj, point_index))
if "vic_curl" in args:
feat_vec.append(__vicinity_curliness(traj, point_index))
if "vic_line" in args:
feat_vec.append(__vicinity_lineness(traj, point_index))
if "vic_slope" in args:
feat_vec.append(__vicinity_slope(traj, point_index))
if "bitmap" in args:
feat_vec.extend(__context_bitmap(traj, point_index))
return np.array(mathutils.normalize(feat_vec))
def __calculate_feature_vector(traj, point_index, args, delayed_strokes=None):
# calculating number of features is not pretty because dir, curv and bitmap are actually more than one feature...
num_features = len(args)
if "dir" in args:
num_features += 1
if "curv" in args:
num_features += 1
if "bitmap" in args:
num_features += 8
if len(traj) < 5:
return np.zeros(num_features)
feat_vec = []
if "dir" in args:
feat_vec.extend(__writing_direction(traj, point_index))
if "curv" in args:
feat_vec.extend(__curvature(traj, point_index))
if "penup" in args:
feat_vec.append(__is_penup(traj, point_index))
if "hat" in args:
feat_vec.append(__hat(traj, point_index, delayed_strokes))
if "vic_aspect" in args:
feat_vec.append(__vicinity_aspect(traj, point_index))
if "vic_curl" in args:
feat_vec.append(__vicinity_curliness(traj, point_index))
if "vic_line" in args:
feat_vec.append(__vicinity_lineness(traj, point_index))
if "vic_slope" in args:
feat_vec.append(__vicinity_slope(traj, point_index))
if "bitmap" in args:
feat_vec.extend(__context_bitmap(traj, point_index))
return np.array(mathutils.normalize(feat_vec))
def __pyramid_to_descriptor(self, descriptor_pyramid):
"""
Calculates descriptor by flattening the spatial bins in descriptor_pyramid.
:return: One-dimensional array representing descriptor of a word-snippet.
"""
flattened = []
for index, level in enumerate(descriptor_pyramid):
tmp = []
for horiz_bin in level:
for vert_bin in horiz_bin:
tmp.extend(mathutils.normalize(vert_bin))
# tmp.extend(vert_bin)
flattened.extend(tmp)
# return np.array(mathutils.normalize(flattened))
return mathutils.normalize(np.array(mathutils.power_normalize(flattened)))
def __pyramid_to_descriptor(self, descriptor_pyramid):
"""
Calculates descriptor by flattening the spatial bins in descriptor_pyramid.
:return: One-dimensional array representing descriptor of a word-snippet.
"""
flattened = []
for index, level in enumerate(descriptor_pyramid):
tmp = []
for horiz_bin in level:
for vert_bin in horiz_bin:
tmp.extend(mathutils.normalize(vert_bin))
# tmp.extend(vert_bin)
flattened.extend(tmp)
# return np.array(mathutils.normalize(flattened))
return mathutils.normalize(
np.array(mathutils.power_normalize(flattened)))
def get_final(self, bat):
choose_expert = np.random.choice(
[self.expert_yomi_1, self.expert_yomi_2, self.expert_yomi_3],
p=[0.6, 0.3, 0.1])
strat = choose_expert(bat)
strat += self.expert_commitment(bat)
m = min(strat)
if m < 0:
strat += np.array([1 - m, 1 - m, 1 - m])
assert min(strat) > 0
nstrat = normalize(strat)
return rps.FinalOrder(np.random.choice(FINAL_ORDER_LIST, p=nstrat))
def get_formation(self, bat):
choose_expert = np.random.choice(
[self.expert_yomi_1, self.expert_yomi_2, self.expert_yomi_3],
p=[0.6, 0.3, 0.1])
strat = choose_expert(bat)
m = min(strat)
if m < 0:
strat += np.array([1 - m, 1 - m, 1 - m])
assert min(strat) > 0
nstrat = normalize(strat)
return rps.FormationOrder(
np.random.choice(FORMATION_ORDER_LIST, p=nstrat))
def __context_bitmap(traj, point_idx, bin_size=10):
# the current point lies in the center of the bitmap and we use a 3x3 grid around that point
window_origin_x = traj[point_idx][0] - 3 * bin_size / 2
window_origin_y = traj[point_idx][1] - 3 * bin_size / 2
bitmap = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
num_points = 0
for p in traj:
bin_x = int((p[0] - window_origin_x) / bin_size)
bin_y = int((p[1] - window_origin_y) / bin_size)
if 0 <= bin_x <= 2 and 0 <= bin_y <= 2:
bitmap[bin_y][bin_x] += 1
num_points += 1
return mathutils.normalize(
np.array([p / float(num_points) for bin in bitmap for p in bin]))
def __context_bitmap(traj, point_idx, bin_size=10):
# the current point lies in the center of the bitmap and we use a 3x3 grid around that point
window_origin_x = traj[point_idx][0] - 3 * bin_size / 2
window_origin_y = traj[point_idx][1] - 3 * bin_size / 2
bitmap = [[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]
num_points = 0
for p in traj:
bin_x = int((p[0] - window_origin_x) / bin_size)
bin_y = int((p[1] - window_origin_y) / bin_size)
if 0 <= bin_x <= 2 and 0 <= bin_y <= 2:
bitmap[bin_y][bin_x] += 1
num_points += 1
return mathutils.normalize(np.array([p / float(num_points) for bin in bitmap for p in bin]))
