def __init__(self, environment, social_vector, strategy, my_index,
noise = 0, prob_explore = 0.5, random_explore = True, log_file = None):
self.strategy = strategy
assert strategy in ['asocial', 'smart', 'naive_copy', 'move_to_center', 'move_to_closest']
self.noise = noise
self.random_explore = random_explore
self.prob_explore = prob_explore
self.world = environment(None)
self.last_pos = None
self.total_score = 0
self.time = -1
self.state = 'exploring'
self.last_state = 'exploring'
self.explore_goal = None
self.copy_goal = None
self.exploit_goal = None
self.my_index = my_index
social_vector[my_index] = False
self.social_vector = [False]*len(social_vector) if strategy == 'asocial' else social_vector
if not self.random_explore:
self.model = inference.Model(lambda: SpotlightBackgroundDiscrete(self.world.edge_goal),
n_samples = 500)
self.goal = self.model.resample(None, None, None)
else :
self.goal = self.world.get_random_position()
self.turn = np.random.choice(['left','right'])
self.copy_targeted = self.strategy in ['smart']
def __init__(self,
par,
score_field_model,
environment,
n_samples=1000,
social_info='spinning',
social_particles=False,
infer_score=False,
ideal=False,
watch_others=True):
#self.threshold = threshold
self.social_info = social_info
self.social_particles = social_particles
self.ideal = ideal
self.infer_score = infer_score
self.watch_others = watch_others
self.model = inference.Model(lambda: score_field_model(noise=par),
n_samples=n_samples)
#self.model = inference.Model(n_samples = n_samples, noise = par)
self.world = environment(None)
self.cache = heuristic.Cacher(self.world)
self.turn = np.random.choice(['left', 'right'])
self.goal = None
self.belief = None
self.last_pos = None
self.time = -1
def __init__(self,
pars,
score_field_model,
environment,
memory=16,
n_samples=500,
watch_others=True,
stop_and_click=False):
self.par = pars
#self.memory = memory
self.watch_others = watch_others
self.model = inference.Model(lambda: score_field_model(noise=pars),
n_samples=n_samples)
self.environment = environment
self.goal = None
self.belief = None
self.goal_models = None
self.last_score = None
self.last_pos = None
self.time = -1
self.stop_and_click = stop_and_click
def __init__(self, data, inactive):
self.inference_model = inference.Model()
active_players = get_active_players(data, inactive)
self.player_data = dict([(p, data.loc[data['pid'] == p,:].copy()) for p in active_players])
for p in self.player_data:
self.player_data[p].index = self.player_data[p].tick
self.ticks = sorted(set(data['tick']))
self.goal_models = {}
self.caches = {}
for p in self.player_data:
self.goal_models[p] = goal_inference.Model(n_samples = 100)
self.caches[p] = cache.Model(memory = 16)
def process_data(data, inactive, verbose = True):
"""
>>> np.random.seed(1)
>>> success, df = process_data(pd.DataFrame({'tick':range(24)+range(24)+range(24)+range(23)+range(23)+range(23),'pid':[1]*24+[2]*24+[3]*24+[4]*23+[5]*23+[6]*23,'x_pos':range(24)+[100]*24+range(24)+range(23)+[100]*23+[100]*23,'y_pos':range(24)+[100]*24+range(24)+range(23)+[100]*23+[100]*23,'velocity':[10]*24+[1]*24+[1]*24+[1]*23+[1]*23+[1]*23,'angle':[135]*24+range(24)+[135]*24+[135]*23+range(23)+range(23),'bg_val':[0]*24+[1]*24+[0]*24+[0]*23+[1]*23+[1]*23}),{}, verbose = False)
>>> len(df)
141
>>> df.iloc[60]
angle 135
bg_val 0
pid 1
tick 10
velocity 10
x_pos 10
y_pos 10
nearby-1 False
facing-1 False
copying-1 False
nearby-2 False
facing-2 True
copying-2 True
nearby-3 False
facing-3 False
copying-3 False
nearby-4 False
facing-4 False
copying-4 False
nearby-5 False
facing-5 True
copying-5 True
nearby-6 False
facing-6 True
copying-6 True
spinning False
staying False
going_straight True
moving_fast True
nearby False
nearby_spinning False
have_been_nearby False
since_nearby NaN
copying True
state copying
other_exploiting True
other_spinning True
copying_exploiting True
score_increased False
score_decreased False
uncertainty_increased True
score_decreased_from_one False
turning False
turning_towards_others False
turning_towards_spinning False
turning_towards_beliefs False
facing True
facing_spinning True
copying_reward 1
uncertainty 8484.406
others_cov 0
ave_dist_others 76.36753
spinning_cov 0
dist_to_mean_others 76.36753
dist_to_mean_spinning 127.2792
dist_to_mean_beliefs 353.2469
angle_to_mean_others 1.490116e-08
angle_to_mean_spinning 1.490116e-08
angle_to_mean_beliefs 0.3436884
Name: 10, Length: 59, dtype: object
>>> df.iloc[61]
angle 10
bg_val 1
pid 2
tick 10
velocity 1
x_pos 100
y_pos 100
nearby-1 False
facing-1 False
copying-1 False
nearby-2 False
facing-2 False
copying-2 False
nearby-3 False
facing-3 False
copying-3 False
nearby-4 False
facing-4 False
copying-4 False
nearby-5 True
facing-5 False
copying-5 False
nearby-6 True
facing-6 False
copying-6 False
spinning True
staying True
going_straight False
moving_fast False
nearby True
nearby_spinning True
have_been_nearby True
since_nearby 0
copying False
state exploiting
other_exploiting True
other_spinning True
copying_exploiting False
score_increased False
score_decreased False
uncertainty_increased False
score_decreased_from_one False
turning True
turning_towards_others False
turning_towards_spinning False
turning_towards_beliefs False
facing False
facing_spinning False
copying_reward NaN
uncertainty 658.3822
others_cov 0
ave_dist_others 76.36753
spinning_cov NaN
dist_to_mean_others 76.36753
dist_to_mean_spinning 0
dist_to_mean_beliefs 2.428225
angle_to_mean_others 0.9599311
angle_to_mean_spinning 0
angle_to_mean_beliefs 0.8797644
Name: 10, Length: 59, dtype: object
>>> df.iloc[62]
angle 135
bg_val 0
pid 3
tick 10
velocity 1
x_pos 10
y_pos 10
nearby-1 False
facing-1 False
copying-1 False
nearby-2 False
facing-2 True
copying-2 False
nearby-3 False
facing-3 False
copying-3 False
nearby-4 True
facing-4 False
copying-4 False
nearby-5 False
facing-5 True
copying-5 False
nearby-6 False
facing-6 True
copying-6 False
spinning False
staying True
going_straight True
moving_fast False
nearby True
nearby_spinning False
have_been_nearby True
since_nearby 0
copying False
state exploiting
other_exploiting True
other_spinning True
copying_exploiting False
score_increased False
score_decreased False
uncertainty_increased False
score_decreased_from_one False
turning False
turning_towards_others False
turning_towards_spinning False
turning_towards_beliefs False
facing True
facing_spinning True
copying_reward NaN
uncertainty 8573.016
others_cov 0
ave_dist_others 76.36753
spinning_cov 0
dist_to_mean_others 76.36753
dist_to_mean_spinning 127.2792
dist_to_mean_beliefs 347.9206
angle_to_mean_others 1.490116e-08
angle_to_mean_spinning 1.490116e-08
angle_to_mean_beliefs 0.3277598
Name: 10, Length: 59, dtype: object
>>> u = list(df.loc[10,'uncertainty'])
>>> u[1] < u[0] and u[1] < u[2] and u[1] < u[3]
True
>>> abs(u[0] - u[3])/u[3] < 0.2 and abs(u[0] - u[2])/u[2] < 0.2 and abs(u[2] - u[3])/u[3] < 0.2
True
"""
player_data = get_player_data(data, inactive)
if len(player_data) == 0:
return False, None
models = dict(zip(player_data.keys(), [inference.Model() for p in player_data]))
ticks = sorted(set(data['tick']))
for t in ticks:
if t % 100 == 0 and verbose:
print 'init', t
for p in player_data:
update_characteristics(player_data, models, p, t)
for t in ticks:
if t % 100 == 0 and verbose:
print 'next', t
for p in player_data:
update_post_pass(player_data, p, t)
df = pd.concat([player_data[p] for p in player_data])
df = df.sort(['tick','pid'])
return True, df
this_out_dir = out_dir + game[:-4] + '/images/'
try:
os.makedirs(this_out_dir)
except:
pass
df = pd.read_csv(in_dir + game)
players = list(set(df['pid']))
#np.random.shuffle(players)
if micro_experiments:
player = 1
if alt_background:
model = smart_particle.Model(lambda: SideBackgroundDiscrete(noise=0.1),
n_samples=1000)
else:
model = smart_particle.Model(
lambda: JumpingBackgroundDiscrete(noise=0.1), n_samples=1000)
else:
models = {}
for p in players:
models[p] = smart_particle.Model(n_samples=1000)
cm = plt.cm.get_cmap('Greens')
ticks = list(set(df['tick']))
for tick in range(max(ticks) + 1):
if micro_experiments:
def process_data(data, inactive, verbose=True):
"""
>>> success, df = process_data(pd.DataFrame({'tick':range(24)+range(24)+range(24)+range(24),'pid':[1]*24+[2]*24+[3]*24+[4]*24,'x_pos':range(24)+[100]*24+range(24)+range(24),'y_pos':range(24)+[100]*24+range(24)+range(24),'velocity':[10]*24+[1]*24+[1]*24+[1]*24,'angle':[135]*24+range(24)+[135]*24+[135]*24,'bg_val':[0]*24+[1]*24+[0]*24+[0]*24}),{}, verbose = False)
>>> df.iloc[10,0:(len(df.columns)-1)]
angle 135
bg_val 0
pid 1
tick 10
velocity 10
x_pos 10
y_pos 10
nearby-1 False
facing-1 False
copying-1 False
nearby-2 False
facing-2 True
copying-2 True
nearby-3 False
facing-3 False
copying-3 False
nearby-4 False
facing-4 False
copying-4 False
spinning False
staying False
going_straight True
moving True
nearby False
have_been_nearby False
since_nearby NaN
copying True
state copying
other_exploiting True
copying_exploiting True
copying_reward 1
Name: 10, dtype: object
>>> df.iloc[34,0:(len(df.columns)-1)]
angle 10
bg_val 1
pid 2
tick 10
velocity 1
x_pos 100
y_pos 100
nearby-1 False
facing-1 False
copying-1 False
nearby-2 False
facing-2 False
copying-2 False
nearby-3 False
facing-3 False
copying-3 False
nearby-4 False
facing-4 False
copying-4 False
spinning True
staying True
going_straight False
moving False
nearby False
have_been_nearby False
since_nearby NaN
copying False
state exploiting
other_exploiting True
copying_exploiting False
copying_reward NaN
Name: 10, dtype: object
>>> df.iloc[58,0:(len(df.columns)-1)]
angle 135
bg_val 0
pid 3
tick 10
velocity 1
x_pos 10
y_pos 10
nearby-1 False
facing-1 False
copying-1 False
nearby-2 False
facing-2 True
copying-2 False
nearby-3 False
facing-3 False
copying-3 False
nearby-4 True
facing-4 False
copying-4 False
spinning False
staying True
going_straight True
moving False
nearby True
have_been_nearby True
since_nearby 0
copying False
state exploiting
other_exploiting True
copying_exploiting False
copying_reward NaN
Name: 10, dtype: object
>>> u = list(df.loc[10,'uncertainty'])
>>> u[1] < u[0] and u[1] < u[2] and u[1] < u[3]
True
>>> abs(u[0] - u[3])/u[3] < 0.2 and abs(u[0] - u[2])/u[2] < 0.2 and abs(u[2] - u[3])/u[3] < 0.2
True
"""
player_data = get_player_data(data, inactive)
if len(player_data) == 0:
return False, None
models = dict(
zip(player_data.keys(), [inference.Model() for p in player_data]))
ticks = sorted(set(data['tick']))
for t in ticks:
if t % 100 == 0 and verbose:
print 'init', t
for p in player_data:
update_characteristics(player_data, models, p, t)
df = pd.concat([player_data[p] for p in player_data])
return True, df