def poll(self, poll_time, on_enter=None, on_exit=None):
print('starting polling')
history = RingBuffer(capacity=20, dtype=np.float)
entered = False
while True:
dist = self.distance()
history.append(dist)
if len(history) < 10:
continue
avg = np.median(history)
if DEBUG:
sys.stdout.write('\rdist: {:06.10f} avg: {:06.10f}'.format(
dist, avg))
if not entered and avg < THRESHOLD:
entered = True
if on_enter:
on_enter()
elif entered and avg > THRESHOLD:
entered = False
if on_exit:
on_exit()
time.sleep(poll_time)
def test_acceptance(self):
instance = RingBuffer(5)
self.assertEqual(instance.write([1, 2, 3]), 3)
self.assertEqual(instance.write([4, 5, 6]), 2)
self.assertEqual(instance.read(), 1)
self.assertEqual(instance.read(), 2)
self.assertEqual(instance.read(), 3)
def test_remove(self):
rb = RingBuffer(4)
rs = RingStack(4)
rq = RingQueue(4)
ls = ListStack(4)
lq = ListQueue(4)
for name in TestRingBufferDS.names:
rb.insert_keep_old(name)
rs.insert(name)
rq.insert(name)
ls.insert(name)
lq.insert(name)
print("Before remove: ")
print("RingBuffer:", rb)
print("RingStack:", rs)
print("RingQueue:", rq)
print("ListStack:", ls)
print("ListQueue:", lq)
for i in range(2):
rs.remove()
rq.remove()
ls.remove()
lq.remove()
print("After remove: ")
print("RingBuffer:", rb)
print("RingStack:", rs)
print("RingQueue:", rq)
print("ListStack:", ls)
print("ListQueue:", lq)
def __init__(self, capacity):
if capacity < 0:
print("Cannot have a negative list")
exit()
elif isinstance(capacity, int):
self.ring_buffer = RingBuffer(capacity)
else:
print("Incorrect format of Capacity")
def __init__(self, capacity):
# store as instance variable
self._capacity = capacity
# create list of size capacity
self.RingBuffer = RingBuffer(self._capacity)
# set other instance variable defaults
self.top = -1
self.size = 0
def test_add_values_replaces_entire_queue(): ring_buffer = RingBuffer([x for x in range(5)], 5) ring_buffer.add_values([ x for x in range(50) if x % 10 == 0 ]) expected_queue = deque([0, 10, 20, 30, 40]) assert ring_buffer.queue == expected_queue
def test_add_values_replaces_newest_values(): ring_buffer = RingBuffer([x for x in range(5)], 5) ring_buffer.add_values([ x for x in range(100) if x % 10 == 0 ]) expected_queue = deque([50, 60, 70, 80, 90]) assert ring_buffer.queue == expected_queue
def test_basic_wrap(self):
b = RingBuffer(4)
b.write(b"ab")
self.assertEqual(b.read(), b"ab")
self.assertEqual(b.bytes_used(), 0)
b.write(b"cde")
self.assertEqual(b.read(), b"cd")
self.assertEqual(b.read(), b"e")
self.assertEqual(b.read(), None)
def __init__(self):
self.last5_left = RingBuffer(5)
self.last5_right = RingBuffer(5)
self.avg_left_fit = 0
self.avg_right_fit = 0
self.avg_left_curv = 0
self.avg_right_curv = 0
self.avg_left_dist = 0
self.avg_right_dist = 0
def __init__(self, output_size, layers, memory_size=3000, batch_size=32,
use_ddqn=False, default_policy=False, model_filename=None, tb_dir="tb_log",
epsilon=1, epsilon_lower_bound=0.1, epsilon_decay_function=lambda e: e - (0.9 / 1000000),
gamma=0.95, optimizer=RMSprop(0.00025), learn_thresh=50000,
update_rate=10000):
"""
Args:
output_size: number of actions
layers: list of Keras layers
memory_size: size of replay memory
batch_size: size of batch for replay memory
use_ddqn: boolean for choosing between DQN/DDQN
default_policy: boolean for loading a model from a file
model_filename: name of file to load
tb_dir: directory for tensorboard logging
epsilon: annealing function upper bound
epsilon_lower_bound: annealing function lower bound
epsilon_decay_function: lambda annealing function
gamma: discount factor hyper parameter
optimizer: Keras optimiser
learn_thresh: number of steps to perform without learning
update_rate: number of steps between network-target weights copy
"""
self.output_size = output_size
self.memory = RingBuffer(memory_size)
self.use_ddqn = use_ddqn
self.default_policy = default_policy
# Tensorboard parameters
self.tb_step = 0
self.tb_gather = 500
self.tb_dir = tb_dir
if tb_dir is not None:
self.tensorboard = TensorBoard(log_dir='./Monitoring/%s' % tb_dir, write_graph=False)
print("Tensorboard Loaded! (log_dir: %s)" % self.tensorboard.log_dir)
# Exploration/Exploitation parameters
self.epsilon = epsilon
self.epsilon_decay_function = epsilon_decay_function
self.epsilon_lower_bound = epsilon_lower_bound
self.total_steps = 0
# Learning parameters
self.gamma = gamma
self.loss = huber_loss #'mean_squared_error'
self.optimizer = optimizer
self.batch_size = batch_size
self.learn_thresh = learn_thresh # Number of steps from which the network starts learning
self.update_rate = update_rate
self.discrete_state = False
if self.default_policy:
self.evaluate_model = self.load_model(model_filename)
else:
self.evaluate_model = self.build_model(layers)
if self.use_ddqn:
self.target_model = self.build_model(layers)
def test_reading_exact_wrapping_singles(self):
b = RingBuffer(4)
b.write(b"ab")
self.assertEqual(b.read(), b"ab")
b.write(b"defg")
self.assertEqual(b.read_exactly(1), b"d")
self.assertEqual(b.read_exactly(1), b"e")
self.assertEqual(b.read_exactly(1), b"f")
def __init__(self, capacity):
"""
initlizes the Stack class
:param own: slef object
:param capacity: Capacity of the Stack
"""
# create list of size capacity
#self.list_stack = [None] * capacity
self.RB = RingBuffer(capacity)
# store as instance variable
self.capacity1 = capacity
# set other instance variable defaults
self.top1 = None
class RingBufferTests(unittest.TestCase):
def setUp(self):
self.buffer = RingBuffer(5)
self.buffer_2 = RingBuffer(5)
def test_ring_buffer(self):
self.assertEqual(len(self.buffer.storage), 5)
self.buffer.append('a')
self.buffer.append('b')
self.buffer.append('c')
self.buffer.append('d')
self.assertEqual(len(self.buffer.storage), 5)
self.assertEqual(self.buffer.get(), ['a', 'b', 'c', 'd'])
def __init__(self, recent_acceptance_lag=None, verbosity=1):
self.start_time = None
self.last_update_time = None
self.verbosity = verbosity
self.recent_acceptance_lag = recent_acceptance_lag
self.last_update_iteration = 0
self.n_iter = None
self.__total_errors__ = 0
self.__iter_time_buffer__ = RingBuffer(100)
self.__text_field__ = ipywidgets.HTML()
self.__error_field__ = None
self.__error_label__ = None
def test_reading_exact_wrapping_full(self):
b = RingBuffer(4)
b.write(b"ab")
self.assertEqual(b.read(), b"ab")
b.write(b"defg")
self.assertEqual(b.read_exactly(4), b"defg")
def test_size_wrap_and_full(self):
b = RingBuffer(2)
b.write(b"a")
self.assertEqual(b.read(), b"a")
self.assertEqual(b.read(), None)
b.write(b"bc")
self.assertEqual(b.read(), b"b")
self.assertEqual(b.read(), b"c")
self.assertEqual(b.read(), None)
def test_read_works_after_several_writes(self):
instance = RingBuffer(3)
self.assertEqual(instance.write([1, 2]), 2)
self.assertEqual(instance.read(), 1)
self.assertEqual(instance.write([3, 4]), 2)
self.assertEqual(instance.read(), 2)
self.assertEqual(instance.write([5, 6]), 1)
self.assertEqual(instance.read(), 3)
def __init__(self, lbuf_size, curr_cycle, num_clients):
self._curr_cycle = curr_cycle # For when a client connects mid game
# Each client has their own buffer of events
# Indexing
self._lockstep_bufs = {RingBuffer(lbuf_size) * 2} * num_clients
self._lbuf_size = lbuf_size
def __init__(self, comparison_function, buffer_len, dimensions,
prest_gamma, prest_lmbda, prest_theta,
pst_gamma, pst_lmbda, pst_theta,
prest_alpha=0.5, prest_beta=0.5, prest_delta=0.5,
prest_eta=0.9995, prest_e_w=0.05, prest_e_n=0.0006,
pst_alpha=0.5, pst_beta=0.75, pst_delta=0.5,
pst_eta=0.9995, pst_e_w=0.05, pst_e_n=0.0006,
ma_window_len=None, ma_recalc_delay=1, ddof=1):
values = inspect.getargvalues(inspect.currentframe())[3]
print('Init parameters: {}'.format(values))
self.comparison_function = comparison_function
self.buffer_len = buffer_len
self.dimensions = dimensions
self.present = mgng.MGNG(dimensions=dimensions,
gamma=int(prest_gamma),
lmbda=int(prest_lmbda),
theta=int(prest_theta),
alpha=float(prest_alpha),
beta=float(prest_beta),
delta=float(prest_delta),
eta=float(prest_eta),
e_w=float(prest_e_w),
e_n=float(prest_e_n))
self.past = mgng.MGNG(dimensions=dimensions,
gamma=int(pst_gamma),
lmbda=int(pst_lmbda),
theta=int(pst_theta),
alpha=float(pst_alpha),
beta=float(pst_beta),
delta=float(pst_delta),
eta=float(pst_eta),
e_w=float(pst_e_w),
e_n=float(pst_e_n))
# self.buffer = deque(maxlen=self.buffer_len)
self.buffer = RingBuffer([[np.nan]*dimensions]*buffer_len)
if ma_window_len is None:
# self.ma_window = deque(maxlen=self.buffer_len)
self.ma_window = RingBuffer([np.nan]*buffer_len)
else:
# self.ma_window = deque(maxlen=ma_window_len)
self.ma_window = RingBuffer([np.nan]*ma_window_len)
self.ma_recalc_delay = ma_recalc_delay
self.ddof = ddof
self.anomaly_mean = None
self.anomaly_std = None
self.t = 0
def __init__(self, update_interval_secs, total_secs=3600, data_type=float):
'''XYBuffer constructor
Args:
update_interval_secs (int): How often is the pyqtgraph updated
total_secs (int): What's the maximum length of the moving window.
Default size 3600 seconds - i.e. 1 hour
data_type(): use numpy data types
'''
self.buffer_size = total_secs // update_interval_secs
self.x_buffer = RingBuffer(size_max=self.buffer_size,
default_value=0,
dtype=np.uint32)
self.y_buffer = RingBuffer(size_max=self.buffer_size,
default_value=0.0,
dtype=data_type)
self._x_counter = 0
def test_size_zero_buffer(self):
b = RingBuffer(0)
self.assertEqual(b.bytes_total(), 0)
self.assertEqual(b.bytes_free(), 0)
self.assertEqual(b.bytes_used(), 0)
with self.assertRaises(ValueError):
b.write(b"a")
class Stack:
__slots__ = "ring_buffer"
def __init__(self, capacity):
if capacity < 0:
print ("Cannot have a negative list")
exit()
elif isinstance(capacity, int):
self.ring_buffer = RingBuffer(capacity)
else:
print ("Incorrect format of Capacity")
def __str__(self):
return self.ring_buffer.__str__()
def push(self, data):
if (data == None):
print ("Data has to be sent!!")
return -1
else:
self.ring_buffer.insert_keep_new(data)
def pop(self):
self.ring_buffer.remove_newest()
def peek(self):
return self.ring_buffer.head
def capacity(self):
self.ring_buffer.capacity()
def test_insert(self):
rb = RingBuffer(4)
rs = RingStack(4)
rq = RingQueue(4)
ls = ListStack(4)
lq = ListQueue(4)
for name in TestRingBufferDS.names:
rb.insert_keep_old(name)
rs.insert(name)
rq.insert(name)
ls.insert(name)
lq.insert(name)
print("RingBuffer:", rb)
print("RingStack:", rs)
print("RingQueue:", rq)
print("ListStack:", ls)
print("ListQueue:", lq)
def test_init(self):
rb = RingBuffer(4)
rs = RingStack(4)
rq = RingQueue(4)
ls = ListStack(4)
lq = ListQueue(4)
self.assertIsNotNone(rb.head)
self.assertEqual(rb.size(), 0)
self.assertEqual(rs.top, -1)
self.assertEqual(rs.size, 0)
self.assertEqual(ls.top, -1)
self.assertEqual(ls.size, 0)
self.assertEqual(rq.front, -1)
self.assertEqual(rq.back, -1)
self.assertEqual(rq.size, 0)
self.assertEqual(lq.front, -1)
self.assertEqual(lq.back, -1)
self.assertEqual(lq.size, 0)
def add_ip(self, ip):
self.__lock.acquire()
try:
current_dt = datetime.datetime.now()
if not self.dictionary.__contains__(ip):
self.dictionary[ip] = RingBuffer(self.__buffer_size)
self.dictionary[ip].append(current_dt)
finally:
self.__lock.release()
def __init__(self, queue):
super().__init__(queue)
self._name = "SoundEffectEngine"
self._chunk = 1024
self._player = pyaudio.PyAudio()
self._wav_dir = config.SOUNDFILE_DIR
self._wav_files = {}
self._cur_wav_file = None
self._stream = None
self._dataFile = open("history.csv", "w")
self._dataWriter = csv.writer(self._dataFile,
delimiter=",",
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
self._currentBpms = RingBuffer(config.AVG_WINDOW)
self._heartbeat = config.HEARTBEAT_TIMEOUT
if config.BPM_RANGE_LOW >= config.BPM_RANGE_HIGH:
raise ValueError(
"BPM Ranges are not configured correctly in config")
class XYBuffer(object):
'''A class for holding X and Y about the sensor
Basically a ring buffer, which serves as the data input for plotting
sensor values as a moving windows in pyqtgraph
'''
def __init__(self, update_interval_secs, total_secs=3600, data_type=float):
'''XYBuffer constructor
Args:
update_interval_secs (int): How often is the pyqtgraph updated
total_secs (int): What's the maximum length of the moving window.
Default size 3600 seconds - i.e. 1 hour
data_type(): use numpy data types
'''
self.buffer_size = total_secs // update_interval_secs
self.x_buffer = RingBuffer(size_max=self.buffer_size,
default_value=0,
dtype=np.uint32)
self.y_buffer = RingBuffer(size_max=self.buffer_size,
default_value=0.0,
dtype=data_type)
self._x_counter = 0
def append_value(self, y_value):
'''Appends the y_value to the y_buffer and increments the sample
number in the x_buffer
'''
self.y_buffer.append(y_value)
self._x_counter += 1
self.x_buffer.append(self._x_counter)
def get_actual_filled_values(self):
'''Gets the values from the buffer, which are already filled
Used for data plotting
'''
return self.x_buffer.partial, self.y_buffer.partial
def __init__(self, recent_acceptance_lag=None, verbosity=1):
self.start_time = None
self.last_update_time = None
self.verbosity = verbosity
self.recent_acceptance_lag = recent_acceptance_lag
self.last_update_iteration = 0
self.n_iter = None
self.__total_errors__ = 0
self.__iter_time_buffer__ = RingBuffer(100)
self.__text_field__ = ipywidgets.HTML()
self.__error_field__ = None
self.__error_label__ = None
class Queue:
__slots__ = 'ring_buffer', 'max_capacity'
"""constructor with intial max capacity of 10"""
def __init__(self, capacity=10):
self.ring_buffer = RingBuffer(capacity)
self.max_capacity = capacity
"""to string function uses the internal to string of ring buffer but
points to the end"""
def __str__(self):
return str(self.ring_buffer) + '<-- end '
""" enqueue function which inserts values in the queue """
def enqueue(self, value):
if value is None:
raise Exception('cannot enqueue None element in the queue')
else:
self.ring_buffer.insert_keep_old(value)
""" dequeue function which removes values from the queue using FIFO
method
raises an exception if dequeue is done on the empty queue"""
def dequeue(self):
if self.ring_buffer.size() is 0:
raise Exception('dequeue from a empty queue is not allowed')
else:
return self.ring_buffer.remove_oldest()
""" returns the current size of the stack"""
def size(self):
return self.ring_buffer.size()
""" :return the max capacity of the stack """
def capacity(self):
return self.ring_buffer.capacity()
""" only returns the value of the first element in the queue does not
removed the element from the queue
"""
def peek(self):
if self.size() < 1:
raise Exception('peek into a empty queue is not allowed')
else:
return self.ring_buffer.start_element()
def __init__(self):
self.state = Transformer.FIRST_INDEX
self.transitions = [
[Transformer.FIRST_INDEX, self.number_exists, self.return_number_as_is, Transformer.SEQUENCE],
[Transformer.FIRST_INDEX, self.number_not_exists, self.return_number_as_is, Transformer.NO_SEQUENCE],
[Transformer.SEQUENCE, self.number_exists, self.return_number_as_is, Transformer.SEQUENCE],
[Transformer.SEQUENCE, self.number_not_exists, self.return_nothing, Transformer.SEQUENCE_BREAK],
[Transformer.SEQUENCE_BREAK, self.number_exists, self.return_number_as_is, Transformer.BUFFERING_SEQUENCE_BREAK], #Brain F**k
[Transformer.SEQUENCE_BREAK, self.number_not_exists, self.return_nothing, Transformer.SEQUENCE_BREAK],
[Transformer.BUFFERING_SEQUENCE_BREAK, all_conditions(self.number_exists, self.is_buffer_not_filled), self.return_number_as_is, Transformer.BUFFERING_SEQUENCE_BREAK],
[Transformer.BUFFERING_SEQUENCE_BREAK, all_conditions(self.number_exists, self.is_buffer_filled), self.interpolate_numbers, Transformer.SEQUENCE],
[Transformer.BUFFERING_SEQUENCE_BREAK, self.number_not_exists, self.return_buffered_numbers_as_is, Transformer.NO_SEQUENCE],
[Transformer.NO_SEQUENCE, self.number_exists, self.return_number_as_is, Transformer.SEQUENCE],
[Transformer.NO_SEQUENCE, self.number_not_exists, self.return_number_as_is, Transformer.NO_SEQUENCE]
]
self.buffer = RingBuffer(length = BUFFER_FILLED_SIZE * 2 + SEQUENCE_BREAK_MAX_SIZE)
class DetectGoal:
_HISTORY_LENGTH = 5
def __init__(self, options):
self.options = options["Goals"]
self.ball_in_field_history = RingBuffer(self.options['HistoryLength'])
self.ball_in_left_goal_history = RingBuffer(2 * self.options['HistoryLength'])
self.ball_in_right_goal_history = RingBuffer(2 * self.options['HistoryLength'])
def detect(self, image, ball):
goal_keep_mask = np.zeros([image.shape[0], image.shape[1]], np.uint8)
height, width, channels = image.shape
# left goal keep
left = self.options['Gates'][0]
cv2.rectangle(goal_keep_mask, tuple(left[0]), tuple(left[1]), (255, 255, 255), thickness=-1)
# right goal keep
right = self.options['Gates'][1]
cv2.rectangle(goal_keep_mask, tuple(right[0]), tuple(right[1]), (255, 255, 255), thickness=-1)
ball_boundaries = ball.get_boundaries(width)
self.ball_in_field_history.extend(np.array(ball.is_visible()))
self.ball_in_left_goal_history.extend(goal_keep_mask[ball_boundaries[0][1], ball_boundaries[0][0]] == 255)
self.ball_in_right_goal_history.extend(goal_keep_mask[ball_boundaries[1][1], ball_boundaries[1][0]] == 255)
if self._detect_goal(self.ball_in_left_goal_history):
return [False, True]
elif self._detect_goal(self.ball_in_right_goal_history):
return [True, False]
else:
return [False, False]
def _detect_goal(self, ball_in_goal_area_history):
"""
Checks goal within goal area and within history of frames
:param ball_in_goal_area_history:
:return: whether goal was detected
"""
if np.any(ball_in_goal_area_history.get()) and not np.any(self.ball_in_field_history.get()):
ball_in_goal_area_history.clear()
return True
return False
class Stack:
__slots__ = "ring_buffer"
def __init__(self, capacity):
self.ring_buffer = RingBuffer(capacity)
def __str__(self):
return self.ring_buffer.__str__()
def push(self, value):
self.ring_buffer.insert_keep_new(value)
def pop(self):
self.ring_buffer.remove_newest()
def capacity(self):
self.ring_buffer.capacity()
def peek(self):
return self.ring_buffer.head
class Queue:
__slots__ = "ring_buffer", "link"
def __init__(self, capacity):
self.ring_buffer = RingBuffer(capacity)
def __str__(self):
return self.ring_buffer.__str__()
def enqueue(self, value):
self.ring_buffer.insert_keep_old(value)
def dequeue(self):
self.ring_buffer.remove_oldest()
def peek(self):
return self.ring_buffer.head
def capacity(self):
return self.ring_buffer.capacity()
def __init__(self, g, zeta,
leader, trajectory_delay=2.0,
update_delta_t=0.01,
orig_leader=None, orig_leader_delay=None,
noise_sigma=0.0, log_file=None,
visibility_fov=config.FOV_ANGLE, visibility_radius=None,
id=None,
dt=0.02):
"""
Construct a follower Behavior.
leader is the Bot to be followed
g > 0 is a tuning parameter
zeta in (0, 1) is a damping coefficient
trajectory_delay is the time interval between leader and follower
"""
super(Follower, self).__init__()
assert(isinstance(leader, Bot))
assert(isinstance(orig_leader, Bot))
self.radius = config.BOT_RADIUS
self.leader = leader
self.orig_leader = orig_leader
self.trajectory_delay = trajectory_delay
assert g > 0, "Follower: g parameter must be positive"
self.g = g
assert 0 < zeta < 1, "Follower: zeta parameter must be in (0, 1)"
self.zeta = zeta
# leader_positions stores config.POSITIONS_BUFFER_SIZE tuples;
# tuple's first field is time, second is the
# corresponding position of the leader
self.leader_positions = RingBuffer(config.POSITIONS_BUFFER_SIZE)
self.noisy_leader_positions = RingBuffer(config.POSITIONS_BUFFER_SIZE)
self.leader_is_visible = False
# precise_orig_leader_states stores the first leader's precise state;
# used to calculate "real" errors (as opposed to calculations
# w.r.t. the approximation curve)
self.precise_orig_leader_states = []
# precise_leader_states is used for the same purpose, but with the bot's own leader
self.precise_leader_states = []
self.update_delta_t = update_delta_t
needed_buffer_size = config.SAMPLE_COUNT // 2 + self.trajectory_delay / self.update_delta_t
if needed_buffer_size > config.POSITIONS_BUFFER_SIZE:
sys.stderr.write("leader_positions buffer is too small for update_delta_t = {}".format(self.update_delta_t) +
" (current = {}, required = {})".format(config.POSITIONS_BUFFER_SIZE, needed_buffer_size))
raise RuntimeError, "leader_positions buffer is too small"
self.last_update_time = 0.0
self.noise_sigma = noise_sigma
self.log_file = log_file
self.visibility_fov = visibility_fov
if visibility_radius is None:
visibility_radius = 2.0 * trajectory_delay * config.BOT_VEL_CAP
self.visibility_radius = visibility_radius
self.id = id;
if orig_leader_delay is None:
orig_leader_delay = trajectory_delay
self.orig_leader_delay = orig_leader_delay
if self.log_file is not None:
log_dict = {"id": self.id,
"g": self.g,
"zeta": self.zeta,
"noise_sigma": self.noise_sigma,
"reference_points_cnt": config.SAMPLE_COUNT,
"trajectory_delay": trajectory_delay}
print >> self.log_file, log_dict
q = 0.01 # std of process
r = 0.05 # std of measurement
self.delta_time = dt
if not config.DISABLE_UKF:
points = MerweScaledSigmaPoints(n=6, alpha=.1, beta=2., kappa=-3)
self.ukf = UKF(dim_x=6, dim_z=2, fx=f, hx=h, dt=dt, points=points)
self.ukf_x_initialized = 0
#self.ukf.x = np.array([0, 0, 1, pi/4, 0, 0])
self.ukf.R = np.diag([r, r]);
self.ukf.Q = np.diag([0, 0, 0, 0, q, q])
class Follower(BehaviorBase):
def __init__(self, g, zeta,
leader, trajectory_delay=2.0,
update_delta_t=0.01,
orig_leader=None, orig_leader_delay=None,
noise_sigma=0.0, log_file=None,
visibility_fov=config.FOV_ANGLE, visibility_radius=None,
id=None,
dt=0.02):
"""
Construct a follower Behavior.
leader is the Bot to be followed
g > 0 is a tuning parameter
zeta in (0, 1) is a damping coefficient
trajectory_delay is the time interval between leader and follower
"""
super(Follower, self).__init__()
assert(isinstance(leader, Bot))
assert(isinstance(orig_leader, Bot))
self.radius = config.BOT_RADIUS
self.leader = leader
self.orig_leader = orig_leader
self.trajectory_delay = trajectory_delay
assert g > 0, "Follower: g parameter must be positive"
self.g = g
assert 0 < zeta < 1, "Follower: zeta parameter must be in (0, 1)"
self.zeta = zeta
# leader_positions stores config.POSITIONS_BUFFER_SIZE tuples;
# tuple's first field is time, second is the
# corresponding position of the leader
self.leader_positions = RingBuffer(config.POSITIONS_BUFFER_SIZE)
self.noisy_leader_positions = RingBuffer(config.POSITIONS_BUFFER_SIZE)
self.leader_is_visible = False
# precise_orig_leader_states stores the first leader's precise state;
# used to calculate "real" errors (as opposed to calculations
# w.r.t. the approximation curve)
self.precise_orig_leader_states = []
# precise_leader_states is used for the same purpose, but with the bot's own leader
self.precise_leader_states = []
self.update_delta_t = update_delta_t
needed_buffer_size = config.SAMPLE_COUNT // 2 + self.trajectory_delay / self.update_delta_t
if needed_buffer_size > config.POSITIONS_BUFFER_SIZE:
sys.stderr.write("leader_positions buffer is too small for update_delta_t = {}".format(self.update_delta_t) +
" (current = {}, required = {})".format(config.POSITIONS_BUFFER_SIZE, needed_buffer_size))
raise RuntimeError, "leader_positions buffer is too small"
self.last_update_time = 0.0
self.noise_sigma = noise_sigma
self.log_file = log_file
self.visibility_fov = visibility_fov
if visibility_radius is None:
visibility_radius = 2.0 * trajectory_delay * config.BOT_VEL_CAP
self.visibility_radius = visibility_radius
self.id = id;
if orig_leader_delay is None:
orig_leader_delay = trajectory_delay
self.orig_leader_delay = orig_leader_delay
if self.log_file is not None:
log_dict = {"id": self.id,
"g": self.g,
"zeta": self.zeta,
"noise_sigma": self.noise_sigma,
"reference_points_cnt": config.SAMPLE_COUNT,
"trajectory_delay": trajectory_delay}
print >> self.log_file, log_dict
q = 0.01 # std of process
r = 0.05 # std of measurement
self.delta_time = dt
if not config.DISABLE_UKF:
points = MerweScaledSigmaPoints(n=6, alpha=.1, beta=2., kappa=-3)
self.ukf = UKF(dim_x=6, dim_z=2, fx=f, hx=h, dt=dt, points=points)
self.ukf_x_initialized = 0
#self.ukf.x = np.array([0, 0, 1, pi/4, 0, 0])
self.ukf.R = np.diag([r, r]);
self.ukf.Q = np.diag([0, 0, 0, 0, q, q])
def point_in_fov(self, p):
if length(p - self.pos) > self.visibility_radius:
return False
d = p - self.pos
ang = atan2(cross(self.real_dir, d), dot(self.real_dir, d))
return -0.5 * self.visibility_fov < ang < 0.5 * self.visibility_fov
def point_is_visible(self, p, obstacles):
if not self.point_in_fov(p):
return False
try:
ray = Ray(self.pos, p - self.pos)
except NormalizationError:
ray = Ray(self.pos, Vector(1.0, 0.0))
i = first_intersection(ray, obstacles)
return (i is None) or (length(i - self.pos) > length(p - self.pos))
def store_leaders_state(self, engine, obstacles):
if self.point_is_visible(self.leader.real.pos, obstacles):
self.leader_is_visible = True
noisy_pos = self.leader.real.pos
noisy_pos += Vector(gauss(0.0, self.noise_sigma),
gauss(0.0, self.noise_sigma))
if config.DISABLE_UKF:
filtered_pos = noisy_pos
else:
if (self.ukf_x_initialized == 0):
self.ukf_x1 = noisy_pos
self.ukf_x_initialized += 1
filtered_pos = noisy_pos
self.ukf.x = np.array([noisy_pos.x, noisy_pos.y,
0, pi/2, 0, 0])
#elif (self.ukf_x_initialized == 1):
# self.ukf_x2 = noisy_pos
# self.ukf_x_initialized += 1
# self.ukf.x = np.array([noisy_pos.x, noisy_pos.y,
# length((noisy_pos - self.ukf_x1) / self.delta_time),
# atan2(noisy_pos.y - self.ukf_x1.y,
# noisy_pos.x - self.ukf_x1.x),
# 0, 0])
# filtered_pos = noisy_pos
else: # UKF is initialized
self.ukf.predict()
self.ukf.update(np.array(noisy_pos))
filtered_pos = Point(self.ukf.x[0], self.ukf.x[1])
self.leader_noisy_pos = noisy_pos
self.noisy_leader_positions.append(TimedPosition(engine.time, noisy_pos))
self.leader_positions.append(TimedPosition(engine.time, filtered_pos))
self.last_update_time = engine.time
else:
self.leader_is_visible = False
if not config.DISABLE_UKF:
self.ukf.predict()
#TODO: do magic with UKF?
orig_leader_theta = atan2(self.orig_leader.real.dir.y,
self.orig_leader.real.dir.x)
self.precise_orig_leader_states.append(TimedState(time=engine.time,
pos=self.orig_leader.real.pos,
theta=orig_leader_theta))
leader_theta = atan2(self.leader.real.dir.y,
self.leader.real.dir.x)
self.precise_leader_states.append(TimedState(time=engine.time,
pos=self.leader.real.pos,
theta=leader_theta))
def polyfit_trajectory(self, pos_data, t):
x_pos = np.array([el.pos.x for el in pos_data])
y_pos = np.array([el.pos.y for el in pos_data])
times = np.array([el.time for el in pos_data])
# needed for trajectory rendering
self.t_st = times[0]
self.t_fn = max(times[-1], t)
# calculate quadratic approximation of the reference trajectory
x_poly = np.polyfit(times, x_pos, deg=2)
y_poly = np.polyfit(times, y_pos, deg=2)
known = Trajectory.from_poly(x_poly, y_poly)
return known, x_pos[-1], y_pos[-1], times[-1]
def extend_trajectory(self, known, last_x, last_y, last_t):
# now adding a circle to the end of known trajectory
# k is signed curvature of the trajectry at t_fn
try:
k = known.curvature(last_t)
except (ValueError, ZeroDivisionError):
k = config.MIN_CIRCLE_CURVATURE
if abs(k) < config.MIN_CIRCLE_CURVATURE:
k = copysign(config.MIN_CIRCLE_CURVATURE, k)
if abs(k) > config.MAX_CURVATURE:
k = copysign(config.MAX_CURVATURE, k)
radius = abs(1.0/k)
# trajectory direction at time t_fn
try:
d = normalize(Vector(known.dx(last_t), known.dy(last_t)))
except NormalizationError:
d = self.real_dir
r = Vector(-d.y, d.x) / k
center = Point(last_x, last_y) + r
phase = atan2(-r.y, -r.x)
#freq = known.dx(last_t) / r.y
freq = k
self.x_approx = lambda time: known.x(time) if time < last_t else \
center.x + radius * cos(freq * (time - last_t) + phase)
self.y_approx = lambda time: known.y(time) if time < last_t else \
center.y + radius * sin(freq * (time - last_t) + phase)
dx = lambda time: known.dx(time) if time < last_t else \
-radius * freq * sin(freq * (time - last_t) + phase)
dy = lambda time: known.dy(time) if time < last_t else \
radius * freq * cos(freq * (time - last_t) + phase)
ddx = lambda time: known.ddx(time) if time < last_t else \
-radius * freq * freq * cos(freq * (time - last_t) + phase)
ddy = lambda time: known.ddy(time) if time < last_t else \
-radius * freq * freq * sin(freq * (time - last_t) + phase)
# FIXME: don't use division by y if y == 0
try:
if isnan(self.x_approx(last_t + 1)):
return known
except Exception:
return known
return Trajectory(x=self.x_approx, y=self.y_approx,
dx=dx, dy=dy, ddx=ddx, ddy=ddy)
def generate_trajectory(self, leader_positions, t):
arr = get_interval(self.leader_positions, t, config.SAMPLE_COUNT)
self.traj_interval = arr
if len(arr) == 0:
return None
known, last_x, last_y, last_t = self.polyfit_trajectory(arr, t)
if config.DISABLE_CIRCLES:
return known
else:
return self.extend_trajectory(known, last_x, last_y, last_t)
def calc_desired_velocity(self, bots, obstacles, targets, engine):
# update trajectory
if engine.time - self.last_update_time > self.update_delta_t:
self.store_leaders_state(engine, obstacles)
# reduce random movements at the start
# TODO: this causes oscillations that smash bots into each other.
# Change to something smoother. PID control?
#if self.leader_is_visible and length(self.pos - self.leader_noisy_pos) < MIN_DISTANCE_TO_LEADER:
# return Instr(0.0, 0.0)
t = engine.time - self.trajectory_delay
self.trajectory = self.generate_trajectory(self.leader_positions, t)
if self.trajectory is None:
return Instr(0.0, 0.0)
dx = self.trajectory.dx
dy = self.trajectory.dy
ddx = self.trajectory.ddx
ddy = self.trajectory.ddy
# calculate the feed-forward velocities
v_fun = lambda time: sqrt(dx(time)**2 + dy(time)**2)
#omega_fun = lambda time: (dx(time) * 2 * y_poly[0] - dy(time) * 2 * x_poly[0]) / (dx(time)**2 + dy(time)**2)
omega_fun = lambda time: (dx(time) * ddy(time) - dy(time) * ddx(time)) / (dx(time)**2 + dy(time)**2)
v_ff = v_fun(t)
omega_ff = omega_fun(t)
# x_r, y_r and theta_r denote the reference robot's state
r = State(x=self.trajectory.x(t),
y=self.trajectory.y(t),
theta=atan2(self.trajectory.dy(t),
self.trajectory.dx(t)))
self.target_point = Point(r.x, r.y)
if isnan(v_ff):
v_ff = 0.0
if isnan(omega_ff):
omega_ff = 0.0
# cur is the current state
cur = State(x=self.pos.x,
y=self.pos.y,
theta=atan2(self.real_dir.y, self.real_dir.x))
# error in the global (fixed) reference frame
delta = State(x=r.x - cur.x,
y=r.y - cur.y,
theta=normalize_angle(r.theta - cur.theta))
# translate error into the follower's reference frame
e = State(x= cos(cur.theta) * delta.x + sin(cur.theta) * delta.y,
y=-sin(cur.theta) * delta.x + cos(cur.theta) * delta.y,
theta=delta.theta)
# calculate gains k_x, k_y, k_theta
# these are just parameters, not a state in any sense!
omega_n = sqrt(omega_ff**2 + self.g * v_ff**2)
k_x = 2 * self.zeta * omega_n
k_y = self.g
k_theta = 2 * self.zeta * omega_n
# calculate control velocities
v = v_ff * cos(e.theta) + k_x * e.x
se = sin(e.theta) / e.theta if e.theta != 0 else 1.0
omega = omega_ff + k_y * v_ff * se * e.y + k_theta * e.theta
if config.DISABLE_FEEDBACK:
v = v_ff
omega = omega_ff
real_e = State(0.0, 0.0, 0.0)
try:
orig_t = engine.time - self.orig_leader_delay
orig_leader_state = lerp_precise_states(orig_t, self.precise_orig_leader_states)
real_e = State(x=orig_leader_state.x - cur.x,
y=orig_leader_state.y - cur.y,
theta=normalize_angle(orig_leader_state.theta - cur.theta))
except LerpError:
# not enough data is yet available to calculate error
pass
try:
leader_t = engine.time - self.trajectory_delay
leader_state = lerp_precise_states(leader_t, self.precise_leader_states)
approx_e = State(x=leader_state.x - cur.x,
y=leader_state.y - cur.y,
theta=normalize_angle(leader_state.theta - cur.theta))
except LerpError:
# same as above
pass
if self.log_file is not None:
log_dict = {"id": self.id,
"time": engine.time,
"delta_time": engine.time_since_last_bot_update,
"x": cur.x,
"y": cur.y,
"theta": cur.theta,
"v": v,
"omega": omega,
"v_ff": v_ff,
"omega_ff": omega_ff,
"e_x": e.x,
"e_y": e.y,
"e_theta": e.theta,
"real_e_x": real_e.x,
"real_e_y": real_e.y,
"real_e_theta": real_e.theta,
"approx_e_x": approx_e.x,
"approx_e_y": approx_e.y,
"approx_e_theta": approx_e.theta,
"leader_is_visible": self.leader_is_visible
}
print >> self.log_file, log_dict
return Instr(v, omega)
def draw(self, screen, field):
if config.DISPLAYED_POINTS_COUNT > 0:
k = config.POSITIONS_BUFFER_SIZE / config.DISPLAYED_POINTS_COUNT
if k == 0:
k = 1
for index, (time, point) in enumerate(self.leader_positions):
if index % k == 0:
draw_circle(screen, field, config.TRAJECTORY_POINTS_COLOR, point, 0.03, 1)
for index, (time, point) in enumerate(self.noisy_leader_positions):
if index % k == 0:
draw_circle(screen, field, config.NOISY_TRAJECTORY_POINTS_COLOR, point, 0.03, 1)
if config.DISPLAYED_USED_POINTS_COUNT > 0:
k = len(self.traj_interval) / config.DISPLAYED_USED_POINTS_COUNT
if k == 0:
k = 1
for index, (time, point) in enumerate(self.traj_interval):
if index % k == 0:
draw_circle(screen, field, config.USED_TRAJECTORY_POINTS_COLOR, point, 0.03, 1)
if config.DRAW_DELAYED_LEADER_POS:
try:
orig_leader_dir = Vector(cos(self.orig_leader_theta),
sin(self.orig_leader_theta))
draw_directed_circle(screen, field, (0, 255, 0),
self.orig_leader_pos, 0.2,
orig_leader_dir, 1)
except AttributeError:
pass
if config.DRAW_SENSOR_RANGE:
ang = atan2(self.real_dir.y, self.real_dir.x)
draw_arc(screen, field, config.SENSOR_COLOR, self.pos, self.visibility_radius,
ang - 0.5 * self.visibility_fov,
ang + 0.5 * self.visibility_fov,
1)
draw_line(screen, field, config.SENSOR_COLOR,
self.pos,
self.pos + rotate(self.real_dir * self.visibility_radius,
0.5 * self.visibility_fov),
1)
draw_line(screen, field, config.SENSOR_COLOR,
self.pos,
self.pos + rotate(self.real_dir * self.visibility_radius,
-0.5 * self.visibility_fov),
1)
try:
if config.DRAW_VISIBILITY and self.leader_is_visible:
draw_circle(screen, field, config.config.VISIBILITY_COLOR, self.leader.real.pos,
0.5 * config.BOT_RADIUS)
except AttributeError:
pass
try:
if config.DRAW_REFERENCE_POS:
draw_circle(screen, field, config.TARGET_POINT_COLOR, self.target_point, 0.2)
if config.DRAW_APPROX_TRAJECTORY and self.trajectory is not None:
#if len(self.traj_interval) > 1:
# p2 = Point(self.traj_interval[0].pos.x,
# self.traj_interval[0].pos.y)
# for t, p in self.traj_interval:
# draw_line(screen, field, config.APPROX_TRAJECTORY_COLOR, p, p2)
# p2 = p
step = (self.t_fn - self.t_st) / config.TRAJECTORY_SEGMENT_COUNT
for t in (self.t_st + k * step for k in xrange(config.TRAJECTORY_SEGMENT_COUNT)):
p = Point(self.trajectory.x(t),
self.trajectory.y(t))
p2 = Point(self.trajectory.x(t + step),
self.trajectory.y(t + step))
draw_line(screen, field, config.APPROX_TRAJECTORY_COLOR, p, p2)
p_st = Point(self.trajectory.x(self.t_st), self.trajectory.y(self.t_st))
p_fn = Point(self.trajectory.x(self.t_fn), self.trajectory.y(self.t_fn))
step = 0.5 / config.TRAJECTORY_SEGMENT_COUNT
p = p_fn
p2 = p
t = self.t_fn
it = 0
while it < config.TRAJECTORY_SEGMENT_COUNT and min(dist(p, p_fn), dist(p, p_st)) < 1.0:
it += 1
t += step
p2 = p
p = Point(self.trajectory.x(t), self.trajectory.y(t))
draw_line(screen, field, config.APPROX_TRAJECTORY_COLOR, p, p2)
except AttributeError as e: # approximation hasn't been calculated yet
pass
# Some common settings
HISTORY_LENGTH = 4
MAX_STEPS = 1000000
MAX_EPOCHS = 10
MINIBATCH_SIZE = 32
LONG_PRESS_TIMES = 4
GAMMA = 0.9
EPSILON = 0.1
UPDATE_FREQUENCY = 4
MAX_LIVES = ale.lives()
episode_sum = 0
episode_sums = []
# Define history variables here
images = RingBuffer(shape=(MAX_STEPS, 84, 84))
actions = RingBuffer(shape=(MAX_STEPS, 1))
rewards = RingBuffer(shape=(MAX_STEPS, 1))
terminals = RingBuffer(shape=(MAX_STEPS, 1))
# Initialize a neural network according to nature paper
# Defining the neural net architecture
model = Sequential()
model.add(Convolution2D(16, 8, 8, subsample=(4,4), input_shape=(HISTORY_LENGTH,84,84)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 4, 4, subsample=(2,2)))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(legal_actions.shape[0]))
class Transformer:
FIRST_INDEX = "FIRST_INDEX"
SEQUENCE = "SEQUENCE"
NO_SEQUENCE = "NO_SEQUENCE"
SEQUENCE_BREAK = "SEQUENCE_BREAK"
BUFFERING_SEQUENCE_BREAK = "BUFFERING_SEQUENCE_BREAK"
def __init__(self):
self.state = Transformer.FIRST_INDEX
self.transitions = [
[Transformer.FIRST_INDEX, self.number_exists, self.return_number_as_is, Transformer.SEQUENCE],
[Transformer.FIRST_INDEX, self.number_not_exists, self.return_number_as_is, Transformer.NO_SEQUENCE],
[Transformer.SEQUENCE, self.number_exists, self.return_number_as_is, Transformer.SEQUENCE],
[Transformer.SEQUENCE, self.number_not_exists, self.return_nothing, Transformer.SEQUENCE_BREAK],
[Transformer.SEQUENCE_BREAK, self.number_exists, self.return_number_as_is, Transformer.BUFFERING_SEQUENCE_BREAK], #Brain F**k
[Transformer.SEQUENCE_BREAK, self.number_not_exists, self.return_nothing, Transformer.SEQUENCE_BREAK],
[Transformer.BUFFERING_SEQUENCE_BREAK, all_conditions(self.number_exists, self.is_buffer_not_filled), self.return_number_as_is, Transformer.BUFFERING_SEQUENCE_BREAK],
[Transformer.BUFFERING_SEQUENCE_BREAK, all_conditions(self.number_exists, self.is_buffer_filled), self.interpolate_numbers, Transformer.SEQUENCE],
[Transformer.BUFFERING_SEQUENCE_BREAK, self.number_not_exists, self.return_buffered_numbers_as_is, Transformer.NO_SEQUENCE],
[Transformer.NO_SEQUENCE, self.number_exists, self.return_number_as_is, Transformer.SEQUENCE],
[Transformer.NO_SEQUENCE, self.number_not_exists, self.return_number_as_is, Transformer.NO_SEQUENCE]
]
self.buffer = RingBuffer(length = BUFFER_FILLED_SIZE * 2 + SEQUENCE_BREAK_MAX_SIZE)
def number_exists(self, index, number):
return number is not None
def number_not_exists(self, index, number):
return not self.number_exists(index, number)
def return_number_as_is(self, index, number):
self.buffer.add_item((index, number))
return [(index, number)]
def return_nothing(self, index, number):
self.buffer.add_item((index, number))
return []
def interpolate_numbers(self, index, number):
groups = self.buffer.last_item_groups(3, grouper = none_number_grouper)
extraction = sum(groups, [])
e = filter(lambda p: p[1] is not None, extraction)
t = map(lambda p: p[0], e)
n = map(lambda p: p[1], e)
f = interpolate.interp1d(t, n)
d = []
for j, _ in groups[1]:
d.append((j, f(j)))
self.buffer.add_item((index, number))
return d + [(index, number)]
def is_buffer_filled(self, index, number):
group = self.buffer.last_item_group(grouper = none_number_grouper)
print "group = %s" % group
return len(group) >= BUFFER_FILLED_SIZE
def is_buffer_not_filled(self, index, number):
return not self.is_buffer_filled(index, number)
def return_buffered_numbers_as_is(self, index, number):
self.buffer.add_item((index, number))
groups = self.buffer.last_item_groups(2, grouper = none_number_grouper)
print " =========> group = %s" % groups
return groups[1]
def is_sequence_break_too_long(self, index, number):
sequence_break = self.buffer.last_item_group(grouper = none_number_grouper)
return len(sequence_break) > SEQUENCE_BREAK_MAX_SIZE
def is_sequence_break_short_enough(self, index, number):
return not self.is_sequence_break_too_long(index, number)
def transform(self, index, number):
print "transform(%s, %s)" % (index, number)
for transition in self.transitions:
if transition[STATE] == self.state:
print "self.state = %s" % self.state
if transition[CONDITION](index, number):
print " ---> %s success" % transition[CONDITION].__name__
pairs = transition[ACTION](index, number)
next_state = transition[NEXT_STATE]
self.state = next_state
return pairs
else:
print " ---> %s failed" % transition[CONDITION].__name__
raise Exception("Unable to continue")
class JupyterProgress(base.ProgressBase):
def __init__(self, recent_acceptance_lag=None, verbosity=1):
self.start_time = None
self.last_update_time = None
self.verbosity = verbosity
self.recent_acceptance_lag = recent_acceptance_lag
self.last_update_iteration = 0
self.n_iter = None
self.__total_errors__ = 0
self.__iter_time_buffer__ = RingBuffer(100)
self.__text_field__ = ipywidgets.HTML()
self.__error_field__ = None
self.__error_label__ = None
def initialise(self, n_iter):
self.start_time = self.last_update_time = time.time()
self.n_iter = n_iter
if self.recent_acceptance_lag is None:
self.recent_acceptance_lag = min(int(n_iter * 1.0 / 100), max_accept_lag)
self.__text_field__.value = "<i>Waiting for {} iterations to have passed...</i>".format(
self.recent_acceptance_lag
)
# display(self.__progress_field__)
display(self.__text_field__)
def report_error(self, iter, error):
if self.__error_field__ is None:
self.__initialise_errors__()
if self.verbosity > 0:
self.__error_field__.value += "Iteration {}: {}\n".format(iter, error)
self.__total_errors__ += 1
def update(self, iteration, acceptances):
if self.n_iter is None:
raise Exception("First pass in the number of iterations!")
recent_acceptance_lag = self.recent_acceptance_lag
now = time.time()
toc = now - self.last_update_time
do_update = toc > update_frequency_seconds and iteration > self.last_update_iteration
if do_update or iteration == self.n_iter:
delta_accept = (
acceptances[-recent_acceptance_lag:].mean() * 100 if iteration > recent_acceptance_lag else np.nan
)
tot_accept = acceptances.mean() * 100
new_iterations = iteration - self.last_update_iteration
time_per_iter = toc * 1.0 / new_iterations
self.__iter_time_buffer__.append(time_per_iter)
# exclude outliers
all_iter_times = np.array(self.__iter_time_buffer__.data)
if len(all_iter_times) > 1:
all_iter_times = all_iter_times[
abs(all_iter_times - np.mean(all_iter_times)) < 2 * np.std(all_iter_times)
]
time_per_iter = all_iter_times.mean()
eta = (self.n_iter - iteration) * time_per_iter
html = self.__get_text_field__(iteration, self.n_iter, delta_accept, tot_accept, time_per_iter, eta)
self.__text_field__.value = html
self.last_update_time = now
self.last_update_iteration = iteration
def __initialise_errors__(self):
if self.verbosity > 0:
self.__error_label__ = ipywidgets.HTML(value="<span style='color: red'>Errors:</span>")
self.__error_field__ = ipywidgets.Textarea(disabled=True)
display(self.__error_label__)
display(self.__error_field__)
def __get_text_field__(self, iteration, n_iter, delta_accept, total_accept, time_per_iter, eta):
template = """
<div class="progress">
<div class="progress-bar" role="progressbar" aria-valuenow="{}"
aria-valuemin="0" aria-valuemax="{}" style="width:{:.2f}%">
<span class="sr-only">{:.2f}% Complete</span>
</div>
</div>
<table class="table">
<tr>
<td>Current iteration</td>
<td>{}</td>
</tr>
<tr>
<td>Accept rate (last {})</td>
<td>{:.2f}%</td>
</tr>
<tr>
<td>Accept rate (overall)</td>
<td>{:.2f}%</td>
</tr>
<tr>
<td>T/iter</td>
<td>{:.4f} seconds</td>
</tr>
<tr>
<td>ETA</td>
<td>{}</td>
</tr>
<tr>
<td>Errors</td>
<td>{}</td>
</tr>
</table>
"""
return template.format(
iteration,
n_iter,
iteration * 100.0 / n_iter,
iteration * 100.0 / n_iter,
iteration,
self.recent_acceptance_lag,
delta_accept,
total_accept,
time_per_iter,
pretty_time_delta(eta),
self.__total_errors__,
)
class AMGNG:
def __init__(self, comparison_function, buffer_len, dimensions,
prest_gamma, prest_lmbda, prest_theta,
pst_gamma, pst_lmbda, pst_theta,
prest_alpha=0.5, prest_beta=0.5, prest_delta=0.5,
prest_eta=0.9995, prest_e_w=0.05, prest_e_n=0.0006,
pst_alpha=0.5, pst_beta=0.75, pst_delta=0.5,
pst_eta=0.9995, pst_e_w=0.05, pst_e_n=0.0006,
ma_window_len=None, ma_recalc_delay=1, ddof=1):
values = inspect.getargvalues(inspect.currentframe())[3]
print('Init parameters: {}'.format(values))
self.comparison_function = comparison_function
self.buffer_len = buffer_len
self.dimensions = dimensions
self.present = mgng.MGNG(dimensions=dimensions,
gamma=int(prest_gamma),
lmbda=int(prest_lmbda),
theta=int(prest_theta),
alpha=float(prest_alpha),
beta=float(prest_beta),
delta=float(prest_delta),
eta=float(prest_eta),
e_w=float(prest_e_w),
e_n=float(prest_e_n))
self.past = mgng.MGNG(dimensions=dimensions,
gamma=int(pst_gamma),
lmbda=int(pst_lmbda),
theta=int(pst_theta),
alpha=float(pst_alpha),
beta=float(pst_beta),
delta=float(pst_delta),
eta=float(pst_eta),
e_w=float(pst_e_w),
e_n=float(pst_e_n))
# self.buffer = deque(maxlen=self.buffer_len)
self.buffer = RingBuffer([[np.nan]*dimensions]*buffer_len)
if ma_window_len is None:
# self.ma_window = deque(maxlen=self.buffer_len)
self.ma_window = RingBuffer([np.nan]*buffer_len)
else:
# self.ma_window = deque(maxlen=ma_window_len)
self.ma_window = RingBuffer([np.nan]*ma_window_len)
self.ma_recalc_delay = ma_recalc_delay
self.ddof = ddof
self.anomaly_mean = None
self.anomaly_std = None
self.t = 0
def time_step(self, xt):
xt = np.reshape(xt, newshape=self.dimensions)
ret_val = 0.
self.buffer.append(xt)
self.present.time_step(xt)
if self.t >= self.buffer_len:
pst_xt = self.buffer[0]
self.past.time_step(pst_xt)
if self.t >= self.present.theta + self.past.theta:
ret_val = self.comparison_function(self.present, self.past,
self.present.alpha)
self.ma_window.append(ret_val)
if self.t % self.ma_recalc_delay == 0:
self.anomaly_mean = bn.nanmean(self.ma_window)
self.anomaly_std = bn.nanstd(self.ma_window, ddof=self.ddof)
if self.anomaly_std is None or self.t < len(self.ma_window):
anomaly_density = 0
else:
normalized_score = (ret_val - self.anomaly_mean)/self.anomaly_std
if -4 <= normalized_score <= 4:
anomaly_density = CDF_TABLE[round(normalized_score, 3)]
elif normalized_score > 4:
anomaly_density = 1.
else:
anomaly_density = 0.
self.t += 1
return ret_val, anomaly_density