def find_phase_number_between_ball_and_wheel(
time_of_ball_in_front_of_mark, time_of_wheel_in_front_of_mark,
wheel_revolution_time, way):
diff_time = np.abs(time_of_ball_in_front_of_mark -
time_of_wheel_in_front_of_mark)
numbers_count = len(Wheel.NUMBERS)
idx_phase = int(
np.round((diff_time / wheel_revolution_time * numbers_count)))
idx_zero = Wheel.find_index_of_number(0) # Should be always 0
if time_of_ball_in_front_of_mark > time_of_wheel_in_front_of_mark:
# t(Wheel) < t(Ball) Wheel is ahead of phase
# The question is: what is the value of the wheel when the ball
# is in front of the mark?
if way == Wheel.WheelWay.CLOCKWISE:
idx = idx_zero - idx_phase
elif way == Wheel.WheelWay.ANTICLOCKWISE:
idx = idx_zero + idx_phase
else:
raise Exception('Unknown type.')
else:
# t(Wheel) > t(Ball) Ball is ahead of phase
# The question is what is the value of the ball when the wheel
# is in front of the wheel?
if way == Wheel.WheelWay.CLOCKWISE:
idx = idx_zero + idx_phase
elif way == Wheel.WheelWay.ANTICLOCKWISE:
idx = idx_zero - idx_phase
else:
raise Exception('Unknown type.')
return Wheel.NUMBERS[Wheel.get_index(idx)]
def next_batch(debug=True):
circ = 0.85 * np.pi
cutoff_speed = 1.0
rev_time = 3 # units of time.
init_max_time_value_first_ball_time = 1.0
# BALL PART
abs_ball_times, abs_cutoff_time = generate_abs_times(
circ, cutoff_speed, init_max_time_value_first_ball_time)
Wheel.find_index_of_number(0)
abs_ball_times -= abs_ball_times[0]
# WHEEL PART
initial_number = np.random.choice(Wheel.NUMBERS) # at time = 0
abs_wheel_times = generate_wheel_abs_times(rev_time,
initial_number,
max_len=len(abs_ball_times))
numbers = []
for abs_ball_time in np.append(abs_ball_times, abs_cutoff_time):
abs_wheel_time = Helper.get_last_time_wheel_is_in_front_of_ref(
abs_wheel_times, abs_ball_time)
if abs_wheel_time is None:
numbers.append(None)
else:
num = get_real_number(abs_ball_time, abs_wheel_time, rev_time)
assert num == get_number(abs_ball_time - abs_wheel_time, 0,
rev_time)
numbers.append(num)
number_landmark_number_cutoff = numbers[-1]
ball_distance_travelled = distance_travelled(abs_ball_times[-1],
abs_cutoff_time, neg_exp)
ball_cutoff_shift = (ball_distance_travelled % circ) / circ * len(
Wheel.NUMBERS)
number_cutoff = Wheel.get_number_with_shift(number_landmark_number_cutoff,
ball_cutoff_shift,
Wheel.WheelWay.CLOCKWISE)
numbers = np.array(numbers)[:-1]
# ball_lap_times = np.diff(abs_ball_times)
# wheel_lap_times = np.diff(abs_wheel_times)
# cutoff_lap_time = abs_cutoff_time - abs_ball_times[-1]
# number_cutoff = get_number(abs_cutoff_time, initial_number, rev_time)
if debug:
print('-' * 80)
print('ABSOLUTE BALL TIMES =', abs_ball_times)
print('ABSOLUTE WHEEL TIMES =', abs_wheel_times)
print('ABS CUTOFF TIME =', abs_cutoff_time)
print('NUMBERS =', numbers)
print('NUMBER CUTOFF =', number_cutoff)
# plot_a(ball_diff_times)
# sleep(0.1)
return {
'abs_ball_times': abs_ball_times,
'abs_wheel_times': abs_wheel_times,
'abs_cutoff_time': abs_cutoff_time,
'numbers': np.array(numbers),
'number_cutoff': number_cutoff
}
def next_batch(debug=True):
circ = 0.85 * np.pi
rev_time = 3 # units of time.
init_max_time_value_first_ball_time = 2.0 # 1 second max after t = 0.
# BALL PART
abs_ball_times, abs_cutoff_time = generate_abs_times(
circ, init_max_time_value_first_ball_time)
Wheel.find_index_of_number(0)
abs_ball_times -= abs_ball_times[0]
# WHEEL PART
initial_number = np.random.choice(Wheel.NUMBERS) # at time = 0
abs_wheel_times = generate_wheel_abs_times(rev_time,
initial_number,
max_len=len(abs_ball_times))
numbers = []
for abs_ball_time in np.append(abs_ball_times, abs_cutoff_time):
abs_wheel_time = Helper.get_last_time_wheel_is_in_front_of_ref(
abs_wheel_times, abs_ball_time)
if abs_wheel_time is None:
numbers.append(None)
else:
numbers.append(
get_real_number(abs_ball_time, abs_wheel_time, rev_time))
number_cutoff = numbers[-1]
numbers = np.array(numbers)
# ball_lap_times = np.diff(abs_ball_times)
# wheel_lap_times = np.diff(abs_wheel_times)
# cutoff_lap_time = abs_cutoff_time - abs_ball_times[-1]
# number_cutoff = get_number(abs_cutoff_time, initial_number, rev_time)
if debug:
print('-' * 80)
print('ABSOLUTE BALL TIMES =', abs_ball_times)
# print('BALL LAP TIMES =', ball_lap_times)
print('ABSOLUTE WHEEL TIMES =', abs_wheel_times)
# print('BALL WHEEL TIMES =', wheel_lap_times)
# print('CUTOFF DIFF TIME =', cutoff_lap_time)
print('ABS CUTOFF TIME =', abs_cutoff_time)
print('NUMBERS =', numbers)
print('NUMBER CUTOFF =', number_cutoff)
# plot_a(ball_diff_times)
# sleep(0.1)
return {
'abs_ball_times': abs_ball_times,
# 'ball_lap_times': ball_lap_times,
'abs_wheel_times': abs_wheel_times,
# 'wheel_lap_times': wheel_lap_times,
# 'cutoff_lap_time': cutoff_lap_time,
'abs_cutoff_time': abs_cutoff_time,
'numbers': np.array(numbers),
'number_cutoff': number_cutoff
}
def generate_wheel_abs_times(rev_time, initial_number, max_len):
# initial number already reflects the position of the wheel at the t=0 for the ball.
cur_time = 0
abs_times = []
shift = -1
for ii in range(len(Wheel.NUMBERS)):
number = Wheel.get_number_with_shift(initial_number, ii, Wheel.WheelWay.CLOCKWISE)
if number == 0:
shift = ii
break
assert shift != -1
cur_time += (shift / len(Wheel.NUMBERS)) * rev_time
abs_times.append(cur_time)
for jj in range(max_len):
cur_time += rev_time
abs_times.append(cur_time)
return np.array(abs_times)
def get_number(t, initial_number, rev_time):
res_t = t % rev_time
shift_to_add = res_t * len(Wheel.NUMBERS)
return Wheel.get_number_with_shift(initial_number, shift_to_add,
Wheel.WheelWay.CLOCKWISE)
def predict(ball_recorded_times, wheel_recorded_times, debug=True):
# Last measurement is when the ball hits the diamond ring.
ts_list = np.array(PredictorPhysics.LAP_TIMES_ALL_GAMES_LIST.copy())
dr_list = np.array(PredictorPhysics.DIAMOND_RING_ALL_GAMES_LIST.copy())
if ts_list is None or dr_list is None:
raise CriticalException(
'Cache is not initialized. Call load_cache().')
ts_list = TimeSeriesMerger.merge(ts_list)
ts_mean = np.nanmean(ts_list, axis=0)
last_time_ball_passes_in_front_of_ref = ball_recorded_times[-1]
last_wheel_lap_time_in_front_of_ref = Helper.get_last_time_wheel_is_in_front_of_ref(
wheel_recorded_times, last_time_ball_passes_in_front_of_ref)
log(
'ref time of the prediction = {0:.2f}s'.format(
last_time_ball_passes_in_front_of_ref), debug)
ball_lap_times = np.diff(ball_recorded_times)
wheel_lap_times = np.diff(wheel_recorded_times)
ball_loop_count = len(ball_lap_times)
index_of_rev_start = Helper.find_abs_start_index(
ball_lap_times, ts_mean)
log('index_of_rev_start = {}'.format(index_of_rev_start), debug)
index_of_last_recorded_time = ball_loop_count + index_of_rev_start
matched_game_indices = TimeSeriesMerger.find_nearest_neighbors(
ball_lap_times,
ts_list,
index_of_rev_start,
neighbors_count=Constants.NEAREST_NEIGHBORS_COUNT)
log('matched_game_indices = {}'.format(matched_game_indices), debug)
estimated_time_left = np.mean(
np.sum(ts_list[matched_game_indices, index_of_last_recorded_time:],
axis=1))
estimated_time_left += np.mean(dr_list[matched_game_indices], axis=0)
log('estimated_time_left = {0:.2f}s'.format(estimated_time_left),
debug)
if estimated_time_left <= 0:
raise PositiveValueExpectedException(
'estimated_time_left must be positive.')
# biased estimator but still it should work here.
max_residual_time = np.max(dr_list)
# if we have [0, 0, 1, 2, 3, 0, 0], index_of_rev_start = 2
# rem_loops is actually useless to compute :)
rem_loops = ts_list[matched_game_indices,
index_of_last_recorded_time:].shape[1]
rem_res_loop = np.mean(
dr_list[matched_game_indices] /
max_residual_time) # we should calibrate it more.
number_of_revolutions_left_ball = rem_loops + rem_res_loop
# TODO: for later.
# we need to have a better approximation of:
# We can maybe fit an ARIMA on the Abs Ball times
# or get rem_res_loop accurate.
if number_of_revolutions_left_ball <= 0:
error_msg = 'rem_loops = {0:.2f}, rem_res_loop = {0:.2f}.'.format(
rem_loops, rem_res_loop)
raise PositiveValueExpectedException(
'number_of_revolutions_left_ball must be positive.' +
error_msg)
log(
'number_of_revolutions_left_ball = {0:.2f}'.format(
number_of_revolutions_left_ball), debug)
# the time values are always taken at the same diamond.
diamond = Diamonds.detect_diamonds(number_of_revolutions_left_ball)
log('diamond to be hit = {}'.format(diamond), debug)
if diamond == Diamonds.DiamondType.BLOCKER:
expected_bouncing_shift = Constants.EXPECTED_BOUNCING_SHIFT_BLOCKER_DIAMOND
else:
expected_bouncing_shift = Constants.EXPECTED_BOUNCING_SHIFT_FORWARD_DIAMOND
shift_ball_cutoff = (number_of_revolutions_left_ball % 1) * len(
Wheel.NUMBERS)
time_at_cutoff_ball = last_time_ball_passes_in_front_of_ref + estimated_time_left
if time_at_cutoff_ball < last_time_ball_passes_in_front_of_ref + Constants.SECONDS_NEEDED_TO_PLACE_BETS:
raise PositiveValueExpectedException()
wheel_last_revolution_time = wheel_lap_times[-1]
# We want to find the number of the wheel where the ball passes in front of the mark.
initial_number = Phase.find_phase_number_between_ball_and_wheel(
last_time_ball_passes_in_front_of_ref,
last_wheel_lap_time_in_front_of_ref, wheel_last_revolution_time,
Constants.DEFAULT_WHEEL_WAY)
shift_between_initial_time_and_cutoff = (
(estimated_time_left / wheel_last_revolution_time) % 1) * len(
Wheel.NUMBERS)
# Explanation:
# shift_between_initial_time_and_cutoff - let's not focus on the ball. What is the configuration of the wheel
# at the cutoff time, i.e. estimated_time_left seconds later.
# shift_ball_cutoff - the ball is also moving during this time. Let's not focus on the wheel. Where is the ball
# compared to the wheel at the cutoff time. We imagine that the wheel is fixed.
# We then add the two quantities (composition of the kinetics) to know about the real shift.
shift_to_add = shift_ball_cutoff + shift_between_initial_time_and_cutoff
predicted_number_cutoff = Wheel.get_number_with_shift(
initial_number, shift_to_add, Constants.DEFAULT_WHEEL_WAY)
log(
'shift_between_initial_time_and_cutoff = {0:.2f}'.format(
shift_between_initial_time_and_cutoff), debug)
log('shift_ball_cutoff = {0:.2f}'.format(shift_ball_cutoff), debug)
log('predicted_number_cutoff = {}'.format(predicted_number_cutoff),
debug)
log('expected_bouncing_shift = {}'.format(expected_bouncing_shift),
debug)
shift_to_add += expected_bouncing_shift
predicted_number = Wheel.get_number_with_shift(
initial_number, shift_to_add, Constants.DEFAULT_WHEEL_WAY)
log('predicted_number is = {}'.format(predicted_number), debug)
return predicted_number_cutoff, predicted_number
