def main():
# flags
make_some_observations_missing = False
use_graphics = False
need_to_generate_data = True
# parse command line arguments
for arg in sys.argv[1:]:
if arg == '--missing':
make_some_observations_missing = True
elif arg == '--nographics':
use_graphics = False
elif arg.startswith('--load='):
filename = arg[7:]
hidden_states, observations = robot.load_data(filename)
need_to_generate_data = False
num_time_steps = len(hidden_states)
# if no data is loaded, then generate new data
if need_to_generate_data:
num_time_steps = 100
hidden_states, observations = \
generate_data(num_time_steps,
make_some_observations_missing,
random_seed=0)
# print('Running forward-backward...')
# marginals = forward_backward(observations)
# print("\n")
#
# timestep = 2
# print("Most likely parts of marginal at time %d:" % (timestep))
# if marginals[timestep] is not None:
# print(sorted(marginals[timestep].items(),
# key=lambda x: x[1],
# reverse=True)[:10])
# else:
# print('*No marginal computed*')
# print("\n")
print('Running Viterbi...')
observations = [(2, 0), (2, 0), (3, 0), (4, 0), (4, 0), (6, 0), (6, 1),
(5, 0), (6, 0), (6, 2)]
#observations = [(1, 6), (4, 6), (4, 7), None, (5, 6), (6, 5), (6, 6), None, (5, 5), (4, 4)]
estimated_states = Viterbi(observations)
print(estimated_states)
print("\n")
# flags
make_some_observations_missing = False
use_graphics = True
need_to_generate_data = True
# parse command line arguments
for arg in sys.argv[1:]:
if arg == '--missing':
make_some_observations_missing = True
elif arg == '--nographics':
use_graphics = False
elif arg.startswith('--load='):
filename = arg[7:]
grid_width, grid_height, hidden_states, observations \
= robot.load_data(filename)
robot.GRID_WIDTH = grid_width
robot.GRID_HEIGHT = grid_height
graphics.GRID_WIDTH = grid_width
graphics.GRID_HEIGHT = grid_height
need_to_generate_data = False
num_time_steps = len(hidden_states)
# if no data is loaded, then generate new data
if need_to_generate_data:
num_time_steps = 100
hidden_states, observations = \
generate_data(robot.initial_distribution,
robot.transition_model,
robot.observation_model,
old = old_transition_model(state)
new = new_transition_model(state)
possible_next_states = set(old.keys()) | set(new.keys())
for next_state in possible_next_states:
sum_so_far += ( old[next_state] - new[next_state] )**2
return sum_so_far < 0.1
if __name__ == '__main__':
need_to_generate_data = True
use_graphics = True
for arg in sys.argv[1:]:
if arg.startswith('--load='):
filename = arg[7:]
hidden_states, observations = robot.load_data(filename)
need_to_generate_data = False
num_time_steps = len(hidden_states)
# if no data is loaded, then generate new data
if need_to_generate_data:
num_time_steps = 1000
hidden_states, observations = \
inference.generate_data(robot.initial_distribution,
robot.transition_model,
robot.observation_model,
num_time_steps,
make_some_observations_missing=False)
all_possible_hidden_states = robot.get_all_hidden_states()
all_possible_observed_states = robot.get_all_observed_states()
def main():
# flags
make_some_observations_missing = False
use_graphics = True
need_to_generate_data = True
# parse command line arguments
for arg in sys.argv[1:]:
if arg == '--missing':
make_some_observations_missing = True
elif arg == '--nographics':
use_graphics = False
elif arg.startswith('--load='):
filename = arg[7:]
hidden_states, observations = robot.load_data(filename)
need_to_generate_data = False
num_time_steps = len(hidden_states)
# if no data is loaded, then generate new data
if need_to_generate_data:
num_time_steps = 100
hidden_states, observations = \
generate_data(num_time_steps,
make_some_observations_missing)
print('Running forward-backward...')
marginals = forward_backward(observations)
print("\n")
timestep = 2
print("Most likely parts of marginal at time %d:" % (timestep))
if marginals[timestep] is not None:
print(
sorted(marginals[timestep].items(),
key=lambda x: x[1],
reverse=True)[:10])
else:
print('*No marginal computed*')
print("\n")
print('Running Viterbi...')
estimated_states = Viterbi(observations)
print("\n")
print("Last 10 hidden states in the MAP estimate:")
for time_step in range(num_time_steps - 10 - 1, num_time_steps):
if estimated_states[time_step] is None:
print('Missing')
else:
print(estimated_states[time_step])
print("\n")
print('Finding second-best MAP estimate...')
estimated_states2 = second_best(observations)
print("\n")
print("Last 10 hidden states in the second-best MAP estimate:")
for time_step in range(num_time_steps - 10 - 1, num_time_steps):
if estimated_states2[time_step] is None:
print('Missing')
else:
print(estimated_states2[time_step])
print("\n")
difference = 0
difference_time_steps = []
for time_step in range(num_time_steps):
if estimated_states[time_step] != hidden_states[time_step]:
difference += 1
difference_time_steps.append(time_step)
print(
"Number of differences between MAP estimate and true hidden " +
"states:", difference)
if difference > 0:
print("Differences are at the following time steps: " + ", ".join(
["%d" % time_step for time_step in difference_time_steps]))
print("\n")
difference = 0
difference_time_steps = []
for time_step in range(num_time_steps):
if estimated_states2[time_step] != hidden_states[time_step]:
difference += 1
difference_time_steps.append(time_step)
print(
"Number of differences between second-best MAP estimate and " +
"true hidden states:", difference)
if difference > 0:
print("Differences are at the following time steps: " + ", ".join(
["%d" % time_step for time_step in difference_time_steps]))
print("\n")
difference = 0
difference_time_steps = []
for time_step in range(num_time_steps):
if estimated_states[time_step] != estimated_states2[time_step]:
difference += 1
difference_time_steps.append(time_step)
print(
"Number of differences between MAP and second-best MAP " +
"estimates:", difference)
if difference > 0:
print("Differences are at the following time steps: " + ", ".join(
["%d" % time_step for time_step in difference_time_steps]))
print("\n")
# display
if use_graphics:
app = graphics.playback_positions(hidden_states, observations,
estimated_states, marginals)
app.mainloop()
if __name__ == '__main__':
# flags
make_some_observations_missing = False
use_graphics = True
need_to_generate_data = True
# parse command line arguments
for arg in sys.argv[1:]:
if arg == '--missing':
make_some_observations_missing = True
elif arg == '--nographics':
use_graphics = False
elif arg.startswith('--load='):
filename = arg[7:]
grid_width, grid_height, hidden_states, observations \
= robot.load_data(filename)
robot.GRID_WIDTH = grid_width
robot.GRID_HEIGHT = grid_height
need_to_generate_data = False
num_time_steps = len(hidden_states)
else:
raise ValueError("I don't know how to handle argument %s" % arg)
# if no data is loaded, then generate new data
if need_to_generate_data:
num_time_steps = 100
hidden_states, observations = \
generate_data(robot.initial_distribution,
robot.transition_model,
robot.observation_model,
use_graphics = True
need_to_generate_data = True
use_spread_output = False
# parameters
# parse command line arguments
for arg in sys.argv[1:]:
if arg == "--missing":
make_some_observations_missing = True
elif arg == "--nographics":
use_graphics = False
elif arg.startswith("--load="):
filename = arg[7:]
grid_width, grid_height, hidden_states, observations = robot.load_data(filename)
robot.GRID_WIDTH = grid_width
robot.GRID_HEIGHT = grid_height
graphics.GRID_WIDTH = grid_width
graphics.GRID_HEIGHT = grid_height
need_to_generate_data = False
num_time_steps = len(hidden_states)
elif arg == "--use-spread-output":
use_spread_output = True
else:
raise ValueError("I don't know how to handle argument %s" % arg)
# if no data is loaded, then generate new data
if need_to_generate_data:
num_time_steps = 10
