def visualize_world(self, brain):
"""
Show what's going on in the world.
"""
if self.print_features:
projections, activities = brain.cortex.get_index_projections(to_screen=True)
wtools.print_pixel_array_features(
projections,
self.fov_span ** 2 * 2,
0,#self.num_actions,
self.fov_span, self.fov_span,
world_name=self.name)
# Periodically show the state history and inputs as perceived by BECCA
print ''.join(["world is ", str(self.timestep), " timesteps old"])
fig = plt.figure(11)
plt.clf()
plt.plot( self.column_history, 'k.')
plt.title(''.join(['Column history for ', self.name]))
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(12)
sensed_image = np.reshape(
0.5 * (self.sensors[:len(self.sensors)/2] -
self.sensors[len(self.sensors)/2:] + 1),
(self.fov_span, self.fov_span))
plt.gray()
plt.imshow(sensed_image, interpolation='nearest')
plt.title("Image sensed")
fig.show()
fig.canvas.draw()
def visualize(self, agent):
""" Show what is going on in BECCA and in the world """
self.row_history.append(self.row_position)
self.column_history.append(self.column_position)
if self.animate:
print ''.join([
str(self.row_position), ', ',
str(self.column_position), '-row and col position ',
str(self.reward), '-reward'
])
# Periodically display the history and inputs as perceived by BECCA
if (self.timestep % self.VISUALIZE_PERIOD) != 0:
return
print ' '.join(["world is", str(self.timestep), "timesteps old."])
fig = plt.figure(11)
plt.clf()
plt.plot(self.row_history, 'k.')
plt.title("Row history")
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(12)
plt.clf()
plt.plot(self.column_history, 'k.')
plt.title("Column history")
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(13)
sensed_image = np.reshape(
0.5 * (self.sensors[:len(self.sensors) / 2] -
self.sensors[len(self.sensors) / 2:] + 1),
(self.fov_span, self.fov_span))
plt.gray()
plt.imshow(sensed_image, interpolation='nearest')
plt.title("Image sensed")
fig.show()
fig.canvas.draw()
# Periodcally show the entire feature set
if self.print_feature_set:
(feature_set, feature_activities) = agent.get_index_projections()
wtools.print_pixel_array_features(feature_set,
self.num_sensors,
self.num_actions,
self.fov_span,
self.fov_span,
directory='log',
world_name=self.name)
def visualize(self, agent):
"""
Show what is going on in BECCA and in the world
"""
self.row_history.append(self.row_position)
self.column_history.append(self.column_position)
if self.animate:
print ''.join([str(self.row_position), ', ',
str(self.column_position),
'-row and col position ',
str(self.reward), '-reward'])
# Periodically display the history and inputs as perceived by BECCA
if (self.timestep % self.VISUALIZE_PERIOD) != 0:
return
print ' '.join(["world is", str(self.timestep), "timesteps old."])
fig = plt.figure(11)
plt.clf()
plt.plot( self.row_history, 'k.')
plt.title("Row history")
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(12)
plt.clf()
plt.plot( self.column_history, 'k.')
plt.title("Column history")
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(13)
sensed_image = np.reshape(0.5 * (
self.sensors[:len(self.sensors)/2] -
self.sensors[len(self.sensors)/2:] + 1),
(self.fov_span, self.fov_span))
plt.gray()
plt.imshow(sensed_image, interpolation='nearest')
plt.title("Image sensed")
fig.show()
fig.canvas.draw()
# Periodcally show the entire feature set
if self.print_feature_set:
(feature_set, feature_activities) = agent.get_index_projections()
wtools.print_pixel_array_features(feature_set, self.num_sensors,
0,
self.fov_span, self.fov_span,
directory='log',
world_name=self.name)
def visualize(self, agent):
if self.animate:
print ''.join([
'column_position: ',
str(self.column_position), ' self.reward: ',
str(self.reward), ' self.column_step: ',
str(self.column_step)
])
if not self.graphing:
return
# Periodically show the state history and inputs as perceived by BECCA
self.column_history.append(self.column_position)
if (self.timestep % self.VISUALIZE_PERIOD) == 0:
# Periodically show the agent's internal state and reward history
agent.visualize()
print ''.join(["world is ", str(self.timestep), " timesteps old"])
fig = plt.figure(11)
plt.clf()
plt.plot(self.column_history, 'k.')
plt.title(''.join(['Column history for ', self.name]))
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(12)
sensed_image = np.reshape(
0.5 * (self.sensors[:len(self.sensors) / 2] -
self.sensors[len(self.sensors) / 2:] + 1),
(self.fov_span, self.fov_span))
plt.gray()
plt.imshow(sensed_image, interpolation='nearest')
plt.title("Image sensed")
fig.show()
fig.canvas.draw()
# Periodically visualize the entire feature set
if self.print_feature_set:
(feature_set, feature_activities) = \
agent.get_index_projections()
wtools.print_pixel_array_features(feature_set,
self.num_sensors,
self.num_actions,
self.fov_span,
self.fov_span,
directory='log',
world_name=self.name)
def visualize(self, agent):
if self.animate:
print ''.join(['column_position: ', str(self.column_position),
' self.reward: ', str(self.reward),
' self.column_step: ', str(self.column_step)])
if not self.graphing:
return
# Periodically show the state history and inputs as perceived by BECCA
self.column_history.append(self.column_position)
if (self.timestep % self.VISUALIZE_PERIOD) == 0:
# Periodically show the agent's internal state and reward history
agent.visualize()
print ''.join(["world is ", str(self.timestep), " timesteps old"])
fig = plt.figure(11)
plt.clf()
plt.plot( self.column_history, 'k.')
plt.title(''.join(['Column history for ', self.name]))
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(12)
sensed_image = np.reshape(
0.5 * (self.sensors[:len(self.sensors)/2] -
self.sensors[len(self.sensors)/2:] + 1),
(self.fov_span, self.fov_span))
plt.gray()
plt.imshow(sensed_image, interpolation='nearest')
plt.title("Image sensed")
fig.show()
fig.canvas.draw()
# Periodically visualize the entire feature set
if self.print_feature_set:
(feature_set, feature_activities) = \
agent.get_index_projections()
wtools.print_pixel_array_features(feature_set, self.num_sensors,
self.num_actions,
self.fov_span, self.fov_span,
directory='log',
world_name=self.name)
def visualize_world(self, brain):
"""
Show what's going on in the world.
"""
if self.print_features:
projections, activities = brain.cortex.get_index_projections(
to_screen=True)
wtools.print_pixel_array_features(
projections,
self.fov_span**2 * 2,
0, #self.num_actions,
self.fov_span,
self.fov_span,
world_name=self.name)
# Periodically show the state history and inputs as perceived by BECCA
print ''.join(["world is ", str(self.timestep), " timesteps old"])
fig = plt.figure(11)
plt.clf()
plt.plot(self.column_history, 'k.')
plt.title(''.join(['Column history for ', self.name]))
plt.xlabel('time step')
plt.ylabel('position (pixels)')
fig.show()
fig.canvas.draw()
fig = plt.figure(12)
sensed_image = np.reshape(
0.5 * (self.sensors[:len(self.sensors) / 2] -
self.sensors[len(self.sensors) / 2:] + 1),
(self.fov_span, self.fov_span))
plt.gray()
plt.imshow(sensed_image, interpolation='nearest')
plt.title("Image sensed")
fig.show()
fig.canvas.draw()