def get_place_cell_response(self, x, y):
"""Compute the firing rate of a each place cell with Gaussian tuning curves, for the current position of the
agent
"""
if isinstance(self.env, PlusMaze):
x, y = self.env.lookup_geodesic_coordinate([x, y])
place_cell_responses = utils.gauss2d(
(x, y),
var=self.field_width**2,
centre=self.geodesic_field_centres) / self.max_response
return place_cell_responses
place_cell_responses = utils.gauss2d(
(x, y), var=self.field_width**2,
centre=self.field_centres) / self.max_response
return place_cell_responses
def update_place_cell_response(self):
"""Compute the firing rate of a each place cell with Gaussian tuning curves, for the current position of the
agent
"""
self.previous_place_cell_responses = self.place_cell_responses
if isinstance(self.env, PlusMaze):
x, y = self.env.lookup_geodesic_coordinate(
[self.env.curr_x, self.env.curr_y])
self.place_cell_responses = utils.gauss2d(
[x, y],
var=self.field_width**2,
centre=self.geodesic_field_centres) / self.max_response
else:
self.place_cell_responses = utils.gauss2d(
[self.env.curr_x, self.env.curr_y],
var=self.field_width**2,
centre=self.field_centres) / self.max_response
def gen_mask(self, fea_sz): """ Generates 2D guassian masked convas with shape same as fea_sz. This method should only called on the first frame. Args: img_sz: input image size. fea_sz: feaure size, to be identical to the Output of sel-CNN net. Returns: convas: fea_sz shape with 1 channel. The central region is an 2D gaussian. """ im_sz = self.first_img.shape x, y, w, h = self.first_gt convas = np.zeros(im_sz[:2]) # Generates 2D gaussian mask scale = min([w,h]) / 3 # To be consistence with the paper mask = gauss2d([h, w], sigma=scale) print(mask.max(), 'max of mask') # bottom right coordinate x2 = x + w - 1 y2 = y + h - 1 # Detects wether the location has out of the img or not clip = min(x, y, im_sz[0]-y2, im_sz[1]-x2) pad = 0 if clip <= 0: pad = abs(clip) + 1 convas = np.zeros((im_sz[0] + 2*pad, im_sz[1] + 2*pad)) x += pad y += pad x2 += pad y2 += pad # Overwrite central arear of convas with mask; convas[y-1:y2, x-1:x2] = mask if clip <= 0: # Remove pad convas = convas[pad:-pad, pad, -pad] if len(convas.shape) < 3: convas = skimage.color.gray2rgb(convas) assert len(convas.shape) == 3 # Extrac ROI and resize bicubicly convas, _, _ = self.extract_roi(convas, self.first_gt) print(convas.shape) convas = imresize(convas[...,0], fea_sz[:2], interp='bicubic') print(convas.max(), 'max convas') # Swap back, and normalize convas = convas / convas.max() #convas = np.transpose(convas) return convas#[..., np.newaxis]
def get_place_cell_response(self, x, y):
"""Compute the firing rate of a each place cell with Gaussian tuning curves, for the current position of the
agent
"""
place_cell_responses = utils.gauss2d(
(x, y), var=self.field_width**2,
centre=self.field_centres) / self.max_response
place_cell_responses /= np.linalg.norm(place_cell_responses)
return place_cell_responses
def compute_place_cell_response(self, position):
"""Compute the responses of all place cells.
:param position: Current position (x,y) of the agent.
:return:
"""
return utils.gauss2d(position,
var=self.field_width**2,
centre=self.field_centres) / self.max_response
def update_place_cell_response(self):
"""Compute the firing rate of a each place cell with Gaussian tuning curves, for the current position of the
agent
"""
self.previous_place_cell_responses = self.place_cell_responses
self.place_cell_responses = utils.gauss2d(
self.current_position,
var=self.field_width**2,
centre=self.field_centres) / self.max_response
def update_place_cell_response(self):
"""Compute the firing rate of a each place cell with Gaussian tuning curves, for the current position of the
agent
"""
self.previous_place_cell_responses = self.place_cell_responses
self.place_cell_responses = utils.gauss2d(
[self.env.curr_x, self.env.curr_y],
var=self.field_width**2,
centre=self.field_centres)
self.place_cell_responses /= np.linalg.norm(self.place_cell_responses)
def gaussian_response(x_grid, y_grid, stimulus_location):
"""Compute response of a grid of gaussian receptive fields to a stimulus location
:param x_grid:
:param y_grid:
:param stimulus_location:
:return:
"""
rf_centres = np.dstack([x_grid.ravel(), y_grid.ravel()])[0]
responses = utils.gauss2d(stimulus_location, .05, rf_centres.T)
return responses
def __init__(self, n_trials=30, env=WaterMazeEnv()):
WaterMazeAgent.__init__(self, n_trials=n_trials, env=env)
self.env.trial = 0
self.field_centres = self.create_place_cells()
self.field_width = .09
self.max_response = utils.gauss2d([0, 0], self.field_width**2, [0, 0])
self.place_cell_responses = np.zeros(self.field_centres[0].shape)
self.previous_place_cell_responses = np.zeros(
self.field_centres[0].shape)
self.update_place_cell_response()
self.actions = {
idx: direction
for (idx, direction) in zip(range(12), range(0, 360, 30))
}
self.actions[0] = 360
self.current_action = np.random.choice(len(self.actions))
self.previous_action = None
# initialise critic:
self.critic_weights = np.zeros(self.place_cell_responses.shape)
self.max_sum_weights = 1.
self.critic_activation = np.dot(self.critic_weights,
self.place_cell_responses)
self.previous_critic_activation = None
# initialise actor:
self.action_weights = np.zeros(
(len(self.actions), self.place_cell_responses.shape[0]))
self.action_values = np.dot(self.action_weights,
self.place_cell_responses)
self.policy = self.softmax(self.action_values)
self.policies = []
self.policies.append(self.evaluate_policy_at_field_centres())
def get_max_response(self):
"""Get the maximum place cell response.
"""
return utils.gauss2d([0, 0], self.field_width**2, [0, 0])
