def get_projected_heatmap_vectors(xs, ys, x_axis_sp, y_axis_sp, z_axis_sp):
if x_axis_sp.__class__.__name__ == 'SemanticPointer':
dim = len(x_axis_sp.v)
else:
dim = len(x_axis_sp)
x_axis_sp = spa.SemanticPointer(data=x_axis_sp)
y_axis_sp = spa.SemanticPointer(data=y_axis_sp)
z_axis_sp = spa.SemanticPointer(data=z_axis_sp)
vectors = np.zeros((len(xs), len(ys), dim))
for i, x in enumerate(xs):
for j, y in enumerate(ys):
# xyz = xy_to_xyz([x, y])
xyz = xy_to_xyz_v(np.array([[x, y]]))[0, :]
p = encode_point_3d(
x=xyz[0],
y=xyz[1],
z=xyz[2],
x_axis_sp=x_axis_sp,
y_axis_sp=y_axis_sp,
z_axis_sp=z_axis_sp,
)
vectors[i, j, :] = p.v
return vectors
def get_heatmap_vectors(xs, ys, x_axis_sp, y_axis_sp):
"""
Precompute spatial semantic pointers for every location in the linspace
Used to quickly compute heat maps by a simple vectorized dot product (matrix multiplication)
"""
if x_axis_sp.__class__.__name__ == 'SemanticPointer':
dim = len(x_axis_sp.v)
else:
dim = len(x_axis_sp)
x_axis_sp = spa.SemanticPointer(data=x_axis_sp)
y_axis_sp = spa.SemanticPointer(data=y_axis_sp)
vectors = np.zeros((len(xs), len(ys), dim))
for i, x in enumerate(xs):
for j, y in enumerate(ys):
p = encode_point(
x=x,
y=y,
x_axis_sp=x_axis_sp,
y_axis_sp=y_axis_sp,
)
vectors[i, j, :] = p.v
return vectors
def generate_coord_dataset(n_samples,
dim,
x_axis_sp=None,
y_axis_sp=None,
z_axis_sp=None,
hexagonal_coordinates=False,
limits=(-1, 1, -1, 1),
seed=13):
"""
Create a dataset of semantic pointer coordinates and their corresponding real coordinates
:param n_samples: number of coordinates to create
:param dim: dimensionality of the semantic pointers
:param x_axis_sp: optional x_axis semantic pointer. If not supplied, will be generated as a unitary vector
:param y_axis_sp: optional y_axis semantic pointer. If not supplied, will be generated as a unitary vector
:param z_axis_sp: optional z_axis semantic pointer. If not supplied, will be generated as a unitary vector
:param hexagonal_coordinates: if three axes of a hexagonal system are to be used
:param limits: limits of the 2D space (x_low, x_high, y_low, y_high)
:param seed: random seed for the memories and axis vectors if not supplied
:return: vectors, coords, x_axis_sp, y_axis_sp, z_axis_sp
"""
# This seed must match the one that was used to generate the model
np.random.seed(seed)
if x_axis_sp is None:
x_axis_sp = spa.SemanticPointer(dim)
x_axis_sp.make_unitary()
if y_axis_sp is None:
y_axis_sp = spa.SemanticPointer(dim)
y_axis_sp.make_unitary()
if z_axis_sp is None:
z_axis_sp = spa.SemanticPointer(dim)
z_axis_sp.make_unitary()
# Semantic pointer vectors
vectors = np.zeros((n_samples, dim))
# Actual coordinates
coords = np.zeros((n_samples, 2))
for i in range(n_samples):
x = np.random.uniform(low=limits[0], high=limits[1])
y = np.random.uniform(low=limits[2], high=limits[3])
if hexagonal_coordinates:
vectors[i, :] = encode_hex_point(x,
y,
x_axis_sp=x_axis_sp,
y_axis_sp=y_axis_sp,
z_axis_sp=z_axis_sp).v
else:
vectors[i, :] = encode_point(x,
y,
x_axis_sp=x_axis_sp,
y_axis_sp=y_axis_sp).v
coords[i, 0] = x
coords[i, 1] = y
return vectors, coords, x_axis_sp, y_axis_sp, z_axis_sp
def make_multigoal_ssp_env(map_array,
csp_scaling,
csp_offset,
object_locations,
x_axis_vec,
y_axis_vec,
dim=512,
continuous=True,
movement_type='holonomic'):
params = {
'x_axis_vec': spa.SemanticPointer(data=x_axis_vec),
'y_axis_vec': spa.SemanticPointer(data=y_axis_vec),
'goal_csp': True,
'agent_csp': True,
'csp_dim': dim,
'goal_csp_egocentric': False,
# other arguments for bio sensors
"full_map_obs": False,
"pob": 0,
"max_sensor_dist": 10,
"n_sensors": 10,
"fov": 180,
"normalize_dist_sensors": True,
"n_grid_cells": 0,
"heading": "none",
"location": "none",
"goal_loc": "none",
"bc_n_ring": 12,
"bc_n_rad": 3,
"bc_dist_rad": 0.75,
"bc_receptive_field_min": 1,
"bc_receptive_field_max": 1.5,
"hd_n_cells": 8,
"hd_receptive_field_min": 0.78539816339,
"hd_receptive_field_max": 0.78539816339,
"goal_vec": "normalized",
}
obs_dict = generate_obs_dict(params)
env = GridWorldEnv(
map_array=map_array,
object_locations=object_locations,
observations=obs_dict,
movement_type=movement_type,
max_lin_vel=5,
max_ang_vel=5,
continuous=continuous,
max_steps=1000,
fixed_episode_length=False, # True,
dt=0.1,
screen_width=300,
screen_height=300,
csp_scaling=csp_scaling,
csp_offset=csp_offset,
)
return env
def one_hot_axes(D=8, xi=0, yi=0):
xv = np.zeros((D, ))
xv[xi] = 1
yv = np.zeros((D, ))
yv[yi] = 1
# Note that making them unitary doesn't seem to be required for one-hot vectors
x_axis_sp = spa.SemanticPointer(data=xv)
x_axis_sp.make_unitary()
y_axis_sp = spa.SemanticPointer(data=yv)
y_axis_sp.make_unitary()
return x_axis_sp, y_axis_sp
def generate_item_memory(dim,
n_items,
limits,
x_axis_sp,
y_axis_sp,
normalize_memory=True,
encoding='pow'):
"""
Create a semantic pointer that contains a number of items bound with respective coordinates
Returns the memory, along with a list of the items and coordinates used
The encoding parameter determines which method is used to store items at locations
"""
assert encoding in ['pow', 'mag', 'sep_pow']
# Start with an empty memory
memory_sp = spa.SemanticPointer(data=np.zeros((dim, )))
coord_list = []
item_list = []
for n in range(n_items):
# Generate random point
x = np.random.uniform(low=limits[0], high=limits[1])
y = np.random.uniform(low=limits[2], high=limits[3])
# Generate random item
item = spa.SemanticPointer(dim)
# Add the item to memory at the particular location
# This is done differently depending on the encoding method
if encoding == 'pow':
# Circular convolution power representation
pos = encode_point(x, y, x_axis_sp=x_axis_sp, y_axis_sp=y_axis_sp)
# Add the item at the point to memory
memory_sp += (pos * item)
elif encoding == 'mag':
# Magnitude scaling representation
memory_sp += (x * (item * x_axis_sp) + y * (item * y_axis_sp))
elif encoding == 'sep_pow':
# Power representation, but X and Y are independent
memory_sp += (item * power(x_axis_sp, x) +
item * power(y_axis_sp, y))
coord_list.append((x, y))
item_list.append(item)
if normalize_memory:
memory_sp.normalize()
return memory_sp, coord_list, item_list
def make_good_unitary(D, eps=1e-3, rng=np.random):
'''
return: a semantic pointer object with dimension D.
'''
a = rng.rand((D - 1) // 2)
sign = rng.choice((-1, +1), len(a))
phi = sign * np.pi * (eps + a * (1 - 2 * eps))
assert np.all(np.abs(phi) >= np.pi * eps)
assert np.all(np.abs(phi) <= np.pi * (1 - eps))
fv = np.zeros(D, dtype='complex64')
fv[0] = 1
fv[1:(D + 1) // 2] = np.cos(phi) + 1j * np.sin(phi)
fv[-1:D // 2:-1] = np.conj(fv[1:(D + 1) // 2])
if D % 2 == 0:
fv[D // 2] = 1
assert np.allclose(np.abs(fv), 1)
v = np.fft.ifft(fv)
# assert np.allclose(v.imag, 0, atol=1e-5)
v = v.real
assert np.allclose(np.fft.fft(v), fv)
assert np.allclose(np.linalg.norm(v), 1)
return spa.SemanticPointer(v)
def random_unitary(n_samples=1000, dim=3, version=1, eps=0.001):
points = np.zeros((n_samples, dim))
good = np.zeros((n_samples, ))
for i in range(n_samples):
if version == 1:
sp = nengo_spa.SemanticPointer(data=np.random.randn(dim))
sp = sp.normalized()
sp = sp.unitary()
elif version == 0:
sp = spa.SemanticPointer(dim)
sp.make_unitary()
elif version == 2:
sp = make_good_unitary(dim=dim)
else:
raise NotImplementedError
points[i, :] = sp.v
pf = np.fft.fft(points[i, :])
if dim % 2 == 0:
if np.abs(pf[0] - 1) < eps and np.abs(pf[dim // 2] - 1) < eps:
good[i] = 1
else:
if np.abs(pf[0] - 1) < eps:
good[i] = 1
return points, good
def angle_spacing_axes(ang_x, ang_y, off_x=0, off_y=0, dim=32):
# X_test = ((np.arange(dim) * ang_x) + off_x) % (2 * np.pi)
# Y_test = ((np.arange(dim) * ang_y) + off_y) % (2 * np.pi)
X_test = ((np.arange(dim) * ang_x) + off_x) % 360
Y_test = ((np.arange(dim) * ang_y) + off_y) % 360
Xc = np.cos(X_test * np.pi / 180) + 1j * np.sin(X_test * np.pi / 180)
Yc = np.cos(Y_test * np.pi / 180) + 1j * np.sin(Y_test * np.pi / 180)
X = np.fft.ifft(Xc)
Y = np.fft.ifft(Yc)
X = spa.SemanticPointer(data=X)
Y = spa.SemanticPointer(data=Y)
X.make_unitary()
Y.make_unitary()
return X, Y
def power(s, e):
'''
s: a semantic pointer object that represent the axis
e: a value that indicates the coordinate
return: a encoded semantic pointer object
'''
x = np.fft.ifft(np.fft.fft(s.v) ** e).real
return spa.SemanticPointer(data=x)
def unitary_points(dim, n_samples, good_unitary=False):
points = np.zeros((n_samples, dim))
for i in range(n_samples):
if good_unitary:
points[i, :] = make_good_unitary(dim=dim).v
else:
sp = spa.SemanticPointer(dim)
sp.make_unitary()
points[i, :] = sp.v
return points
def unitary_determinant_test(dim=2, n_samples=1000):
for i in range(n_samples):
if True:
vec = make_good_unitary(dim=dim).v
print(vec)
else:
sp = spa.SemanticPointer(dim)
sp.make_unitary()
vec = sp.v
if not np.allclose(np.dot(circulant(vec)[0], circulant(vec)[1]), 0):
print(np.dot(circulant(vec)[0], circulant(vec)[1]))
print(np.linalg.det(circulant(vec)))
# assert np.allclose(np.dot(circulant(vec)[0], circulant(vec)[1]), 0)
assert np.allclose(np.abs(np.linalg.det(circulant(vec))), 1)
return True
def random_unitary(n_samples=1000, dim=3, version=2):
points = np.zeros((n_samples, dim))
for i in range(n_samples):
if version == 1:
sp = nengo_spa.SemanticPointer(data=np.random.randn(dim))
sp = sp.normalized()
sp = sp.unitary()
elif version == 0:
sp = spa.SemanticPointer(dim)
sp.make_unitary()
elif version == 2:
sp = make_good_unitary(dim=dim)
else:
raise NotImplementedError
points[i, :] = sp.v
return points
def generate_region_vector(desired, xs, ys, x_axis_sp, y_axis_sp):
"""
:param desired: occupancy grid of what points should be in the region and which ones should not be
:param xs: linspace in x
:param ys: linspace in y
:param x_axis_sp: x axis semantic pointer
:param y_axis_sp: y axis semantic pointer
:return: a normalized semantic pointer designed to be highly similar to the desired region
"""
vector = np.zeros_like((x_axis_sp.v))
for i, x in enumerate(xs):
for j, y in enumerate(ys):
if desired[i, j] == 1:
vector += encode_point(x, y, x_axis_sp, y_axis_sp).v
sp = spa.SemanticPointer(data=vector)
sp.normalize()
return sp
def encode_dataset(encoded_feature, aggregate_between_feature = 'sum'):
'''
encoded_feature: A list of encoded_features. Each entry is an numpy array with size n * dim.
aggregate_between_feature: 'sum' or 'multiply'
return: a n * d array where n is the number of datapoints and d is the dimension of the sps.
'''
m = len(encoded_feature)
n = encoded_feature[0].shape[0]
dim = encoded_feature[1].shape[1]
result = np.zeros((n, dim))
if aggregate_between_feature == 'sum':
for i in range(n):
for j in range(m):
result[i,:] += encoded_feature[j][i,:]
elif aggregate_between_feature == 'multiply':
for i in range(n):
temp = 1
for j in range(m):
temp *= spa.SemanticPointer(data=encoded_feature[j][i,:])
result[i,:] = temp.v
return result
def step(self):
self.diameter += self.diameter_increment
self.ellipse_sp = generate_elliptic_region_vector(
xs=self.xs,
ys=self.ys,
x_axis_sp=self.x_axis_sp,
y_axis_sp=self.y_axis_sp,
f1=self.current_loc,
f2=self.goal_loc,
diameter=self.diameter,
normalize=self.normalize,
)
potential_landmark = self.allo_connections_sp * ~self.ellipse_sp
# NOTE: this is only correct if allo_connections has landmark_id_sp information in it
sim = np.tensordot(potential_landmark.v,
self.landmark_vectors,
axes=([0], [1]))
# argsort sorts from lowest to highest, so create a view that reverses it
inds = np.argsort(sim)[::-1]
for i in inds:
if sim[i] < self.threshold:
# The next closest match is below threshold, so all others will be too
return None
elif i not in self.expanded_list:
print(
"{} detected as closest connection with diameter {} and {} similarity"
.format(i, self.diameter, sim[i]))
# Above threshold and has not been expanded yet, add it to the list now
self.expanded_list.append(i)
# Return the ID and the semantic pointer
return i, spa.SemanticPointer(data=self.landmark_vectors[i])
def encode_datapoint(datapoint, sps, binding = 'multiply', aggregate = 'sum'):
'''
datapoint: an 1 by m array. m is the number of possible choices of the property being encoded.
(ex. when encoding object kind, the possible choices of object kind are 'bolt' and 'nut').
sps: A list of size m. Each entry is the semantic pointer for each of the possible choices.
binding: The type to bind each entry. There are two ways:
1. 'multiply': c1*sp1
2. 'power': sp1^c1
aggregate: The type to bind between entries. There are two ways:
1. 'sum': c1*sp1 + c2 * sp2
2. 'multiply': sp1^c1 * sp2^c2
return: a SemanticPointer object that encode a datapoint(ex. c1*sp1 + c2*sp2)
'''
m = len(sps)
if aggregate == 'sum':
result = 0
for i in range(m):
result += encode_entry(datapoint[i], sps[i], binding = binding).v
return spa.SemanticPointer(data = result)
elif aggregate == 'multiply':
result = 1
for i in range(m):
result *= encode_entry(datapoint[i], sps[i], binding = binding)
return result
import nengo_spa
from spatial_semantic_pointers.utils import make_good_unitary
version = 2
n_samples = 25000 #10000
dim = 5 #3
points = np.zeros((n_samples, dim))
for i in range(n_samples):
if version == 1:
sp = nengo_spa.SemanticPointer(data=np.random.randn(dim))
sp = sp.normalized()
sp = sp.unitary()
elif version == 0:
sp = spa.SemanticPointer(dim)
sp.make_unitary()
elif version == 2:
sp = make_good_unitary(dim=dim)
points[i, :] = sp.v
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
def orthogonal_dir_unitary(dim=5, phi=np.pi / 2.):
xf = np.zeros((dim, ), dtype='Complex64')
xf[0] = 1
xf[1] = np.exp(1.j * phi)
xf[2] = 1
# # n_mazes by dim
# maze_sps = data['maze_sps']
# n_mazes by n_goals by 2
goals = data['goals']
# n_goals = goals.shape[1]
n_mazes = fine_mazes.shape[0]
id_size = args.maze_id_dim
maze_sps = np.zeros((n_mazes, args.maze_id_dim))
# overwrite data
for mi in range(n_mazes):
maze_sps[mi, :] = spa.SemanticPointer(args.maze_id_dim).v
# the unsqueeze is to add the batch dimension
map_id = torch.Tensor(maze_sps[args.maze_index, :]).unsqueeze(0)
n_sensors = 36
colour_centers = np.array([
[3, 3],
[10, 4],
[7, 7],
])
def colour_func(x, y, sigma=7):
ret = np.zeros((3, ))
def __init__(
self,
start_landmark_id,
end_landmark_id,
landmark_map_sp,
con_ego_sp,
con_allo_sp,
landmark_vectors,
x_axis_sp,
y_axis_sp,
xs,
ys,
heatmap_vectors,
# params for debugging
true_allo_con_sps,
connectivity_list,
con_calculation='true_allo',
normalize=True,
debug_mode=False,
# ellipse params
diameter_increment=1,
**unused_params):
self.debug_mode = debug_mode
# Various methods for calculating the connectivity of a particular node. Used for debugging
assert con_calculation in ['ego', 'allo', 'true_allo']
self.con_calculation = con_calculation
self.start_landmark_id = start_landmark_id
self.end_landmark_id = end_landmark_id
self.landmark_map_sp = landmark_map_sp
self.con_ego_sp = con_ego_sp
self.con_allo_sp = con_allo_sp
self.landmark_vectors = landmark_vectors
self.x_axis_sp = x_axis_sp
self.y_axis_sp = y_axis_sp
self.xs = xs
self.ys = ys
self.heatmap_vectors = heatmap_vectors
self.true_allo_con_sps = true_allo_con_sps
self.connectivity_list = connectivity_list
# Whether or not to normalize the ellipse region SP
self.normalize = normalize
self.diameter_increment = diameter_increment
start_landmark_sp = spa.SemanticPointer(
self.landmark_vectors[self.start_landmark_id])
end_landmark_sp = spa.SemanticPointer(
self.landmark_vectors[self.end_landmark_id])
current_loc_sp = self.landmark_map_sp * ~start_landmark_sp
self.goal_loc_sp = self.landmark_map_sp * ~end_landmark_sp
self.goal_loc = ssp_to_loc(self.goal_loc_sp,
heatmap_vectors=self.heatmap_vectors,
xs=self.xs,
ys=self.ys)
# egocentric displacements to nearby landmarks
ego_connections_sp = con_ego_sp * ~start_landmark_sp
# allocentric coordinates of nearby landmarks
if self.con_calculation == 'ego':
# calculating from ego
allo_connections_sp = current_loc_sp * ego_connections_sp
elif self.con_calculation == 'allo':
# getting true value from allo
allo_connections_sp = self.con_allo_sp * ~start_landmark_sp
elif self.con_calculation == 'true_allo':
# get a clean value from allo
allo_connections_sp = self.true_allo_con_sps[
self.start_landmark_id]
else:
raise NotImplementedError
# dictionary of nodes currently being expanded
self.expanding_nodes = {
self.start_landmark_id:
ExpandingNode(
current_loc_sp=current_loc_sp,
goal_loc_sp=self.goal_loc_sp,
closest_landmark_id=self.start_landmark_id,
allo_connections_sp=allo_connections_sp,
landmark_map_sp=self.landmark_map_sp,
landmark_vectors=self.landmark_vectors,
x_axis_sp=self.x_axis_sp,
y_axis_sp=self.y_axis_sp,
xs=self.xs,
ys=self.ys,
heatmap_vectors=self.heatmap_vectors,
normalize=self.normalize,
)
}
def orthogonal_hex_dir_7dim(phi=np.pi / 2., angle=0):
dim = 7
xf = np.zeros((dim, ), dtype='Complex64')
xf[0] = 1
xf[1] = np.exp(1.j * phi)
xf[2] = 1
xf[3] = 1
xf[4] = np.conj(xf[3])
xf[5] = np.conj(xf[2])
xf[6] = np.conj(xf[1])
yf = np.zeros((dim, ), dtype='Complex64')
yf[0] = 1
yf[1] = 1
yf[2] = np.exp(1.j * phi)
yf[3] = 1
yf[4] = np.conj(yf[3])
yf[5] = np.conj(yf[2])
yf[6] = np.conj(yf[1])
zf = np.zeros((dim, ), dtype='Complex64')
zf[0] = 1
zf[1] = 1
zf[2] = 1
zf[3] = np.exp(1.j * phi)
zf[4] = np.conj(zf[3])
zf[5] = np.conj(zf[2])
zf[6] = np.conj(zf[1])
Xh = np.fft.ifft(xf).real
Yh = np.fft.ifft(yf).real
Zh = np.fft.ifft(zf).real
# checks to make sure everything worked correctly
assert np.allclose(np.abs(xf), 1)
assert np.allclose(np.abs(yf), 1)
assert np.allclose(np.fft.fft(Xh), xf)
assert np.allclose(np.fft.fft(Yh), yf)
assert np.allclose(np.linalg.norm(Xh), 1)
assert np.allclose(np.linalg.norm(Yh), 1)
axis_sps = [
spa.SemanticPointer(data=Xh),
spa.SemanticPointer(data=Yh),
spa.SemanticPointer(data=Zh),
]
n = 3
points_nd = np.eye(n) * np.sqrt(n)
# points in 2D that will correspond to each axis, plus one at zero
points_2d = np.zeros((n, 2))
thetas = np.linspace(0, 2 * np.pi, n + 1)[:-1] + angle
# TODO: will want a scaling here, or along the high dim axes
for i, theta in enumerate(thetas):
points_2d[i, 0] = np.cos(theta)
points_2d[i, 1] = np.sin(theta)
transform_mat = np.linalg.lstsq(points_2d, points_nd)
x_axis = transform_mat[0][0, :] / transform_mat[3][0]
y_axis = transform_mat[0][1, :] / transform_mat[3][1]
X = power(axis_sps[0], x_axis[0])
Y = power(axis_sps[0], y_axis[0])
for i in range(1, n):
X *= power(axis_sps[i], x_axis[i])
Y *= power(axis_sps[i], y_axis[i])
sv = transform_mat[3][0]
return X, Y, sv, transform_mat[0]
sim_arcs = np.zeros((n_seeds, n_animals))
if not os.path.exists(folder):
# Data is not saved already, generate it now and save it after
for seed in range(n_seeds):
rstate = np.random.RandomState(seed=seed)
x_axis_sp = make_good_unitary(dim, rng=rstate)
y_axis_sp = make_good_unitary(dim, rng=rstate)
heatmap_vectors = get_heatmap_vectors(xs, ys, x_axis_sp, y_axis_sp)
vocab_sps = {}
for i, animal in enumerate(vocab_labels):
vocab_sps[animal] = spa.SemanticPointer(dim)
vocab_vectors[i, :] = vocab_sps[animal].v
mem = spa.SemanticPointer(data=np.zeros(dim))
fox_pos1 = encode_point(1.2, 1.3, x_axis_sp, y_axis_sp)
fox_pos2 = encode_point(-3.4, -1.1, x_axis_sp, y_axis_sp)
dog_pos = encode_point(1.7, -1.1, x_axis_sp, y_axis_sp)
badger_pos = encode_point(4.1, 3.2, x_axis_sp, y_axis_sp)
bear_pos = encode_point(2.1, 2.4, x_axis_sp, y_axis_sp)
none_pos = encode_point(0, 0, x_axis_sp, y_axis_sp)
mem += vocab_sps['Fox'] * fox_pos1
mem += vocab_sps['Fox'] * fox_pos2
mem += vocab_sps['Dog'] * dog_pos
mem += vocab_sps['Badger'] * badger_pos
xs = np.linspace(0, 10, res)
ys = np.linspace(0, 10, res)
# These will include the space that is the difference between any two nodes
xs_larger = np.linspace(-10, 10, res)
ys_larger = np.linspace(-10, 10, res)
x_axis_sp = make_good_unitary(dim)
y_axis_sp = make_good_unitary(dim)
heatmap_vectors = get_heatmap_vectors(xs, ys, x_axis_sp, y_axis_sp)
heatmap_vectors_larger = get_heatmap_vectors(xs_larger, ys_larger, x_axis_sp,
y_axis_sp)
# Map
map_sp = spa.SemanticPointer(data=np.zeros((dim, )))
# version of the map with landmark IDs bound to each location
landmark_map_sp = spa.SemanticPointer(data=np.zeros((dim, )))
# Connectivity
# contains each connection egocentrically
con_ego_sp = spa.SemanticPointer(data=np.zeros((dim, )))
# contains each connection allocentrically
con_allo_sp = spa.SemanticPointer(data=np.zeros((dim, )))
# Agent Location
agent_sp = spa.SemanticPointer(data=np.zeros((dim, )))
# True values for individual node connections, for debugging
true_allo_con_sps = list()
# args.dim = 256
args.dim = 512
limit_low = 0
limit_high = 13
seed = 13
limit = 10 #5
res = 256
neurons_per_dim = 10 #50
enc_func, repr_dim = get_encoding_function(args,
limit_low=limit_low,
limit_high=limit_high)
X = spa.SemanticPointer(data=enc_func(1, 0))
Y = spa.SemanticPointer(data=enc_func(0, 1))
# get exact params of the encoding used by using the same seed
eps = 0.001
n_toroids = ((args.dim - 1) // 2) // args.n_proj
rng_params = np.random.RandomState(seed=args.seed)
# Randomly select the angle for each toroid
phis = rng_params.uniform(0, 2 * np.pi, size=(n_toroids, ))
angles = rng_params.uniform(-np.pi + eps, np.pi - eps, size=n_toroids)
rng = np.random.RandomState(seed=seed)
# phis = (np.pi*.75, np.pi / 2., np.pi/3., np.pi/5., np.pi*.4, np.pi*.6)
# # angles = rng.uniform(0, 2*np.pi, size=len(phis))#(0, np.pi/3., np.pi/5.)
# angles = (0, np.pi*.3, np.pi*.2, np.pi*.4, np.pi*.1, np.pi*.5)
name="Single Object",
vmin=vmin,
vmax=vmax,
cmap=cmap,
)
fig.savefig('figures/single_item.pdf', dpi=600, bbox_inches='tight')
#####################
# Two Items Decoded #
#####################
if "Two Items Decoded" in plot_types:
fig, ax = plt.subplots(tight_layout=True, figsize=(4, 4))
pos1 = encode_point(3, -2, x_axis_sp, y_axis_sp)
pos2 = encode_point(-.3, 1.5, x_axis_sp, y_axis_sp)
item1 = spa.SemanticPointer(dim)
item2 = spa.SemanticPointer(dim)
mem = pos1 * item1 + pos2 * item2
decode1 = mem * ~item1
decode2 = mem * ~item2
heatmap(
(decode1 + decode2).v,
heatmap_vectors,
ax,
name='',
vmin=vmin,
vmax=vmax,
cmap=cmap,
def __init__(self, data, maze_sps, maze_indices, goal_indices, subsample=2, spatial_encoding='ssp'):
x_axis_sp = spa.SemanticPointer(data=data['x_axis_sp'])
y_axis_sp = spa.SemanticPointer(data=data['y_axis_sp'])
# n_mazes by res by res
fine_mazes = data['fine_mazes']
# n_mazes by n_goals by res by res by 2
solved_mazes = data['solved_mazes']
# NOTE: this can be modified from the original dataset, so it is explicitly passed in
# n_mazes by dim
# maze_sps = data['maze_sps']
# n_mazes by n_goals by 2
goals = data['goals']
n_mazes = data['goal_sps'].shape[0]
n_goals = data['goal_sps'].shape[1]
dim = data['goal_sps'].shape[2]
# NOTE: this code is assuming xs as ys are the same
assert(np.all(data['xs'] == data['ys']))
limit_low = data['xs'][0]
limit_high = data['xs'][1]
# NOTE: only used for one-hot encoded location representation case
xso = np.linspace(limit_low, limit_high, int(np.sqrt(dim)))
yso = np.linspace(limit_low, limit_high, int(np.sqrt(dim)))
# n_mazes by n_goals by dim
if spatial_encoding == 'ssp':
goal_sps = data['goal_sps']
elif spatial_encoding == 'random':
goal_sps = np.zeros_like(data['goal_sps'])
for ni in range(goal_sps.shape[0]):
for gi in range(goal_sps.shape[1]):
goal_sps[ni, gi, :] = encode_random(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=dim)
elif spatial_encoding == '2d' or spatial_encoding == 'learned':
goal_sps = goals.copy()
elif spatial_encoding == '2d-normalized':
goal_sps = goals.copy()
goal_sps = ((goal_sps - xso[0]) * 2 / (xso[-1] - xso[0])) - 1
elif spatial_encoding == 'one-hot':
goal_sps = np.zeros((n_mazes, n_goals, len(xso) * len(yso)))
for ni in range(goal_sps.shape[0]):
for gi in range(goal_sps.shape[1]):
goal_sps[ni, gi, :] = encode_one_hot(x=goals[ni, gi, 0], y=goals[ni, gi, 1], xs=xso, ys=yso)
elif spatial_encoding == 'trig':
goal_sps = np.zeros((n_mazes, n_goals, dim))
for ni in range(goal_sps.shape[0]):
for gi in range(goal_sps.shape[1]):
goal_sps[ni, gi, :] = encode_trig(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=dim)
elif spatial_encoding == 'random-trig':
goal_sps = np.zeros((n_mazes, n_goals, dim))
for ni in range(goal_sps.shape[0]):
for gi in range(goal_sps.shape[1]):
goal_sps[ni, gi, :] = encode_random_trig(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=dim)
elif spatial_encoding == 'random-proj':
goal_sps = np.zeros((n_mazes, n_goals, dim))
for ni in range(goal_sps.shape[0]):
for gi in range(goal_sps.shape[1]):
goal_sps[ni, gi, :] = encode_projection(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=dim)
else:
raise NotImplementedError
self.xs = data['xs']
self.ys = data['ys']
# n_mazes = goals.shape[0]
# n_goals = goals.shape[1]
self.maze_indices = maze_indices
self.goal_indices = goal_indices
self.n_mazes = len(maze_indices)
self.n_goals = len(goal_indices)
res = fine_mazes.shape[1]
dim = goal_sps.shape[2]
n_samples = int(res/subsample) * int(res/subsample) * self.n_mazes * self.n_goals
# Visualization
viz_locs = np.zeros((n_samples, 2))
viz_goals = np.zeros((n_samples, 2))
viz_loc_sps = np.zeros((n_samples, goal_sps.shape[2]))
viz_goal_sps = np.zeros((n_samples, goal_sps.shape[2]))
viz_output_dirs = np.zeros((n_samples, 2))
viz_maze_sps = np.zeros((n_samples, maze_sps.shape[1]))
# Generate data so each batch contains a single maze and goal
si = 0 # sample index, increments each time
for mi in maze_indices:
for gi in goal_indices:
for xi in range(0, res, subsample):
for yi in range(0, res, subsample):
loc_x = self.xs[xi]
loc_y = self.ys[yi]
viz_locs[si, 0] = loc_x
viz_locs[si, 1] = loc_y
viz_goals[si, :] = goals[mi, gi, :]
if spatial_encoding == 'ssp':
viz_loc_sps[si, :] = encode_point(loc_x, loc_y, x_axis_sp, y_axis_sp).v
elif spatial_encoding == 'random':
viz_loc_sps[si, :] = encode_random(loc_x, loc_y, dim)
elif spatial_encoding == '2d' or spatial_encoding == 'learned':
viz_loc_sps[si, :] = np.array([loc_x, loc_y])
elif spatial_encoding == '2d-normalized':
viz_loc_sps[si, :] = ((np.array([loc_x, loc_y]) - limit_low)*2 / (limit_high - limit_low)) - 1
elif spatial_encoding == 'one-hot':
viz_loc_sps[si, :] = encode_one_hot(x=loc_x, y=loc_y, xs=xso, ys=yso)
elif spatial_encoding == 'trig':
viz_loc_sps[si, :] = encode_trig(x=loc_x, y=loc_y, dim=dim)
elif spatial_encoding == 'random-trig':
viz_loc_sps[si, :] = encode_random_trig(x=loc_x, y=loc_y, dim=dim)
elif spatial_encoding == 'random-proj':
viz_loc_sps[si, :] = encode_projection(x=loc_x, y=loc_y, dim=dim)
viz_goal_sps[si, :] = goal_sps[mi, gi, :]
viz_output_dirs[si, :] = solved_mazes[mi, gi, xi, yi, :]
viz_maze_sps[si, :] = maze_sps[mi]
si += 1
self.batch_size = int(si / (self.n_mazes * self.n_goals))
print("Visualization Data Generated")
print("Total Samples: {}".format(si))
print("Mazes: {}".format(self.n_mazes))
print("Goals: {}".format(self.n_goals))
print("Batch Size: {}".format(self.batch_size))
print("Batches: {}".format(self.n_mazes * self.n_goals))
dataset_viz = MazeDataset(
maze_ssp=viz_maze_sps,
loc_ssps=viz_loc_sps,
goal_ssps=viz_goal_sps,
locs=viz_locs,
goals=viz_goals,
direction_outputs=viz_output_dirs,
)
# Each batch will contain the samples for one maze. Must not be shuffled
self.vizloader = torch.utils.data.DataLoader(
dataset_viz, batch_size=self.batch_size, shuffle=False, num_workers=0,
)
dim = 256
X = make_good_unitary(dim, rng=rng)
# X = spa.SemanticPointer(dim)
# X.make_unitary()
X_circ = circulant(X.v)
X_vec = circulant_matrix_to_vec(X_circ)
# assert (np.all(X_vec == X.v))
elif axis_vector_type == 'covariance':
# generate SSP based on a given circulant matrix
data = np.load(args.fname)
X_circ = data['covariance']
dim = X_circ.shape[0]
X_vec = circulant_matrix_to_vec(X_circ)
X = spa.SemanticPointer(data=X_vec)
X.make_unitary()
X_circ = circulant(X.v)
print(X.v)
elif axis_vector_type == 'pca':
data = np.load(args.dataset)
# if the dataset already has activations, just load them
if args.spatial_encoding in args.dataset:
print("Loading activations directly")
activations = data['activations']
flat_pos = data['positions']
else:
print("Computing activations")
# help='Directory for saved model and tensorboard log')
parser.add_argument('--logdir', type=str, default='', help='Directory for saved model and tensorboard log')
parser.add_argument('--load-saved-model', type=str, default='', help='Saved model to load from')
parser.add_argument('--folder', type=str, default='figure_output_new_colours', help='folder to save the figures')
args = parser.parse_args()
assert(args.limit_low < args.limit_high)
data = np.load(args.dataset)
rng = np.random.RandomState(seed=args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
x_axis_sp = spa.SemanticPointer(data=data['x_axis_sp'])
y_axis_sp = spa.SemanticPointer(data=data['y_axis_sp'])
# n_mazes by size by size
coarse_mazes = data['coarse_mazes']
# n_mazes by res by res
fine_mazes = data['fine_mazes']
# n_mazes by res by res by 2
solved_mazes = data['solved_mazes']
# n_mazes by dim
maze_sps = data['maze_sps']
print("Loading existing base maze data")
# base data already exists, load it instead of generating
base_data = np.load(base_name)
xs = base_data['xs']
ys = base_data['ys']
x_axis_vec = base_data['x_axis_sp']
y_axis_vec = base_data['y_axis_sp']
coarse_mazes = base_data['coarse_mazes']
fine_mazes = base_data['fine_mazes']
solved_mazes = base_data['solved_mazes']
maze_sps = base_data['maze_sps']
goal_sps = base_data['goal_sps']
goals = base_data['goals']
x_axis_sp = spa.SemanticPointer(data=x_axis_vec)
y_axis_sp = spa.SemanticPointer(data=y_axis_vec)
if (not args.no_sensors):
if not os.path.exists(sensor_name):
print("Generating Sensor Data")
n_mazes = solved_mazes.shape[0]
n_goals = solved_mazes.shape[1]
res = solved_mazes.shape[2]
coarse_maze_size = coarse_mazes.shape[1]
limit_low = xs[0]
limit_high = xs[-1]
sensor_scaling = (limit_high - limit_low) / coarse_maze_size
def create_dataloader(data, n_samples, maze_sps, args):
x_axis_sp = spa.SemanticPointer(data=data['x_axis_sp'])
y_axis_sp = spa.SemanticPointer(data=data['y_axis_sp'])
# n_mazes by size by size
coarse_mazes = data['coarse_mazes']
# n_mazes by res by res
fine_mazes = data['fine_mazes']
# n_mazes by res by res by 2
solved_mazes = data['solved_mazes']
# NOTE: this can be modified from the original dataset, so it is explicitly passed in
# n_mazes by dim
# maze_sps = data['maze_sps']
# n_mazes by n_goals by 2
goals = data['goals']
n_goals = goals.shape[1]
n_mazes = fine_mazes.shape[0]
# NOTE: only used for one-hot encoded location representation case
xso = np.linspace(args.limit_low, args.limit_high, int(np.sqrt(args.dim)))
yso = np.linspace(args.limit_low, args.limit_high, int(np.sqrt(args.dim)))
# n_mazes by n_goals by dim
if args.spatial_encoding == 'ssp':
goal_sps = data['goal_sps']
elif args.spatial_encoding == 'random':
goal_sps = np.zeros((n_mazes, n_goals, args.dim))
for ni in range(n_mazes):
for gi in range(n_goals):
goal_sps[ni, gi, :] = encode_random(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=args.dim)
elif args.spatial_encoding == '2d' or args.spatial_encoding == 'learned':
goal_sps = goals.copy()
elif args.spatial_encoding == '2d-normalized':
goal_sps = goals.copy()
goal_sps = ((goal_sps - xso[0]) * 2 / (xso[-1] - xso[0])) - 1
elif args.spatial_encoding == 'one-hot':
goal_sps = np.zeros((n_mazes, n_goals, len(xso)*len(yso)))
for ni in range(n_mazes):
for gi in range(n_goals):
goal_sps[ni, gi, :] = encode_one_hot(x=goals[ni, gi, 0], y=goals[ni, gi, 1], xs=xso, ys=yso)
elif args.spatial_encoding == 'trig':
goal_sps = np.zeros((n_mazes, n_goals, args.dim))
for ni in range(n_mazes):
for gi in range(n_goals):
goal_sps[ni, gi, :] = encode_trig(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=args.dim)
elif args.spatial_encoding == 'random-trig':
goal_sps = np.zeros((n_mazes, n_goals, args.dim))
for ni in range(n_mazes):
for gi in range(n_goals):
goal_sps[ni, gi, :] = encode_random_trig(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=args.dim)
elif args.spatial_encoding == 'random-proj':
goal_sps = np.zeros((n_mazes, n_goals, args.dim))
for ni in range(n_mazes):
for gi in range(n_goals):
goal_sps[ni, gi, :] = encode_projection(x=goals[ni, gi, 0], y=goals[ni, gi, 1], dim=args.dim)
else:
raise NotImplementedError
if 'xs' in data.keys():
xs = data['xs']
ys = data['ys']
else:
# backwards compatibility
xs = np.linspace(args.limit_low, args.limit_high, args.res)
ys = np.linspace(args.limit_low, args.limit_high, args.res)
free_spaces = np.argwhere(fine_mazes == 0)
n_free_spaces = free_spaces.shape[0]
# Training
train_locs = np.zeros((n_samples, 2))
train_goals = np.zeros((n_samples, 2))
train_loc_sps = np.zeros((n_samples, goal_sps.shape[2]))
train_goal_sps = np.zeros((n_samples, goal_sps.shape[2]))
train_output_dirs = np.zeros((n_samples, 2))
train_maze_sps = np.zeros((n_samples, maze_sps.shape[1]))
train_indices = np.random.randint(low=0, high=n_free_spaces, size=n_samples)
for n in range(n_samples):
# print("Sample {} of {}".format(n + 1, n_samples))
# n_mazes by res by res
indices = free_spaces[train_indices[n], :]
maze_index = indices[0]
x_index = indices[1]
y_index = indices[2]
goal_index = np.random.randint(low=0, high=n_goals)
# 2D coordinate of the agent's current location
loc_x = xs[x_index]
loc_y = ys[y_index]
train_locs[n, 0] = loc_x
train_locs[n, 1] = loc_y
train_goals[n, :] = goals[maze_index, goal_index, :]
if args.spatial_encoding == 'ssp':
train_loc_sps[n, :] = encode_point(loc_x, loc_y, x_axis_sp, y_axis_sp).v
elif args.spatial_encoding == 'random':
train_loc_sps[n, :] = encode_random(loc_x, loc_y, args.dim)
elif args.spatial_encoding == '2d' or args.spatial_encoding == 'learned':
train_loc_sps[n, :] = np.array([loc_x, loc_y])
elif args.spatial_encoding == '2d-normalized':
train_loc_sps[n, :] = ((np.array([loc_x, loc_y]) - xso[0]) * 2 / (xso[-1] - xso[0])) - 1
elif args.spatial_encoding == 'one-hot':
train_loc_sps[n, :] = encode_one_hot(x=loc_x, y=loc_y, xs=xso, ys=yso)
elif args.spatial_encoding == 'trig':
train_loc_sps[n, :] = encode_trig(x=loc_x, y=loc_y, dim=args.dim)
elif args.spatial_encoding == 'random-trig':
train_loc_sps[n, :] = encode_random_trig(x=loc_x, y=loc_y, dim=args.dim)
elif args.spatial_encoding == 'random-proj':
train_loc_sps[n, :] = encode_projection(x=loc_x, y=loc_y, dim=args.dim)
train_goal_sps[n, :] = goal_sps[maze_index, goal_index, :]
train_output_dirs[n, :] = solved_mazes[maze_index, goal_index, x_index, y_index, :]
train_maze_sps[n, :] = maze_sps[maze_index]
dataset_train = MazeDataset(
maze_ssp=train_maze_sps,
loc_ssps=train_loc_sps,
goal_ssps=train_goal_sps,
locs=train_locs,
goals=train_goals,
direction_outputs=train_output_dirs,
)
trainloader = torch.utils.data.DataLoader(
dataset_train, batch_size=args.batch_size, shuffle=True, num_workers=0,
)
return trainloader