n_cconv_neurons = 50 #10
#ssp_cleanup_path = '/home/bjkomer/metric-representation/metric_representation/pytorch/ssp_cleanup_cosine_15_items/May07_15-21-15/model.pt'
ssp_cleanup_path = '/home/ctnuser/metric-representation/metric_representation/pytorch/ssp_cleanup_cosine_15_items/May07_15-21-15/model.pt'
def angle_to_ssp(x):
return encode_point(np.cos(x), np.sin(x), x_axis_sp, y_axis_sp).v
heatmap_vectors = get_heatmap_vectors(xs, ys, x_axis_sp, y_axis_sp)
spatial_heatmap = SpatialHeatmap(heatmap_vectors,
xs,
ys,
cmap='plasma',
vmin=None,
vmax=None)
with model:
# Initial kick to get the starting location SSP to represent 0,0
initial_stim = nengo.Node(lambda t: 1 if t < 0.01 else 0)
direction_heatmap_node = nengo.Node(
SpatialHeatmap(heatmap_vectors,
xs,
ys,
cmap='plasma',
vmin=None,
vmax=None),
nengo.Connection(cconv_view.output, exp_node[args.dim:])
nengo.Connection(exp_node[args.dim:2 * args.dim], model.queried_item.input)
# drive the memory to the next start when the current task is finished
nengo.Connection(exp_node[2 * args.dim:], model.current_loc.input)
nengo.Connection(model.current_loc.output, exp_node[:args.dim])
initial_kick = nengo.Node(lambda t: 1 if t < 0.01 else 0)
nengo.Connection(initial_kick, model.current_loc.input[0])
if __name__ != '__main__':
# plots for debugging
heatmap = SpatialHeatmap(heatmap_vectors=hmv,
xs=xs,
ys=xs,
cmap='plasma',
vmin=-1,
vmax=1)
heatmap_node = nengo.Node(heatmap, size_in=args.dim, size_out=0)
nengo.Connection(model.current_loc.output, heatmap_node)
dir_heatmap = SpatialHeatmap(heatmap_vectors=hmv,
xs=xs,
ys=xs,
cmap='plasma',
vmin=-1,
vmax=1)
dir_heatmap_node = nengo.Node(heatmap, size_in=args.dim, size_out=0)
nengo.Connection(model.direction.output, dir_heatmap_node)
if __name__ == '__main__':
if __name__ == '__main__':
# probes
spikes_p = nengo.Probe(ssp_loc.neurons, synapse=0.01)
bound_spikes_p = nengo.Probe(memory.neurons, synapse=0.01)
pos_2d_p = nengo.Probe(pos_2d)
else:
res = 128
xs = np.linspace(limit_low, limit_high, res)
ys = np.linspace(limit_low, limit_high, res)
heatmap_vectors = get_heatmap_vectors(xs, ys, X, Y)
# create a visualization of the heatmap
heatmap_node = nengo.Node(
SpatialHeatmap(
heatmap_vectors,
xs,
ys,
cmap='plasma',
vmin=-1, # None,
vmax=1, # None,
),
size_in=dim,
size_out=0,
)
memory_heatmap_node = nengo.Node(
SpatialHeatmap(
heatmap_vectors,
xs,
ys,
cmap='plasma',
vmin=-1, # None,
vmax=1, # None,
for n in range(n_neurons):
encoders_place_cell_dir[n, :] = to_ssp(preferred_locations_dir[n, :])
model = nengo.Network(seed=seed)
model.config[nengo.Ensemble].neuron_type = nengo.LIF()
# model.config[nengo.Ensemble].neuron_type=nengo.Direct()
with model:
# Initial kick to get the starting location SSP to represent 0,0
# initial_stim = nengo.Node(lambda t: 1 if t < 0.01 else 0)
initial_stim = nengo.Node(lambda t: 1 if t < 0.1 else 0)
direction_heatmap_node = nengo.Node(
SpatialHeatmap(heatmap_vectors_dir,
xs_dir,
ys_dir,
cmap='plasma',
vmin=None,
vmax=None),
size_in=dim,
size_out=0,
)
location_heatmap_node = nengo.Node(
SpatialHeatmap(heatmap_vectors,
xs,
ys,
cmap='plasma',
vmin=None,
vmax=None),
size_in=dim,
size_out=0,
)
# )
nengo.Connection(
policy_out_node,
env,
synapse=None,
)
# SSP visualization
if __name__ != "__main__":
vmin = 0
vmax = 1
spatial_cleanup_input_heatmap_node = nengo.Node(
SpatialHeatmap(heatmap_vectors,
xs,
ys,
cmap='plasma',
vmin=vmin,
vmax=vmax),
size_in=args.dim,
size_out=0,
)
if args.use_cleanup_gt:
nengo.Connection(spatial_cleanup,
spatial_cleanup_input_heatmap_node)
else:
# nengo.Connection(policy_inp_node[2 * args.dim:3 * args.dim], spatial_cleanup_heatmap_node)
# input to the cleanup
nengo.Connection(env[args.maze_id_dim:args.maze_id_dim + args.dim],
spatial_cleanup_input_heatmap_node)
localization_heatmap_node = nengo.Node(
# model.cconv_location = nengo.networks.CircularConvolution(
# n_neurons=neurons_per_dim * 2, dimensions=dim, invert_b=True
# )
model.cconv_location = spa.networks.CircularConvolution(
n_neurons=neurons_per_dim * 2, dimensions=dim, invert_b=True)
nengo.Connection(model.noisy_level_filler_id_cleanup.output,
model.cconv_location.input_a)
nengo.Connection(item_id_input_node, model.cconv_location.input_b)
out_node = nengo.Node(size_in=dim, size_out=0)
nengo.Connection(model.cconv_location.output, out_node)
heatmap_node = nengo.Node(
SpatialHeatmap(hmv, xs, ys, cmap='plasma', vmin=None, vmax=None),
size_in=dim,
size_out=0,
)
nengo.Connection(model.cconv_location.output, heatmap_node)
with direct_config:
model.item_id_reader = spa.State(vocab,
# neuron_type=nengo.Direct()
)
nengo.Connection(item_id_input_node, model.item_id_reader.input)
model.item_reader = spa.State(vocab, )
nengo.Connection(item_input_node, model.item_reader.input)
vocab=dim,
phis=phis,
angles=angles,
feedback=1.01,
limit_low=-limit,
limit_high=limit,
)
# x, y, and on/off
ssp_input = nengo.Node([0, 0, 0])
nengo.Connection(ssp_input, model.current_loc.input, function=input_ssp)
heatmap = SpatialHeatmap(heatmap_vectors=hmv,
xs=xs,
ys=xs,
cmap='plasma',
vmin=-1,
vmax=1)
heatmap_node = nengo.Node(heatmap, size_in=dim, size_out=0)
nengo.Connection(model.current_loc.output, heatmap_node)
heatmap_true = SpatialHeatmap(heatmap_vectors=hmv,
xs=xs,
ys=xs,
cmap='plasma',
vmin=None,
vmax=None)
heatmap_true_node = nengo.Node(heatmap_true, size_in=dim, size_out=0)
# nengo.Connection(model.current_loc.input, heatmap_true_node)
nengo.Connection(ssp_input, heatmap_true_node, function=input_ssp)