def test_thalamus(Simulator, plt, seed):
model = spa.SPA(seed=seed)
with model:
model.vision = spa.Buffer(dimensions=16, neurons_per_dimension=80)
model.vision2 = spa.Buffer(dimensions=16, neurons_per_dimension=80)
model.motor = spa.Buffer(dimensions=16, neurons_per_dimension=80)
model.motor2 = spa.Buffer(dimensions=32, neurons_per_dimension=80)
actions = spa.Actions(
'dot(vision, A) --> motor=A, motor2=vision*vision2',
'dot(vision, B) --> motor=vision, motor2=vision*A*~B',
'dot(vision, ~A) --> motor=~vision, motor2=~vision*vision2')
model.bg = spa.BasalGanglia(actions)
model.thalamus = spa.Thalamus(model.bg)
def input_f(t):
if t < 0.1:
return 'A'
elif t < 0.3:
return 'B'
elif t < 0.5:
return '~A'
else:
return '0'
model.input = spa.Input(vision=input_f, vision2='B*~A')
input, vocab = model.get_module_input('motor')
input2, vocab2 = model.get_module_input('motor2')
p = nengo.Probe(input, 'output', synapse=0.03)
p2 = nengo.Probe(input2, 'output', synapse=0.03)
with Simulator(model) as sim:
sim.run(0.5)
t = sim.trange()
data = vocab.dot(sim.data[p].T)
data2 = vocab2.dot(sim.data[p2].T)
plt.subplot(2, 1, 1)
plt.plot(t, data.T)
plt.subplot(2, 1, 2)
plt.plot(t, data2.T)
# Action 1
assert data[0, t == 0.1] > 0.8
assert data[1, t == 0.1] < 0.2
assert data2[0, t == 0.1] < 0.35
assert data2[1, t == 0.1] > 0.4
# Action 2
assert data[0, t == 0.3] < 0.2
assert data[1, t == 0.3] > 0.8
assert data2[0, t == 0.3] > 0.5
assert data2[1, t == 0.3] < 0.3
# Action 3
assert data[0, t == 0.5] > 0.8
assert data[1, t == 0.5] < 0.2
assert data2[0, t == 0.5] < 0.5
assert data2[1, t == 0.5] > 0.4
def model(self, p):
model = spa.SPA()
with model:
model.word = spa.State(dimensions=p.D)
model.marker = spa.State(dimensions=p.D)
model.memory = spa.State(dimensions=p.D, feedback=1)
model.cortical = spa.Cortical(
spa.Actions('memory = word * marker', ))
def word(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'S%d' % index
return '0'
def marker(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'M%d' % index
return '0'
model.input = spa.Input(word=word, marker=marker)
self.p_memory = nengo.Probe(model.memory.output, synapse=0.03)
self.vocab = model.get_output_vocab('memory')
return model
def thalamus_net(d=2, n=20, seed=None):
model = spa.SPA(seed=seed)
with model:
model.vision = spa.Buffer(dimensions=d, neurons_per_dimension=n)
model.vision2 = spa.Buffer(dimensions=d, neurons_per_dimension=n)
model.motor = spa.Buffer(dimensions=d, neurons_per_dimension=n)
model.motor2 = spa.Buffer(dimensions=d * 2, neurons_per_dimension=n)
actions = spa.Actions(
'dot(vision, A) --> motor=A, motor2=vision*vision2',
'dot(vision, B) --> motor=vision, motor2=vision*A*~B',
'dot(vision, ~A) --> motor=~vision, motor2=~vision*vision2')
model.bg = spa.BasalGanglia(actions)
model.thalamus = spa.Thalamus(model.bg)
def input_f(t):
if t < 0.1:
return 'A'
elif t < 0.3:
return 'B'
elif t < 0.5:
return '~A'
else:
return '0'
model.input = spa.Input(vision=input_f, vision2='B*~A')
return model
def __init__(self):
self.buffer1 = spa.Buffer(dimensions=16)
self.buffer2 = spa.Buffer(dimensions=16)
self.buffer3 = spa.Buffer(dimensions=16)
self.cortical = spa.Cortical(
spa.Actions('buffer2=buffer1', 'buffer3=~buffer1'))
self.input = spa.Input(buffer1='A')
def test_routing(Simulator, seed, plt):
D = 3
model = spa.SPA(seed=seed)
with model:
model.ctrl = spa.Buffer(16, label="ctrl")
def input_func(t):
if t < 0.2:
return "A"
elif t < 0.4:
return "B"
else:
return "C"
model.input = spa.Input(ctrl=input_func)
model.buff1 = spa.Buffer(D, label="buff1")
model.buff2 = spa.Buffer(D, label="buff2")
model.buff3 = spa.Buffer(D, label="buff3")
node1 = nengo.Node([0, 1, 0])
node2 = nengo.Node([0, 0, 1])
nengo.Connection(node1, model.buff1.state.input)
nengo.Connection(node2, model.buff2.state.input)
actions = spa.Actions(
"dot(ctrl, A) --> buff3=buff1",
"dot(ctrl, B) --> buff3=buff2",
"dot(ctrl, C) --> buff3=buff1*buff2",
)
model.bg = spa.BasalGanglia(actions)
model.thal = spa.Thalamus(model.bg)
buff3_probe = nengo.Probe(model.buff3.state.output, synapse=0.03)
with Simulator(model) as sim:
sim.run(0.6)
data = sim.data[buff3_probe]
plt.plot(sim.trange(), data)
valueA = np.mean(data[150:200], axis=0) # should be [0, 1, 0]
valueB = np.mean(data[350:400], axis=0) # should be [0, 0, 1]
valueC = np.mean(data[550:600], axis=0) # should be [1, 0, 0]
assert valueA[0] < 0.2
assert valueA[1] > 0.8
assert valueA[2] < 0.2
assert valueB[0] < 0.2
assert valueB[1] < 0.2
assert valueB[2] > 0.8
assert valueC[0] > 0.8
assert valueC[1] < 0.2
assert valueC[2] < 0.2
def test_errors():
# buffer2 does not exist
with pytest.raises(NameError):
with spa.SPA() as model:
model.buffer = spa.Buffer(dimensions=16)
model.cortical = spa.Cortical(spa.Actions('buffer2=buffer'))
# conditional expressions not implemented
with pytest.raises(NotImplementedError):
with spa.SPA() as model:
model.buffer = spa.Buffer(dimensions=16)
model.cortical = spa.Cortical(
spa.Actions('dot(buffer,A) --> buffer=buffer'))
# dot products not implemented
with pytest.raises(NotImplementedError):
with spa.SPA() as model:
model.scalar = spa.Buffer(dimensions=1, subdimensions=1)
model.cortical = spa.Cortical(
spa.Actions('scalar=dot(scalar, FOO)'))
def test_routing_recurrency_compilation(Simulator, seed):
D = 2
model = spa.SPA(seed=seed)
with model:
model.buff1 = spa.Buffer(D, label="buff1")
model.buff2 = spa.Buffer(D, label="buff2")
actions = spa.Actions("0.5 --> buff2=buff1, buff1=buff2")
model.bg = spa.BasalGanglia(actions)
model.thal = spa.Thalamus(model.bg)
with Simulator(model) as sim:
assert sim
def test_thalamus(Simulator):
model = spa.SPA(seed=30)
with model:
model.vision = spa.Buffer(dimensions=16, neurons_per_dimension=80)
model.vision2 = spa.Buffer(dimensions=16, neurons_per_dimension=80)
model.motor = spa.Buffer(dimensions=16, neurons_per_dimension=80)
model.motor2 = spa.Buffer(dimensions=32, neurons_per_dimension=80)
actions = spa.Actions(
'dot(vision, A) --> motor=A, motor2=vision*vision2',
'dot(vision, B) --> motor=vision, motor2=vision*A*~B',
'dot(vision, ~A) --> motor=~vision, motor2=~vision*vision2'
)
model.bg = spa.BasalGanglia(actions)
model.thalamus = spa.Thalamus(model.bg)
def input_f(t):
if t < 0.1:
return 'A'
elif t < 0.3:
return 'B'
elif t < 0.5:
return '~A'
else:
return '0'
model.input = spa.Input(vision=input_f, vision2='B*~A')
input, vocab = model.get_module_input('motor')
input2, vocab2 = model.get_module_input('motor2')
p = nengo.Probe(input, 'output', synapse=0.03)
p2 = nengo.Probe(input2, 'output', synapse=0.03)
sim = Simulator(model)
sim.run(0.5)
data = vocab.dot(sim.data[p].T)
data2 = vocab2.dot(sim.data[p2].T)
assert 0.8 < data[0, 100] < 1.1 # Action 1
assert -0.2 < data2[0, 100] < 0.35
assert -0.25 < data[1, 100] < 0.2
assert 0.4 < data2[1, 100] < 0.6
assert -0.2 < data[0, 299] < 0.2 # Action 2
assert 0.6 < data2[0, 299] < 0.95
assert 0.8 < data[1, 299] < 1.1
assert -0.2 < data2[1, 299] < 0.2
assert 0.8 < data[0, 499] < 1.0 # Action 3
assert 0.0 < data2[0, 499] < 0.5
assert -0.2 < data[1, 499] < 0.2
assert 0.4 < data2[1, 499] < 0.7
def model(self, p):
model = spa.SPA()
with model:
model.word = spa.State(dimensions=p.D)
model.marker = spa.State(dimensions=p.D)
model.memory = spa.State(dimensions=p.D, feedback=1)
model.cortical = spa.Cortical(
spa.Actions('memory = word * marker', ))
def word(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'S%d' % index
return '0'
def marker(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'M%d' % index
return '0'
model.input = spa.Input(word=word, marker=marker)
self.p_memory = nengo.Probe(model.memory.output, synapse=0.03)
self.vocab = model.get_output_vocab('memory')
split.split_input_nodes(model, max_dim=16)
self.replaced = split.split_passthrough(model, max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[nengo_spinnaker.Simulator].placer_kwargs = dict(
effort=0.1)
split.remove_outputless_passthrough(model)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def test_copy_spa(Simulator):
with spa.SPA() as original:
original.state = spa.State(16)
original.cortex = spa.Cortical(spa.Actions("state = A"))
cp = original.copy()
# Check that it still builds.
with Simulator(cp):
pass
# check vocab instance param is set
for node in cp.all_nodes:
if node.label in ["input", "output"]:
assert cp.config[node].vocab is not None
def test_routing(Simulator):
D = 2
model = spa.SPA(seed=123)
with model:
model.ctrl = spa.Buffer(D, label='ctrl')
def input_func(t):
if t < 0.25:
return 'A'
else:
return 'B'
model.input = spa.Input(ctrl=input_func)
model.buff1 = spa.Buffer(D, label='buff1')
model.buff2 = spa.Buffer(D, label='buff2')
model.buff3 = spa.Buffer(D, label='buff3')
node1 = nengo.Node([1, 0])
node2 = nengo.Node([0, 1])
nengo.Connection(node1, model.buff1.state.input)
nengo.Connection(node2, model.buff2.state.input)
actions = spa.Actions('dot(ctrl, A) --> buff3=buff1',
'dot(ctrl, B) --> buff3=buff2')
model.bg = spa.BasalGanglia(actions)
model.thal = spa.Thalamus(model.bg)
buff1_probe = nengo.Probe(model.buff1.state.output, synapse=0.03)
buff2_probe = nengo.Probe(model.buff2.state.output, synapse=0.03)
buff3_probe = nengo.Probe(model.buff3.state.output, synapse=0.03)
thal_probe = nengo.Probe(model.thal.output, synapse=0.03)
sim = nengo.Simulator(model)
sim.run(0.5)
data1 = sim.data[buff1_probe]
data2 = sim.data[buff2_probe]
data3 = sim.data[buff3_probe]
data_thal = sim.data[thal_probe]
trange = sim.trange()
assert abs(np.mean(data1[trange < 0.25] - data3[trange < 0.25])) < 0.15
assert abs(np.mean(data2[trange > 0.25] - data3[trange > 0.25])) < 0.15
assert data_thal[249][1] < 0.01
assert data_thal[249][0] > 0.8
assert data_thal[499][1] > 0.8
assert data_thal[499][0] < 0.01
def test_nondefault_routing(Simulator, seed):
D = 3
model = spa.SPA(seed=seed)
with model:
model.ctrl = spa.Buffer(16, label="ctrl")
def input_func(t):
if t < 0.2:
return "A"
elif t < 0.4:
return "B"
else:
return "C"
model.input = spa.Input(ctrl=input_func)
model.buff1 = spa.Buffer(D, label="buff1")
model.buff2 = spa.Buffer(D, label="buff2")
model.cmp = spa.Compare(D)
node1 = nengo.Node([0, 1, 0])
node2 = nengo.Node([0, 0, 1])
nengo.Connection(node1, model.buff1.state.input)
nengo.Connection(node2, model.buff2.state.input)
actions = spa.Actions(
"dot(ctrl, A) --> cmp_A=buff1, cmp_B=buff1",
"dot(ctrl, B) --> cmp_A=buff1, cmp_B=buff2",
"dot(ctrl, C) --> cmp_A=buff2, cmp_B=buff2",
)
model.bg = spa.BasalGanglia(actions)
model.thal = spa.Thalamus(model.bg)
compare_probe = nengo.Probe(model.cmp.output, synapse=0.03)
with Simulator(model) as sim:
sim.run(0.6)
similarity = sim.data[compare_probe]
valueA = np.mean(similarity[150:200], axis=0) # should be [1]
valueB = np.mean(similarity[350:400], axis=0) # should be [0]
valueC = np.mean(similarity[550:600], axis=0) # should be [1]
assert valueA > 0.6
assert valueB < 0.3
assert valueC > 0.6
def test_nondefault_routing(Simulator, seed, plt):
D = 3
model = spa.SPA(seed=seed)
with model:
model.ctrl = spa.Buffer(16, label='ctrl')
def input_func(t):
if t < 0.2:
return 'A'
elif t < 0.4:
return 'B'
else:
return 'C'
model.input = spa.Input(ctrl=input_func)
model.buff1 = spa.Buffer(D, label='buff1')
model.buff2 = spa.Buffer(D, label='buff2')
model.cmp = spa.Compare(D)
node1 = nengo.Node([0, 1, 0])
node2 = nengo.Node([0, 0, 1])
nengo.Connection(node1, model.buff1.state.input)
nengo.Connection(node2, model.buff2.state.input)
actions = spa.Actions('dot(ctrl, A) --> cmp_A=buff1, cmp_B=buff1',
'dot(ctrl, B) --> cmp_A=buff1, cmp_B=buff2',
'dot(ctrl, C) --> cmp_A=buff2, cmp_B=buff2',
)
model.bg = spa.BasalGanglia(actions)
model.thal = spa.Thalamus(model.bg)
compare_probe = nengo.Probe(model.cmp.output, synapse=0.03)
sim = Simulator(model)
sim.run(0.6)
vocab = model.get_output_vocab('cmp')
data = sim.data[compare_probe]
similarity = np.dot(data, vocab.parse('YES').v)
valueA = np.mean(similarity[150:200], axis=0) # should be [1]
valueB = np.mean(similarity[350:400], axis=0) # should be [0]
valueC = np.mean(similarity[550:600], axis=0) # should be [1]
assert valueA > 0.6
assert valueB < 0.3
assert valueC > 0.6
def test_transform(Simulator, seed):
with spa.SPA(seed=seed) as model:
model.buffer1 = spa.Buffer(dimensions=16)
model.buffer2 = spa.Buffer(dimensions=16)
model.cortical = spa.Cortical(spa.Actions("buffer2=buffer1*B"))
model.input = spa.Input(buffer1="A")
output, vocab = model.get_module_output("buffer2")
with model:
p = nengo.Probe(output, "output", synapse=0.03)
with Simulator(model) as sim:
sim.run(0.2)
match = np.dot(sim.data[p], vocab.parse("A*B").v)
assert match[199] > 0.7
def create_model():
D = 16
model = spa.SPA(seed=1)
with model:
model.a = spa.State(dimensions=D)
model.b = spa.State(dimensions=D)
model.c = spa.State(dimensions=D)
model.cortical = spa.Cortical(spa.Actions(
'c = a+b',
))
model.input = spa.Input(
a='A',
b=(lambda t: 'C*~A' if (t%0.1 < 0.05) else 'D*~A'),
)
return model, list(), dict()
def test_translate(Simulator, seed):
with spa.SPA(seed=seed) as model:
model.buffer1 = spa.Buffer(dimensions=16)
model.buffer2 = spa.Buffer(dimensions=32)
model.input = spa.Input(buffer1='A')
model.cortical = spa.Cortical(spa.Actions('buffer2=buffer1'))
output, vocab = model.get_module_output('buffer2')
with model:
p = nengo.Probe(output, 'output', synapse=0.03)
sim = Simulator(model)
sim.run(0.2)
match = np.dot(sim.data[p], vocab.parse('A').v)
assert match[199] > 0.8
def model(self, p):
model = spa.SPA()
with model:
model.word = spa.State(dimensions=p.D)
model.marker = spa.State(dimensions=p.D)
model.memory = spa.State(dimensions=p.D, feedback=1)
model.motor = spa.State(dimensions=p.D)
model.cue = spa.State(dimensions=p.D)
model.cortical = spa.Cortical(
spa.Actions(
'memory = word * marker',
'motor = memory * ~cue',
))
def word(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'S%d' % index
return '0'
def marker(t):
index = t / p.time_per_symbol
if index < p.n_symbols:
return 'M%d' % index
return '0'
def cue(t):
index = (t - p.time_per_symbol * p.n_symbols) / p.time_per_cue
if index > 0:
index = index % (2 * p.n_symbols)
if index < p.n_symbols:
return 'S%d' % index
else:
return 'M%d' % (index - p.n_symbols)
return '0'
model.input = spa.Input(word=word, marker=marker, cue=cue)
self.p_memory = nengo.Probe(model.memory.output, synapse=0.03)
self.p_motor = nengo.Probe(model.motor.output, synapse=0.03)
self.vocab = model.get_output_vocab('motor')
return model
def model(self, p):
model = spa.SPA()
with model:
model.vision = spa.Buffer(dimensions=p.D)
model.state = spa.Memory(dimensions=p.D)
actions = ['dot(state, S%d) --> state=S%d' % (i,(i+1))
for i in range(p.n_actions - 1)]
actions.append('dot(state, S%d) --> state=vision' %
(p.n_actions - 1))
model.bg = spa.BasalGanglia(actions=spa.Actions(*actions))
model.thal = spa.Thalamus(model.bg)
model.input = spa.Input(vision='S%d' % p.start,
state=lambda t: 'S%d' % p.start if
t < 0.1 else '0')
self.probe = nengo.Probe(model.thal.actions.output, synapse=0.03)
return model
def model(self, p):
model = spa.SPA()
with model:
model.vision = spa.Buffer(dimensions=p.D)
model.state = spa.Memory(dimensions=p.D)
actions = [
'dot(state, S%d) --> state=S%d' % (i, (i + 1))
for i in range(p.n_actions - 1)
]
actions.append('dot(state, S%d) --> state=vision' %
(p.n_actions - 1))
model.bg = spa.BasalGanglia(actions=spa.Actions(*actions))
model.thal = spa.Thalamus(model.bg)
model.input = spa.Input(vision='S%d' % p.start,
state=lambda t: 'S%d' % p.start
if t < 0.1 else '0')
self.probe = nengo.Probe(model.thal.actions.output, synapse=0.03)
split.split_passthrough(model, max_dim=p.split_max_dim)
if p.pass_ensembles > 0:
split.pass_ensembles(model, max_dim=p.pass_ensembles)
if p.backend == 'nengo_spinnaker':
import nengo_spinnaker
nengo_spinnaker.add_spinnaker_params(model.config)
for node in model.all_nodes:
if node.output is None:
if node.size_in > p.pf_max_dim:
print 'limiting', node
model.config[node].n_cores_per_chip = p.pf_cores
model.config[node].n_chips = p.pf_n_chips
model.config[nengo_spinnaker.Simulator].placer_kwargs = dict(
effort=0.1)
if p.fixed_seed:
for ens in model.all_ensembles:
ens.seed = 1
return model
def test_basal_ganglia(Simulator, seed, plt):
model = spa.SPA(seed=seed)
with model:
model.vision = spa.Buffer(dimensions=16)
model.motor = spa.Buffer(dimensions=16)
actions = spa.Actions(
'0.5 --> motor=A',
'dot(vision,CAT) --> motor=B',
'dot(vision*CAT,DOG) --> motor=C',
'2*dot(vision,CAT*0.5) --> motor=D',
'dot(vision,CAT)+0.5-dot(vision,CAT) --> motor=E',
)
model.bg = spa.BasalGanglia(actions)
def input(t):
if t < 0.1:
return '0'
elif t < 0.2:
return 'CAT'
elif t < 0.3:
return 'DOG*~CAT'
else:
return '0'
model.input = spa.Input(vision=input)
p = nengo.Probe(model.bg.input, 'output', synapse=0.03)
sim = Simulator(model)
sim.run(0.3)
t = sim.trange()
plt.plot(t, sim.data[p])
plt.title('Basal Ganglia output')
assert 0.6 > sim.data[p][t == 0.1, 0] > 0.4
assert sim.data[p][t == 0.2, 1] > 0.8
assert sim.data[p][-1, 2] > 0.6
assert np.allclose(sim.data[p][:, 1], sim.data[p][:, 3])
assert np.allclose(sim.data[p][:, 0], sim.data[p][:, 4])
def model(self, p):
model = spa.SPA()
with model:
model.memory = spa.State(p.D, feedback=1)
model.cue = spa.State(p.D)
model.input = spa.Input(memory=lambda t: 'P1*DOG+P2*CAT+P3*RAT'
if t < 0.1 else '0',
cue='P3')
model.item = spa.State(p.D)
model.cortical = spa.Cortical(
spa.Actions(
'item = memory*~cue',
'memory = %g*item*cue' % p.strength,
))
self.p_memory = nengo.Probe(model.memory.output, synapse=0.03)
self.vocab = model.get_output_vocab('memory')
return model
def model(self, p):
model = spa.SPA()
with model:
model.state = spa.Memory(dimensions=p.D)
actions = [
'dot(state, S%d) --> state=S%d' % (i, (i + 1) % p.n_actions)
for i in range(p.n_actions)
]
model.bg = spa.BasalGanglia(actions=spa.Actions(*actions))
model.thal = spa.Thalamus(model.bg)
def state_input(t):
if t < 0.1:
return 'S0'
else:
return '0'
model.input = spa.Input(state=state_input)
self.probe = nengo.Probe(model.thal.actions.output, synapse=0.03)
return model
def __init__(self, num_actions):
spa.SPA.__init__(self)
#Specify the modules to be used
self.cortex = spa.Buffer(dimensions=dimensions)
# Generate names for actions
def int_to_ascii(i):
return chr(ord("A") + i)
# Generate sequential action names
self.action_names = [
int_to_ascii((i / 26) % 26) + int_to_ascii(i % 26)
for i in range(num_actions)
]
# From this build action mapping
action_mappings = [
"dot(cortex, %s) --> cortex = %s" % (a, b)
for (a, b) in zip(self.action_names, self.action_names[1:])
]
action_mappings.append("dot(cortex, %s) --> cortex = %s" %
(self.action_names[-1], self.action_names[0]))
# Build SPA actions
actions = spa.Actions(*action_mappings)
self.bg = spa.BasalGanglia(actions=actions)
self.thal = spa.Thalamus(self.bg)
#Specify the input
def start(t):
if t < 0.05:
return self.action_names[0]
else:
return '0'
self.input = spa.Input(cortex=start)
def model(self, p):
model = spa.SPA()
with model:
model.inA = spa.Buffer(p.D, subdimensions=p.SD,
neurons_per_dimension=p.n_per_d)
model.inB = spa.Buffer(p.D, subdimensions=p.SD,
neurons_per_dimension=p.n_per_d)
model.result = spa.Buffer(p.D, subdimensions=p.SD,
neurons_per_dimension=p.n_per_d)
model.cortical = spa.Cortical(spa.Actions('result = inA * inB'),
synapse=p.pstc,
neurons_cconv=p.n_cconv)
model.input = spa.Input(inA='A', inB='B')
self.probe = nengo.Probe(model.result.state.output, synapse=p.pstc)
ideal = nengo.Node(model.get_output_vocab('inA').parse('A*B').v)
self.probe_ideal = nengo.Probe(ideal, synapse=None)
return model
def test_connect(Simulator, seed):
with spa.SPA(seed=seed) as model:
model.buffer1 = spa.Buffer(dimensions=16)
model.buffer2 = spa.Buffer(dimensions=16)
model.buffer3 = spa.Buffer(dimensions=16)
model.cortical = spa.Cortical(
spa.Actions("buffer2=buffer1", "buffer3=~buffer1"))
model.input = spa.Input(buffer1="A")
output2, vocab = model.get_module_output("buffer2")
output3, vocab = model.get_module_output("buffer3")
with model:
p2 = nengo.Probe(output2, "output", synapse=0.03)
p3 = nengo.Probe(output3, "output", synapse=0.03)
with Simulator(model) as sim:
sim.run(0.2)
match = np.dot(sim.data[p2], vocab.parse("A").v)
assert match[199] > 0.9
match = np.dot(sim.data[p3], vocab.parse("~A").v)
assert match[199] > 0.9
def test_direct(Simulator, seed):
with spa.SPA(seed=seed) as model:
model.buffer1 = spa.Buffer(dimensions=16)
model.buffer2 = spa.Buffer(dimensions=32)
model.cortical = spa.Cortical(
spa.Actions('buffer1=A', 'buffer2=B', 'buffer1=C, buffer2=C'))
output1, vocab1 = model.get_module_output('buffer1')
output2, vocab2 = model.get_module_output('buffer2')
with model:
p1 = nengo.Probe(output1, 'output', synapse=0.03)
p2 = nengo.Probe(output2, 'output', synapse=0.03)
sim = Simulator(model)
sim.run(0.2)
match1 = np.dot(sim.data[p1], vocab1.parse('A+C').v)
match2 = np.dot(sim.data[p2], vocab2.parse('B+C').v)
# both values should be near 1.0 since buffer1 is driven to both A and C
# and buffer2 is driven to both B and C.
assert match1[199] > 0.75
assert match2[199] > 0.75
def test_connect(Simulator, seed):
with spa.SPA(seed=seed) as model:
model.buffer1 = spa.Buffer(dimensions=16)
model.buffer2 = spa.Buffer(dimensions=16)
model.buffer3 = spa.Buffer(dimensions=16)
model.cortical = spa.Cortical(
spa.Actions('buffer2=buffer1', 'buffer3=~buffer1'))
model.input = spa.Input(buffer1='A')
output2, vocab = model.get_module_output('buffer2')
output3, vocab = model.get_module_output('buffer3')
with model:
p2 = nengo.Probe(output2, 'output', synapse=0.03)
p3 = nengo.Probe(output3, 'output', synapse=0.03)
sim = Simulator(model)
sim.run(0.2)
match = np.dot(sim.data[p2], vocab.parse('A').v)
assert match[199] > 0.9
match = np.dot(sim.data[p3], vocab.parse('~A').v)
assert match[199] > 0.9
def __init__(self):
self.vision = spa.Buffer(dimensions=16)
self.motor = spa.Buffer(dimensions=16)
actions = spa.Actions(
'0.5 --> motor=A',
'dot(vision,CAT) --> motor=B',
'dot(vision*CAT,DOG) --> motor=C',
'2*dot(vision,CAT*0.5) --> motor=D',
'dot(vision,CAT)+0.5-dot(vision,CAT) --> motor=E',
)
self.bg = spa.BasalGanglia(actions)
def input(t):
if t < 0.1:
return '0'
elif t < 0.2:
return 'CAT'
elif t < 0.3:
return 'DOG*~CAT'
else:
return '0'
self.input = spa.Input(vision=input)
import nengo
import nengo.spa as spa
D = 32 # the dimensionality of the vectors
model = spa.SPA()
with model:
model.vision = spa.Buffer(D)
model.speech = spa.Buffer(D)
actions = spa.Actions(
'dot(vision, DOG) --> speech=BARK',
'dot(vision, CAT) --> speech=MEOW',
'dot(vision, RAT) --> speech=SQUEAK',
'dot(vision, COW) --> speech=MOO',
)
model.bg = spa.BasalGanglia(actions)
model.thalamus = spa.Thalamus(model.bg)
#step = nengo.Node(0)
model.food = spa.State(D)
model.now_state = spa.State(D)
model.now_action = spa.State(D)
model.subgoal = spa.State(D)
def update_step():
pass
actions = spa.Actions(
# Start when subgoal is SEARCH_BALL
'dot(subgoal, SEARCH_BALL) -->now_state=START',
'dot(now_state, START) --> subgoal=WAIT, now_state=SEARCH1',
#Search, then turn
'dot(now_state, SEARCH1) --> now_action=TURN',
'dot(now_action, TURN) --> now_state=WAIT, now_action=DONE1',
# Then search again and then move forward
'dot(now_action, DONE1) --> now_state=SEARCH2',
'dot(now_state, SEARCH2) --> now_action=FORWARD',
'dot(now_action, FORWARD) --> now_state=WAIT, now_action=DONE2',
# Then repeat
'dot(now_action, DONE2) --> now_state=SEARCH1',
# Stop when food is found
'dot(food, ISFOOD) --> now_state=STOP',
)
def subgoal_input(t):
if t < 0.1:
return 'SEARCH_BALL'
else:
return '0'
model.bg = spa.BasalGanglia(actions)
