def test_nonlinearity_to_nonlinearity(self):
N = 30
m = nengo.Model('test_nonlinearity_to_nonlinearity', seed=123)
a = m.make_ensemble('A', nengo.LIF(N), dimensions=1)
m.make_node('in', output=np.sin)
m.make_node('inh', piecewise({0:0,2.5:1}))
m.make_node('ideal', piecewise({0:np.sin,2.5:0}))
m.connect('in', 'A')
con = m.connect('inh', a.neurons, transform=[[-2.5]]*N)
m.probe('in')
m.probe('A', filter=0.1)
m.probe('inh')
m.probe('ideal')
sim = m.simulator(dt=0.001, sim_class=self.Simulator)
sim.run(5.0)
with Plotter(self.Simulator) as plt:
t = sim.data(m.t)
plt.plot(t, sim.data('in'), label='Input')
plt.plot(t, sim.data('A'), label='Neuron approx, filter=0.1')
plt.plot(t, sim.data('inh'), label='Inhib signal')
plt.plot(t, sim.data('ideal'), label='Ideal output')
plt.legend(loc=0, prop={'size':10})
plt.savefig('test_tononlinearity_connection.test_nonlinearity_to_nonlinearity.pdf')
plt.close()
self.assertTrue(np.allclose(sim.data('A')[-10:], sim.data('ideal')[-10:],
atol=.1, rtol=.01))
def test_multiplication(self):
model = nengo.Model('multiplication')
with declarative_syntax(model):
ens.ensemble('A', nengo.LIF(100), dimensions=1, radius=10)
ens.ensemble('B', nengo.LIF(100), dimensions=1, radius=10)
ens.ensemble('Combined', nengo.LIF(100), dimensions=2, radius=15)
ens.ensemble('D', nengo.LIF(100), dimensions=1, radius=20)
ens.encoders(
'Combined',
np.tile([[1, 1], [-1, 1], [1, -1], [-1, -1]],
(ens.n_neurons('Combined') / 4, 1)))
ens.node('Input A', piecewise({0: 0, 2.5: 10, 4: -10}))
ens.node('Input B', piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))
conn.connect('Input A', 'A')
conn.connect('Input B', 'B')
conn.connect('A', 'Combined', transform=[[1], [0]])
conn.connect('B', 'Combined', transform=[[0], [1]])
conn.connect('Combined', 'D', function=lambda x: x[0] * x[1])
for name in 'Input A', 'Input B':
probe(name)
for name in 'A', 'B', 'Combined', 'D':
probe(name, filter=0.01)
sim = model.simulator()
sim.run(5)
import matplotlib.pyplot as plt
# Plot the input signals and decoded ensemble values
correct_ans = nengo.helpers.piecewise({
0: 0,
1.5: 0,
2.5: 20,
3: 0,
4: 0,
4.5: -20
})
t = sim.data(model.t)
plt.plot(t, sim.data('A'), label="Decoded A")
plt.plot(t, sim.data('B'), label="Decoded B")
plt.plot(t, sim.data('D'), label="Decoded D")
out = [0] * t.shape[0]
for i in np.arange(t.shape[0]):
out[i] = correct_ans(t[i])
plt.plot(t, out)
plt.legend()
plt.ylim(-25, 25)
def test_decoder_to_nonlinearity(self):
N = 30
m = nengo.Model('test_decoder_to_nonlinearity', seed=123)
a = m.make_ensemble('A', nengo.LIF(N), dimensions=1)
b = m.make_ensemble('B', nengo.LIF(N), dimensions=1)
m.make_node('in', output=np.sin)
m.make_node('inh', piecewise({0: 0, 2.5: 1}))
m.make_node('ideal', piecewise({0: np.sin, 2.5: 0}))
m.connect('in', 'A')
m.connect('inh', 'B')
con = m.connect('B', a.neurons, transform=[[-2.5]] * N)
m.probe('in')
m.probe('A', filter=0.1)
m.probe('B', filter=0.1)
m.probe('inh')
m.probe('ideal')
sim = m.simulator(dt=0.001, sim_class=self.Simulator)
sim.run(5.0)
with Plotter(self.Simulator) as plt:
t = sim.data(m.t)
plt.plot(t, sim.data('in'), label='Input')
plt.plot(t, sim.data('A'), label='Neuron approx, pstc=0.1')
plt.plot(t,
sim.data('B'),
label='Neuron approx of inhib sig, pstc=0.1')
plt.plot(t, sim.data('inh'), label='Inhib signal')
plt.plot(t, sim.data('ideal'), label='Ideal output')
plt.legend(loc=0, prop={'size': 10})
plt.savefig(
'test_tononlinearity_connection.test_decoder_to_nonlinearity.pdf'
)
plt.close()
self.assertTrue(
np.allclose(sim.data('A')[-10:],
sim.data('ideal')[-10:],
atol=.1,
rtol=.01))
self.assertTrue(
np.allclose(sim.data('B')[-10:],
sim.data('inh')[-10:],
atol=.1,
rtol=.01))
def test_node_to_neurons(Simulator, nl_nodirect):
name = 'node_to_neurons'
N = 30
m = nengo.Model(name, seed=123)
a = nengo.Ensemble(nl_nodirect(N), dimensions=1)
inn = nengo.Node(output=np.sin)
inh = nengo.Node(piecewise({0: 0, 2.5: 1}))
nengo.Connection(inn, a)
nengo.Connection(inh, a.neurons, transform=[[-2.5]]*N)
inn_p = nengo.Probe(inn, 'output')
a_p = nengo.Probe(a, 'decoded_output', filter=0.1)
inh_p = nengo.Probe(inh, 'output')
sim = Simulator(m)
sim.run(5.0)
t = sim.trange()
ideal = np.sin(t)
ideal[t >= 2.5] = 0
with Plotter(Simulator, nl_nodirect) as plt:
plt.plot(t, sim.data(inn_p), label='Input')
plt.plot(t, sim.data(a_p), label='Neuron approx, filter=0.1')
plt.plot(t, sim.data(inh_p), label='Inhib signal')
plt.plot(t, ideal, label='Ideal output')
plt.legend(loc=0, prop={'size': 10})
plt.savefig('test_connection.test_' + name + '.pdf')
plt.close()
assert np.allclose(sim.data(a_p)[-10:], 0, atol=.1, rtol=.01)
def test_multiplication(self):
model = nengo.Model('multiplication')
with declarative_syntax(model):
ens.ensemble('A', nengo.LIF(100), dimensions=1, radius=10)
ens.ensemble('B', nengo.LIF(100), dimensions=1, radius=10)
ens.ensemble('Combined', nengo.LIF(100), dimensions=2, radius=15)
ens.ensemble('D', nengo.LIF(100), dimensions=1, radius=20)
ens.encoders('Combined',
np.tile([[1,1],[-1,1],[1,-1],[-1,-1]],
(ens.n_neurons('Combined')/4, 1)))
ens.node('Input A', piecewise({0:0, 2.5:10, 4:-10}))
ens.node('Input B', piecewise({0:10, 1.5:2, 3:0, 4.5:2}))
conn.connect('Input A', 'A')
conn.connect('Input B', 'B')
conn.connect('A','Combined', transform=[[1], [0]])
conn.connect('B','Combined', transform=[[0], [1]])
conn.connect('Combined', 'D', function=lambda x: x[0] * x[1])
for name in 'Input A', 'Input B':
probe(name)
for name in 'A', 'B', 'Combined', 'D':
probe(name, filter=0.01)
sim = model.simulator()
sim.run(5)
import matplotlib.pyplot as plt
# Plot the input signals and decoded ensemble values
correct_ans = nengo.helpers.piecewise({0:0, 1.5:0, 2.5:20, 3:0, 4:0, 4.5:-20})
t = sim.data(model.t)
plt.plot(t, sim.data('A'), label="Decoded A")
plt.plot(t, sim.data('B'), label="Decoded B")
plt.plot(t, sim.data('D'), label="Decoded D")
out = [0] * t.shape[0]
for i in np.arange(t.shape[0]):
out[i] = correct_ans(t[i])
plt.plot(t, out)
plt.legend()
plt.ylim(-25,25);
def test_function(self):
f = piecewise({0: np.sin, 0.5: np.cos})
self.assertEqual(f(0), [np.sin(0)])
self.assertEqual(f(0.25), [np.sin(0.25)])
self.assertEqual(f(0.4999), [np.sin(0.4999)])
self.assertEqual(f(0.5), [np.cos(0.5)])
self.assertEqual(f(0.75), [np.cos(0.75)])
self.assertEqual(f(1.0), [np.cos(1.0)])
def test_function(self):
f = piecewise({0: np.sin, 0.5: np.cos})
self.assertEqual(f(0), [np.sin(0)])
self.assertEqual(f(0.25), [np.sin(0.25)])
self.assertEqual(f(0.4999), [np.sin(0.4999)])
self.assertEqual(f(0.5), [np.cos(0.5)])
self.assertEqual(f(0.75), [np.cos(0.75)])
self.assertEqual(f(1.0), [np.cos(1.0)])
def test_function():
f = piecewise({0: np.sin, 0.5: np.cos})
assert np.allclose(f(0), [np.sin(0)])
assert np.allclose(f(0.25), [np.sin(0.25)])
assert np.allclose(f(0.4999), [np.sin(0.4999)])
assert np.allclose(f(0.5), [np.cos(0.5)])
assert np.allclose(f(0.75), [np.cos(0.75)])
assert np.allclose(f(1.0), [np.cos(1.0)])
def test_lists():
f = piecewise({0.5: [1, 0], 1.0: [0, 1]})
assert np.allclose(f(-10), [0, 0])
assert np.allclose(f(0), [0, 0])
assert np.allclose(f(0.25), [0, 0])
assert np.allclose(f(0.5), [1, 0])
assert np.allclose(f(0.75), [1, 0])
assert np.allclose(f(1.0), [0, 1])
assert np.allclose(f(1.5), [0, 1])
assert np.allclose(f(100), [0, 1])
def test_basic(self):
f = piecewise({0.5: 1, 1.0: 0})
self.assertEqual(f(-10), [0])
self.assertEqual(f(0), [0])
self.assertEqual(f(0.25), [0])
self.assertEqual(f(0.5), [1])
self.assertEqual(f(0.75), [1])
self.assertEqual(f(1.0), [0])
self.assertEqual(f(1.5), [0])
self.assertEqual(f(100), [0])
def test_lists(self):
f = piecewise({0.5: [1, 0], 1.0: [0, 1]})
self.assertEqual(f(-10), [0, 0])
self.assertEqual(f(0), [0, 0])
self.assertEqual(f(0.25), [0, 0])
self.assertEqual(f(0.5), [1, 0])
self.assertEqual(f(0.75), [1, 0])
self.assertEqual(f(1.0), [0, 1])
self.assertEqual(f(1.5), [0, 1])
self.assertEqual(f(100), [0, 1])
def test_basic(self):
f = piecewise({0.5: 1, 1.0: 0})
self.assertEqual(f(-10), [0])
self.assertEqual(f(0), [0])
self.assertEqual(f(0.25), [0])
self.assertEqual(f(0.5), [1])
self.assertEqual(f(0.75), [1])
self.assertEqual(f(1.0), [0])
self.assertEqual(f(1.5), [0])
self.assertEqual(f(100), [0])
def test_basic():
f = piecewise({0.5: 1, 1.0: 0})
assert np.allclose(f(-10), [0])
assert np.allclose(f(0), [0])
assert np.allclose(f(0.25), [0])
assert np.allclose(f(0.5), [1])
assert np.allclose(f(0.75), [1])
assert np.allclose(f(1.0), [0])
assert np.allclose(f(1.5), [0])
assert np.allclose(f(100), [0])
def test_lists(self):
f = piecewise({0.5: [1, 0], 1.0: [0, 1]})
self.assertEqual(f(-10), [0, 0])
self.assertEqual(f(0), [0, 0])
self.assertEqual(f(0.25), [0, 0])
self.assertEqual(f(0.5), [1, 0])
self.assertEqual(f(0.75), [1, 0])
self.assertEqual(f(1.0), [0, 1])
self.assertEqual(f(1.5), [0, 1])
self.assertEqual(f(100), [0, 1])
def test_function_list(self):
def func1(t):
return t, t**2, t**3
def func2(t):
return t**4, t**5, t**6
f = piecewise({0:func1, 0.5: func2})
self.assertEqual(f(0), func1(0))
self.assertEqual(f(0.25), func1(0.25))
self.assertEqual(f(0.4999), func1(0.4999))
self.assertEqual(f(0.5), func2(0.5))
self.assertEqual(f(0.75), func2(0.75))
self.assertEqual(f(1.0), func2(1.0))
def test_function_list(self):
def func1(t):
return t, t**2, t**3
def func2(t):
return t**4, t**5, t**6
f = piecewise({0: func1, 0.5: func2})
self.assertEqual(f(0), func1(0))
self.assertEqual(f(0.25), func1(0.25))
self.assertEqual(f(0.4999), func1(0.4999))
self.assertEqual(f(0.5), func2(0.5))
self.assertEqual(f(0.75), func2(0.75))
self.assertEqual(f(1.0), func2(1.0))
def test_function_list():
def func1(t):
return t, t**2, t**3
def func2(t):
return t**4, t**5, t**6
f = piecewise({0: func1, 0.5: func2})
assert np.allclose(f(0), func1(0))
assert np.allclose(f(0.25), func1(0.25))
assert np.allclose(f(0.4999), func1(0.4999))
assert np.allclose(f(0.5), func2(0.5))
assert np.allclose(f(0.75), func2(0.75))
assert np.allclose(f(1.0), func2(1.0))
def test_invalid_length(self):
with self.assertRaises(Exception):
f = piecewise({0.5: [1, 0], 1.0: [1, 0, 0]})
def test_invalid_function_length(self):
with self.assertRaises(Exception):
f = piecewise({0.5: 0, 1.0: lambda t: [t, t ** 2]})
def test_invalid_length(self):
with self.assertRaises(Exception):
f = piecewise({0.5: [1, 0], 1.0: [1, 0, 0]})
def test_invalid_key(self):
with self.assertRaises(TypeError):
f = piecewise({0.5: 1, 1: 0, 'a': 0.2})
def test_invalid_function_length(self):
with self.assertRaises(Exception):
f = piecewise({0.5: 0, 1.0: lambda t: [t, t**2]})
def test_invalid_key():
with pytest.raises(TypeError):
f = piecewise({0.5: 1, 1: 0, 'a': 0.2})
assert f
def test_invalid_key(self):
with self.assertRaises(TypeError):
f = piecewise({0.5: 1, 1: 0, 'a': 0.2})
def test_invalid_length():
with pytest.raises(Exception):
f = piecewise({0.5: [1, 0], 1.0: [1, 0, 0]})
assert f
def test_invalid_function_length():
with pytest.raises(Exception):
f = piecewise({0.5: 0, 1.0: lambda t: [t, t ** 2]})
assert f