def test_connection(self):
n1=nef.ScalarNode(noise=0)
n2=nef.ScalarNode(noise=0)
n1.connect(n2)
n1.set(0.3)
n1.tick()
n1.tick()
self.assertEqual(n2.value(),0.3)
n1.set(-0.7)
n1.tick()
n1.tick()
self.assertEqual(n2.value(),-0.7)
n1=nef.VectorNode(2,noise=0)
n2=nef.VectorNode(2,noise=0)
n1.connect(n2,weight=0.1)
n1.set([0.3,-0.7])
n1.tick()
n1.tick()
self.assertAlmostEqual(n2.value()[0],0.03)
self.assertAlmostEqual(n2.value()[1],-0.07)
n1=nef.VectorNode(2,noise=0)
n2=nef.VectorNode(2,noise=0)
n1.connect(n2,weight=numpy.array([[0,1],[1,0]]))
n1.set([0.3,-0.7])
n1.tick()
n1.tick()
self.assertEqual(n2.value()[0],-0.7)
self.assertEqual(n2.value()[1],0.3)
def test_connection(self):
modes = ['direct', 'rate']
for m1 in modes:
for m2 in modes:
#print m1,m2
n1 = nef.ScalarNode()
if m1 == 'rate':
n1.configure(neurons=150, activation_noise=0.01)
n2 = nef.ScalarNode()
if m2 == 'rate':
n2.configure(neurons=150, activation_noise=0.01)
n1.connect(n2)
n1.set(0.3)
n1.tick()
n1.tick()
#print 0.3,n2.value()
self.assertAlmostEqual(n2.value(), 0.3, 1)
n1.set(-0.7)
n2.tick()
n2.tick()
#print -0.7,n2.value()
self.assertAlmostEqual(n2.value(), -0.7, 1)
n1 = nef.VectorNode(2, noise=0)
if m1 == 'rate':
n1.configure(neurons=300,
activation_noise=0.01,
basis_style='Aligned')
n2 = nef.VectorNode(2, noise=0, min=-0.5, max=0.5)
if m2 == 'rate':
n2.configure(neurons=300,
activation_noise=0.01,
basis_style='Aligned')
n1.connect(n2, weight=0.5)
n1.set([0.3, -0.7])
n2.tick()
n2.tick()
#print [0.15,-0.35],n2.value()
self.assertAlmostEqual(n2.value()[0], 0.15, 2)
self.assertAlmostEqual(n2.value()[1], -0.35, 2)
n1 = nef.VectorNode(2, noise=0)
if m1 == 'rate':
n1.configure(neurons=300,
activation_noise=0.01,
basis_style='Aligned')
n2 = nef.VectorNode(2, noise=0)
if m2 == 'rate':
n2.configure(neurons=300,
activation_noise=0.01,
basis_style='Aligned')
n1.connect(n2, weight=numpy.array([[0, 1], [1, 0]]))
n1.set([0.3, -0.7])
n1.tick()
n1.tick()
#print [-0.7,0.3],n2.value()
self.assertAlmostEqual(n2.value()[0], -0.7, 1)
self.assertAlmostEqual(n2.value()[1], 0.3, 1)
def test_aligned(self):
n = nef.VectorNode(10)
n.configure(neurons=20, basis_style='Aligned')
for b in n.basis:
self.assertEqual(numpy.abs(b).max(), 1)
self.assertAlmostEqual(numpy.linalg.norm(b), 1, 5)
n = nef.VectorNode(10)
n.configure(neurons=20, basis_style='Sphere')
for b in n.basis:
self.assertNotEqual(numpy.abs(b).max(), 1)
self.assertAlmostEqual(numpy.linalg.norm(b), 1, 5)
def _test_spike_connect_s_d(self):
n=nef.VectorNode(2)
n.configure(neurons=20,apply_noise=False)
n.configure_spikes(pstc=0.06)
n2=nef.VectorNode(2)
n.set([-0.9,0.2])
n.connect(n2)
N=2000
v=0
for i in range(N):
n.tick()
v+=n.value()
#if i%100==99: print (i+1),v/(i+1),n2.value()
self.assertAlmostEqual(v[0]/N,n2.value()[0],1)
self.assertAlmostEqual(v[1]/N,n2.value()[1],1)
def _test_spike_represent(self):
n=nef.VectorNode(2)
n.configure(neurons=20,apply_noise=False)
n.configure_spikes()
n2=nef.VectorNode(2)
n2.configure(neurons=20,apply_noise=False)
n.set([-0.1,0.8])
n2.set([-0.1,0.8])
v=0
for i in range(1000):
n.tick()
n2.tick()
v+=n.value()
#if i%100==99: print (i+1),v/(i+1),n2.value()
self.assertAlmostEqual(v[0]/1000,n2.value()[0],1)
self.assertAlmostEqual(v[1]/1000,n2.value()[1],1)
def test_highdim(self):
D = 50
N = 100
n1 = nef.VectorNode(D)
n1.configure(neurons=N, code='n1', use_hd=True, apply_noise=False)
n2 = nef.VectorNode(D)
n2.configure(neurons=N, code='n2', use_hd=False, apply_noise=False)
for x in [[0, 0, 0], [0, 0, 1], [0.5, 0.5, 0.5]]:
if len(x) < D:
x.extend([0.0] * (D - len(x)))
print(x)
n1.set(x)
n1.tick()
print((1, n1.value()))
n2.set(x)
n2.tick()
print((2, n2.value()))
def test_collection(self):
n=nef.CollectionNode(nef.ScalarNode(),nef.VectorNode(3),nef.ScalarNode(),noise=0)
v1=[0.3,[0.2,0.1,-0.5],-1.0]
n.set(v1)
n.tick()
v2=n.value()
self.assertEqual(v1[0],v2[0])
self.assertEqual(v1[1][0],v2[1][0])
self.assertEqual(v1[1][1],v2[1][1])
self.assertEqual(v1[1][2],v2[1][2])
self.assertEqual(v1[2],v2[2])
def test_representation(self):
n1 = nef.ScalarNode()
n1.configure(neurons=150, activation_noise=0.01)
n1.set(0.3)
n1.tick()
self.assertAlmostEqual(n1.value(), 0.3, 2)
n2 = nef.VectorNode(2)
n2.configure(neurons=200, activation_noise=0)
n2.set([0.3, -0.5])
n2.tick()
self.assertAlmostEqual(n2.value()[0], 0.3, 1)
self.assertAlmostEqual(n2.value()[1], -0.5, 1)
def test_add(self):
samples = None
a = nef.ScalarNode()
b = nef.ScalarNode()
c = nef.ScalarNode()
a.configure(neurons=150,
activation_noise=0.1,
apply_noise=False,
sample_count=samples)
b.configure(neurons=150,
activation_noise=0.1,
apply_noise=False,
sample_count=samples)
c.configure(neurons=150,
activation_noise=0.1,
apply_noise=False,
sample_count=samples)
a.connect(c)
b.connect(c)
ab = nef.VectorNode(2)
a.connect(ab, weight=numpy.array([1, 0]))
b.connect(ab, weight=numpy.array([0, 1]))
ab.configure(neurons=300,
activation_noise=0.1,
apply_noise=False,
sample_count=samples)
c2 = nef.ScalarNode()
c2.configure(neurons=150,
activation_noise=0.1,
apply_noise=False,
sample_count=samples)
ab.connect(c2, func=lambda x: x[0] + x[1])
n = [-0.4, -0.3, -0.1, 0, 0.1, 0.3, 0.4]
for x in n:
for y in n:
a.set(x)
b.set(y)
c.tick()
c.tick()
c.tick()
self.assertAlmostEqual(x + y, c.value(), 1)
self.assertAlmostEqual(x + y, c2.value(), 1)
D1 = 5
D2 = 5
D3 = 5
pstc = 0.01
dt = 0.001
A = [nef.ScalarNode() for i in range(D1 * D2)]
B = [nef.ScalarNode() for i in range(D2 * D3)]
for n in A + B:
n.configure(neurons=50)
n.configure_spikes(pstc=pstc, dt=dt)
# the C matrix holds the intermediate product calculations
# need to compute D1*D2*D3 products to multiply 2 matrices together
C = [nef.VectorNode(2) for i in range(D1 * D2 * D3)]
for n in C:
n.configure(neurons=200)
n.configure_spikes(pstc=pstc, dt=dt)
# determine the transformation matrices to get the correct pairwise
# products computed. This looks a bit like black magic but if
# you manually try multiplying two matrices together, you can see
# the underlying pattern. Basically, we need to build up D1*D2*D3
# pairs of numbers in C to compute the product of. If i,j,k are the
# indexes into the D1*D2*D3 products, we want to compute the product
# of element (i,j) in A with the element (j,k) in B. The index in
# A of (i,j) is j+i*D2 and the index in B of (j,k) is k+j*D3.
for i in range(D1):
for j in range(D2):
for k in range(D3):