def test_affine_transform(self):
X = dot(randn(5, 5), randn(5, 1000)) + randn(5, 1)
Y = dot(randn(2, 2), randn(2, 1000)) + dot(randn(2, 5), X)
meanIn = randn(5, 1)
preIn = randn(5, 5)
pre = AffineTransform(meanIn, preIn, Y.shape[0])
self.assertLess(max(abs(pre(X) - dot(preIn, X - meanIn))), 1e-10)
# test inverse
Xp, Yp = pre(X, Y)
Xr, Yr = pre.inverse(Xp, Yp)
self.assertLess(max(abs(Xr - X)), 1e-10)
self.assertLess(max(abs(Yr - Y)), 1e-10)
# reference counts should not change
Xrc = sys.getrefcount(X)
Yrc = sys.getrefcount(Y)
for i in range(10):
pre(X, Y)
self.assertEqual(sys.getrefcount(X), Xrc)
self.assertEqual(sys.getrefcount(Y), Yrc)
def test_affine_transform(self): X = dot(randn(5, 5), randn(5, 1000)) + randn(5, 1) Y = dot(randn(2, 2), randn(2, 1000)) + dot(randn(2, 5), X) meanIn = randn(5, 1) preIn = randn(5, 5) pre = AffineTransform(meanIn, preIn, Y.shape[0]) self.assertLess(max(abs(pre(X) - dot(preIn, X - meanIn))), 1e-10) # test inverse Xp, Yp = pre(X, Y) Xr, Yr = pre.inverse(Xp, Yp) self.assertLess(max(abs(Xr - X)), 1e-10) self.assertLess(max(abs(Yr - Y)), 1e-10) # reference counts should not change Xrc = sys.getrefcount(X) Yrc = sys.getrefcount(Y) for i in range(10): pre(X, Y) self.assertEqual(sys.getrefcount(X), Xrc) self.assertEqual(sys.getrefcount(Y), Yrc)
def test_affine_transform_pickle(self):
meanIn = randn(5, 1)
preIn = randn(5, 5)
dim_out = 3
pre0 = AffineTransform(meanIn, preIn, dim_out)
tmp_file = mkstemp()[1]
# store transformation
with open(tmp_file, 'w') as handle:
dump({'pre': pre0}, handle)
# load transformation
with open(tmp_file) as handle:
pre1 = load(handle)['pre']
X, Y = randn(5, 100), randn(3, 100)
X0, Y0 = pre0(X, Y)
X1, Y1 = pre1(X, Y)
# make sure linear transformation hasn't changed
self.assertLess(max(abs(X0 - X1)), 1e-20)
self.assertLess(max(abs(Y0 - Y1)), 1e-20)
def test_sample_spike_train(self):
inputs = array([[0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1],
[0, 1, 0, 1, 0, 1, 0, 1]])
outputs = array([[1, 0, 0, 0, 1, 0, 0, 0]])
glm = GLM(3, LogisticFunction, Bernoulli)
glm.train(inputs, outputs)
# generate a spike train without any stimulus input
spike_train = sample_spike_train(empty([0, 100]), glm, 3)
# test difference to expected spike train
diff = spike_train.ravel()[:10] - [0, 0, 0, 1, 0, 0, 1, 0, 0, 1]
self.assertLess(max(abs(diff)), 1e-8)
# preconditioner which removes first (uninformative) dimension from input
m = zeros([3, 1])
A = array([[0, 1, 0], [0, 0, 1]])
pre = AffineTransform(m, A)
glm = GLM(2, LogisticFunction, Bernoulli)
glm.train(pre(inputs), outputs)
# generate a spike train with preconditioned spike history
spike_train = sample_spike_train(empty([0, 100]), glm, 3, pre)
# test difference to expected spike train
diff = spike_train.ravel()[:10] - [0, 0, 0, 1, 0, 0, 1, 0, 0, 1]
self.assertLess(max(abs(diff)), 1e-8)
def __new__(cls, *args, **kwargs): return AffineTransform( mean_in=vstack([transform.mean_in for transform in args]), pre_in=block_diag(*[transform.pre_in for transform in args]), **kwargs)