def test_sample_scales(self):
isa = ISA(2, 5, num_scales=4)
# get a copy of subspaces
subspaces = isa.subspaces()
# replace scales
for gsm in subspaces:
gsm.scales = asarray([1., 2., 3., 4.])
isa.set_subspaces(subspaces)
samples = isa.sample_prior(100000)
scales = isa.sample_scales(samples)
# simple sanity checks
self.assertEqual(scales.shape[0], isa.num_hiddens)
self.assertEqual(scales.shape[1], samples.shape[1])
priors = mean(
abs(scales.flatten() - asarray([[1., 2., 3., 4.]]).T) < 0.5, 1)
# prior probabilities of scales should be equal and sum up to one
self.assertLess(max(abs(priors - 1. / subspaces[0].num_scales)), 0.01)
self.assertLess(abs(sum(priors) - 1.), 1e-10)
def test_sample_scales(self): isa = ISA(2, 5, num_scales=4) # get a copy of subspaces subspaces = isa.subspaces() # replace scales for gsm in subspaces: gsm.scales = asarray([1., 2., 3., 4.]) isa.set_subspaces(subspaces) samples = isa.sample_prior(100000) scales = isa.sample_scales(samples) # simple sanity checks self.assertEqual(scales.shape[0], isa.num_hiddens) self.assertEqual(scales.shape[1], samples.shape[1]) priors = mean(abs(scales.flatten() - asarray([[1., 2., 3., 4.]]).T) < 0.5, 1) # prior probabilities of scales should be equal and sum up to one self.assertLess(max(abs(priors - 1. / subspaces[0].num_scales)), 0.01) self.assertLess(abs(sum(priors) - 1.), 1e-10)
def test_evaluate(self): isa1 = ISA(2) isa1.A = eye(2) subspaces = isa1.subspaces() for gsm in subspaces: gsm.scales = ones(gsm.num_scales) isa1.set_subspaces(subspaces) # equivalent overcomplete model isa2 = ISA(2, 4) A = copy(isa2.A) A[:, :2] = isa1.A / sqrt(2.) A[:, 2:] = isa1.A / sqrt(2.) isa2.A = A subspaces = isa2.subspaces() for gsm in subspaces: gsm.scales = ones(gsm.num_scales) isa2.set_subspaces(subspaces) data = isa1.sample(100) # the results should not depend on the parameters ll1 = isa1.evaluate(data) ll2 = isa2.evaluate(data) self.assertLess(abs(ll1 - ll2), 1e-5) isa1 = ISA(2) isa1.initialize() # equivalent overcomplete model isa2 = ISA(2, 4) isa2.set_subspaces(isa1.subspaces() * 2) A = isa2.basis() A[:, :2] = isa1.basis() A[:, 2:] = 0. isa2.set_basis(A) data = isa1.sample(100) params = isa2.default_parameters() params['ais']['num_iter'] = 100 params['ais']['num_samples'] = 100 ll1 = isa1.evaluate(data) ll2 = isa2.evaluate(data, params) self.assertLess(abs(ll1 - ll2), 0.1)
def test_evaluate(self):
isa1 = ISA(2)
isa1.A = eye(2)
subspaces = isa1.subspaces()
for gsm in subspaces:
gsm.scales = ones(gsm.num_scales)
isa1.set_subspaces(subspaces)
# equivalent overcomplete model
isa2 = ISA(2, 4)
A = copy(isa2.A)
A[:, :2] = isa1.A / sqrt(2.)
A[:, 2:] = isa1.A / sqrt(2.)
isa2.A = A
subspaces = isa2.subspaces()
for gsm in subspaces:
gsm.scales = ones(gsm.num_scales)
isa2.set_subspaces(subspaces)
data = isa1.sample(100)
# the results should not depend on the parameters
ll1 = isa1.evaluate(data)
ll2 = isa2.evaluate(data)
self.assertLess(abs(ll1 - ll2), 1e-5)
isa1 = ISA(2)
isa1.initialize()
# equivalent overcomplete model
isa2 = ISA(2, 4)
isa2.set_subspaces(isa1.subspaces() * 2)
A = isa2.basis()
A[:, :2] = isa1.basis()
A[:, 2:] = 0.
isa2.set_basis(A)
data = isa1.sample(100)
params = isa2.default_parameters()
params['ais']['num_iter'] = 100
params['ais']['num_samples'] = 100
ll1 = isa1.evaluate(data)
ll2 = isa2.evaluate(data, params)
self.assertLess(abs(ll1 - ll2), 0.1)