def test_sample_prior(self):
isa = ISA(5, 10)
samples = isa.sample_prior(20)
# simple sanity checks
self.assertEqual(samples.shape[0], 10)
self.assertEqual(samples.shape[1], 20)
def test_sample_prior(self): isa = ISA(5, 10) samples = isa.sample_prior(20) # simple sanity checks self.assertEqual(samples.shape[0], 10) self.assertEqual(samples.shape[1], 20)
def test_sample_posterior(self): isa = ISA(2, 3, num_scales=10) isa.A = asarray([[1., 0., 1.], [0., 1., 1.]]) isa.initialize() params = isa.default_parameters() params['gibbs']['verbosity'] = 0 params['gibbs']['num_iter'] = 100 states_post = isa.sample_posterior(isa.sample(1000), params) states_prio = isa.sample_prior(states_post.shape[1]) states_post = states_post.flatten() states_post = states_post[permutation(states_post.size)] states_prio = states_prio.flatten() states_prio = states_prio[permutation(states_prio.size)] # on average, posterior samples should be distributed like prior samples p = ks_2samp(states_post, states_prio)[1] self.assertGreater(p, 0.0001) samples = isa.sample(100) states = isa.sample_posterior(samples, params) # reconstruction should be perfect self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()), 1e-10)
def test_subspaces(self): isa = ISA(2, 4, 2) # simple sanity checks self.assertEqual(isa.subspaces()[0].dim, 2) self.assertEqual(isa.subspaces()[1].dim, 2) self.assertEqual(sys.getrefcount(isa.subspaces()), 1) self.assertEqual(sys.getrefcount(isa.subspaces()[0]), 1)
def test_subspaces(self):
isa = ISA(2, 4, 2)
# simple sanity checks
self.assertEqual(isa.subspaces()[0].dim, 2)
self.assertEqual(isa.subspaces()[1].dim, 2)
self.assertEqual(sys.getrefcount(isa.subspaces()), 1)
self.assertEqual(sys.getrefcount(isa.subspaces()[0]), 1)
def test_sample(self): isa = ISA(3, 4) samples = isa.sample(100) samples_prior = isa.sample_prior(100) # simple sanity checks self.assertEqual(samples.shape[0], isa.num_visibles) self.assertEqual(samples.shape[1], 100) self.assertEqual(samples_prior.shape[0], isa.num_hiddens) self.assertEqual(samples_prior.shape[1], 100)
def test_sample(self):
isa = ISA(3, 4)
samples = isa.sample(100)
samples_prior = isa.sample_prior(100)
# simple sanity checks
self.assertEqual(samples.shape[0], isa.num_visibles)
self.assertEqual(samples.shape[1], 100)
self.assertEqual(samples_prior.shape[0], isa.num_hiddens)
self.assertEqual(samples_prior.shape[1], 100)
def test_nullspace_basis(self):
isa = ISA(2, 5)
B = isa.nullspace_basis()
# simple sanity checks
self.assertTrue(B.shape[0], 3)
self.assertTrue(B.shape[1], 5)
# B should be orthogonal to A and orthonormal
self.assertLess(max(abs(dot(isa.A, B.T).flatten())), 1e-10)
self.assertLess(max(abs((dot(B, B.T) - eye(3)).flatten())), 1e-10)
self.assertEqual(sys.getrefcount(B) - 1, 1)
def test_nullspace_basis(self): isa = ISA(2, 5) B = isa.nullspace_basis() # simple sanity checks self.assertTrue(B.shape[0], 3) self.assertTrue(B.shape[1], 5) # B should be orthogonal to A and orthonormal self.assertLess(max(abs(dot(isa.A, B.T).flatten())), 1e-10) self.assertLess(max(abs((dot(B, B.T) - eye(3)).flatten())), 1e-10) self.assertEqual(sys.getrefcount(B) - 1, 1)
def test_default_parameters(self): isa = ISA(2, 4) params = isa.default_parameters() # simple sanity checks self.assertTrue(isinstance(params, dict)) self.assertEqual(sys.getrefcount(params) - 1, 1) self.assertEqual(sys.getrefcount(params['sgd']) - 1, 1) self.assertEqual(sys.getrefcount(params['lbfgs']) - 1, 1) self.assertEqual(sys.getrefcount(params['mp']) - 1, 1) self.assertEqual(sys.getrefcount(params['gsm']) - 1, 1) self.assertEqual(sys.getrefcount(params['gibbs']) - 1, 1) self.assertEqual(sys.getrefcount(params['ais']) - 1, 1) self.assertEqual(sys.getrefcount(params['merge']) - 1, 1)
def test_default_parameters(self):
isa = ISA(2, 4)
params = isa.default_parameters()
# simple sanity checks
self.assertTrue(isinstance(params, dict))
self.assertEqual(sys.getrefcount(params) - 1, 1)
self.assertEqual(sys.getrefcount(params['sgd']) - 1, 1)
self.assertEqual(sys.getrefcount(params['lbfgs']) - 1, 1)
self.assertEqual(sys.getrefcount(params['mp']) - 1, 1)
self.assertEqual(sys.getrefcount(params['gsm']) - 1, 1)
self.assertEqual(sys.getrefcount(params['gibbs']) - 1, 1)
self.assertEqual(sys.getrefcount(params['ais']) - 1, 1)
self.assertEqual(sys.getrefcount(params['merge']) - 1, 1)
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_prior_energy_gradient(self): isa = ISA(4) samples = isa.sample_prior(100) grad = isa.prior_energy_gradient(samples) # simple sanity checks self.assertEqual(grad.shape[0], samples.shape[0]) self.assertEqual(grad.shape[1], samples.shape[1]) f = lambda x: isa.prior_energy(x.reshape(-1, 1)).flatten() df = lambda x: isa.prior_energy_gradient(x.reshape(-1, 1)).flatten() for i in range(samples.shape[1]): relative_error = check_grad(f, df, samples[:, i]) / sqrt(sum(square(df(samples[:, i])))) # comparison with numerical gradient self.assertLess(relative_error, 0.001)
def test_pickle(self):
isa0 = ISA(4, 16, ssize=3)
isa0.set_hidden_states(randn(16, 100))
tmp_file = mkstemp()[1]
# store model
with open(tmp_file, 'w') as handle:
dump({'isa': isa0}, handle)
# load model
with open(tmp_file) as handle:
isa1 = load(handle)['isa']
# make sure parameters haven't changed
self.assertEqual(isa0.num_visibles, isa1.num_visibles)
self.assertEqual(isa0.num_hiddens, isa1.num_hiddens)
self.assertLess(max(abs(isa0.A - isa1.A)), 1e-20)
self.assertLess(max(abs(isa0.hidden_states() - isa1.hidden_states())), 1e-20)
self.assertLess(max(abs(isa0.subspaces()[1].scales - isa1.subspaces()[1].scales)), 1e-20)
def test_train_lbfgs(self):
isa = ISA(2)
isa.initialize()
isa.A = eye(2)
samples = isa.sample(10000)
# initialize close to original parameters
isa.A = asarray([[cos(0.4), sin(0.4)], [-sin(0.4), cos(0.4)]])
params = isa.default_parameters()
params['training_method'] = 'LBFGS'
params['train_prior'] = False
params['max_iter'] = 1
params['lbfgs']['max_iter'] = 50
isa.train(samples, params)
# L-BFGS should be able to recover the parameters
self.assertLess(sqrt(sum(square(isa.A.flatten() - eye(2).flatten()))),
0.1)
def test_callback(self):
isa = ISA(2)
# callback function
def callback(i, isa_):
callback.count += 1
self.assertTrue(isa == isa_)
callback.count = 0
# set callback function
parameters = {
'verbosity': 0,
'max_iter': 7,
'callback': callback,
'sgd': {'max_iter': 0}
}
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, parameters['max_iter'] + 1)
def callback(i, isa_):
if i == 5:
return False
callback.count += 1
callback.count = 0
parameters['callback'] = callback
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, 5)
# make sure referece counts stay stable
self.assertEqual(sys.getrefcount(isa) - 1, 1)
self.assertEqual(sys.getrefcount(callback) - 1, 2)
def test_sample_posterior_ais(self):
isa = ISA(2, 3, num_scales=10)
isa.A = asarray([[1., 0., 1.], [0., 1., 1.]])
isa.initialize()
params = isa.default_parameters()
params['ais']['verbosity'] = 0
params['ais']['num_iter'] = 100
samples = isa.sample(100)
states, _ = isa.sample_posterior_ais(samples, params)
# reconstruction should be perfect
self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()),
1e-10)
def test_loglikelihood(self):
isa = ISA(7, ssize=3)
samples = isa.sample(100)
energy = isa.prior_energy(dot(inv(isa.A), samples))
loglik = isa.loglikelihood(samples)
# difference between loglik and -energy should be const
self.assertTrue(var(loglik + energy) < 1e-10)
isa = ISA(2, 3)
samples = isa.sample(20)
params = isa.default_parameters()
params['ais']['num_samples'] = 5
params['ais']['num_iter'] = 10
loglik = isa.loglikelihood(samples, params, return_all=True)
# simple sanity checks
self.assertTrue(loglik.shape[0], params['ais']['num_samples'])
self.assertTrue(loglik.shape[1], samples.shape[1])
def test_train_lbfgs(self): isa = ISA(2) isa.initialize() isa.A = eye(2) samples = isa.sample(10000) # initialize close to original parameters isa.A = asarray([[cos(0.4), sin(0.4)], [-sin(0.4), cos(0.4)]]) params = isa.default_parameters() params['training_method'] = 'LBFGS' params['train_prior'] = False params['max_iter'] = 1 params['lbfgs']['max_iter'] = 50 isa.train(samples, params) # L-BFGS should be able to recover the parameters self.assertLess(sqrt(sum(square(isa.A.flatten() - eye(2).flatten()))), 0.1)
def test_initialize(self):
def sqrtmi(mat):
"""
Compute matrix inverse square root.
@type mat: array_like
@param mat: matrix for which to compute inverse square root
"""
# find eigenvectors
eigvals, eigvecs = eig(mat)
# eliminate eigenvectors whose eigenvalues are zero
eigvecs = eigvecs[:, eigvals > 0.]
eigvals = eigvals[eigvals > 0.]
# inverse square root
return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T))
# white data
data = randn(5, 1000)
data = dot(sqrtmi(cov(data)), data)
isa = ISA(5, 10)
isa.initialize(data)
# rows of A should be roughly orthogonal
self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(5)).flatten()),
1e-3)
p = kstest(
isa.sample_prior(100).flatten(),
lambda x: laplace.cdf(x, scale=1. / sqrt(2.)))[1]
# prior marginals should be roughly Laplace
self.assertGreater(p, 0.0001)
# test initialization with larger subspaces
isa = ISA(5, 10, ssize=2)
isa.initialize(data)
def test_train_mp(self):
isa = ISA(5, 10)
params = isa.default_parameters()
params['training_method'] = 'MP'
params['mp']['num_coeff'] = 4
samples = isa.sample(100)
states = isa.matching_pursuit(samples, params)
# simple sanity checks
self.assertEqual(states.shape[1], 100)
self.assertEqual(states.shape[0], 10)
self.assertFalse(any(sum(states > 0., 0) > 4))
# make sure training with MP doesn't throw any errors
isa.train(isa.sample(1011), params)
def test_sample_posterior_ais(self): isa = ISA(2, 3, num_scales=10) isa.A = asarray([[1., 0., 1.], [0., 1., 1.]]) isa.initialize() params = isa.default_parameters() params['ais']['verbosity'] = 0 params['ais']['num_iter'] = 100 samples = isa.sample(100) states, _ = isa.sample_posterior_ais(samples, params) # reconstruction should be perfect self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()), 1e-10)
def test_train_mp(self): isa = ISA(5, 10) params = isa.default_parameters() params['training_method'] = 'MP' params['mp']['num_coeff'] = 4 samples = isa.sample(100) states = isa.matching_pursuit(samples, params) # simple sanity checks self.assertEqual(states.shape[1], 100) self.assertEqual(states.shape[0], 10) self.assertFalse(any(sum(states > 0., 0) > 4)) # make sure training with MP doesn't throw any errors isa.train(isa.sample(1011), params)
def test_loglikelihood(self): isa = ISA(7, ssize=3) samples = isa.sample(100) energy = isa.prior_energy(dot(inv(isa.A), samples)) loglik = isa.loglikelihood(samples) # difference between loglik and -energy should be const self.assertTrue(var(loglik + energy) < 1e-10) isa = ISA(2, 3) samples = isa.sample(20) params = isa.default_parameters() params['ais']['num_samples'] = 5 params['ais']['num_iter'] = 10 loglik = isa.loglikelihood(samples, params, return_all=True) # simple sanity checks self.assertTrue(loglik.shape[0], params['ais']['num_samples']) self.assertTrue(loglik.shape[1], samples.shape[1])
def test_initialize(self): def sqrtmi(mat): """ Compute matrix inverse square root. @type mat: array_like @param mat: matrix for which to compute inverse square root """ # find eigenvectors eigvals, eigvecs = eig(mat) # eliminate eigenvectors whose eigenvalues are zero eigvecs = eigvecs[:, eigvals > 0.] eigvals = eigvals[eigvals > 0.] # inverse square root return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) # white data data = randn(5, 1000) data = dot(sqrtmi(cov(data)), data) isa = ISA(5, 10) isa.initialize(data) # rows of A should be roughly orthogonal self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(5)).flatten()), 1e-3) p = kstest( isa.sample_prior(100).flatten(), lambda x: laplace.cdf(x, scale=1. / sqrt(2.)))[1] # prior marginals should be roughly Laplace self.assertGreater(p, 0.0001) # test initialization with larger subspaces isa = ISA(5, 10, ssize=2) isa.initialize(data)
def test_prior_energy_gradient(self):
isa = ISA(4)
samples = isa.sample_prior(100)
grad = isa.prior_energy_gradient(samples)
# simple sanity checks
self.assertEqual(grad.shape[0], samples.shape[0])
self.assertEqual(grad.shape[1], samples.shape[1])
f = lambda x: isa.prior_energy(x.reshape(-1, 1)).flatten()
df = lambda x: isa.prior_energy_gradient(x.reshape(-1, 1)).flatten()
for i in range(samples.shape[1]):
relative_error = check_grad(f, df, samples[:, i]) / sqrt(
sum(square(df(samples[:, i]))))
# comparison with numerical gradient
self.assertLess(relative_error, 0.001)
def test_callback(self):
isa = ISA(2)
# callback function
def callback(i, isa_):
callback.count += 1
self.assertTrue(isa == isa_)
callback.count = 0
# set callback function
parameters = {
'verbosity': 0,
'max_iter': 7,
'callback': callback,
'sgd': {
'max_iter': 0
}
}
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, parameters['max_iter'] + 1)
def callback(i, isa_):
if i == 5:
return False
callback.count += 1
callback.count = 0
parameters['callback'] = callback
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, 5)
# make sure referece counts stay stable
self.assertEqual(sys.getrefcount(isa) - 1, 1)
self.assertEqual(sys.getrefcount(callback) - 1, 2)
def test_pickle(self):
isa0 = ISA(4, 16, ssize=3)
isa0.set_hidden_states(randn(16, 100))
tmp_file = mkstemp()[1]
# store model
with open(tmp_file, 'w') as handle:
dump({'isa': isa0}, handle)
# load model
with open(tmp_file) as handle:
isa1 = load(handle)['isa']
# make sure parameters haven't changed
self.assertEqual(isa0.num_visibles, isa1.num_visibles)
self.assertEqual(isa0.num_hiddens, isa1.num_hiddens)
self.assertLess(max(abs(isa0.A - isa1.A)), 1e-20)
self.assertLess(max(abs(isa0.hidden_states() - isa1.hidden_states())),
1e-20)
self.assertLess(
max(abs(isa0.subspaces()[1].scales - isa1.subspaces()[1].scales)),
1e-20)
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_orthogonalize(self):
isa = ISA(4, 8)
isa.orthogonalize()
self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(4)).flatten()),
1e-3)
def test_merge(self): isa1 = ISA(5, ssize=2) isa2 = ISA(5) isa1.initialize() isa1.orthogonalize() isa2.initialize() isa2.A = isa1.A params = isa2.default_parameters() params['train_basis'] = False params['merge_subspaces'] = True params['merge']['verbosity'] = 0 isa2.train(isa1.sample(10000), params) ssizes1 = [gsm.dim for gsm in isa1.subspaces()] ssizes2 = [gsm.dim for gsm in isa2.subspaces()] # algorithm should be able to recover subspace sizes self.assertTrue(all(sort(ssizes1) == sort(ssizes2)))
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)
def test_sample_posterior(self):
isa = ISA(2, 3, num_scales=10)
isa.A = asarray([[1., 0., 1.], [0., 1., 1.]])
isa.initialize()
params = isa.default_parameters()
params['gibbs']['verbosity'] = 0
params['gibbs']['num_iter'] = 100
states_post = isa.sample_posterior(isa.sample(1000), params)
states_prio = isa.sample_prior(states_post.shape[1])
states_post = states_post.flatten()
states_post = states_post[permutation(states_post.size)]
states_prio = states_prio.flatten()
states_prio = states_prio[permutation(states_prio.size)]
# on average, posterior samples should be distributed like prior samples
p = ks_2samp(states_post, states_prio)[1]
self.assertGreater(p, 0.0001)
samples = isa.sample(100)
states = isa.sample_posterior(samples, params)
# reconstruction should be perfect
self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()),
1e-10)
def __init__(self):
self.cpu = CPU()
self.memory = Memory()
self.isa = ISA(self)
def test_merge(self):
isa1 = ISA(5, ssize=2)
isa2 = ISA(5)
isa1.initialize()
isa1.orthogonalize()
isa2.initialize()
isa2.A = isa1.A
params = isa2.default_parameters()
params['train_basis'] = False
params['merge_subspaces'] = True
params['merge']['verbosity'] = 0
isa2.train(isa1.sample(10000), params)
ssizes1 = [gsm.dim for gsm in isa1.subspaces()]
ssizes2 = [gsm.dim for gsm in isa2.subspaces()]
# algorithm should be able to recover subspace sizes
self.assertTrue(all(sort(ssizes1) == sort(ssizes2)))
def test_orthogonalize(self): isa = ISA(4, 8) isa.orthogonalize() self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(4)).flatten()), 1e-3)
def test_train(self): # make sure train() doesn't throw any errors isa = ISA(2) params = isa.default_parameters() params['verbosity'] = 0 params['max_iter'] = 2 params['training_method'] = 'SGD' params['sgd']['max_iter'] = 1 params['sgd']['batch_size'] = 57 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params) isa = ISA(4, ssize=2) isa.initialize(randn(4, 1000)) isa.train(randn(4, 1000), params) isa = ISA(2, 3) params['gibbs']['ini_iter'] = 2 params['gibbs']['num_iter'] = 2 params['verbosity'] = 0 params['gibbs']['verbosity'] = 0 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params)
* IN THE SOFTWARE.
*/
#ifndef _${guard}_
#define _${guard}_
#include "${isa}"
#endif /* _${guard}_ */
"""
xml = sys.argv[1]
glue_h = sys.argv[2]
dst_c = sys.argv[3]
dst_h = sys.argv[4]
isa = ISA(xml)
with open(glue_h, 'w') as f:
guard = os.path.basename(glue_h).upper().replace("-",
"_").replace(".", "_")
f.write(Template(glue).render(guard=guard, isa=os.path.basename(dst_h)))
with open(dst_c, 'w') as f:
f.write(Template(template).render(isa=isa))
with open(dst_h, 'w') as f:
guard = os.path.basename(dst_h).upper().replace("-", "_").replace(".", "_")
f.write(Template(header).render(isa=isa, guard=guard))
def test_train(self):
# make sure train() doesn't throw any errors
isa = ISA(2)
params = isa.default_parameters()
params['verbosity'] = 0
params['max_iter'] = 2
params['training_method'] = 'SGD'
params['sgd']['max_iter'] = 1
params['sgd']['batch_size'] = 57
isa.initialize(randn(2, 1000))
isa.train(randn(2, 1000), params)
isa = ISA(4, ssize=2)
isa.initialize(randn(4, 1000))
isa.train(randn(4, 1000), params)
isa = ISA(2, 3)
params['gibbs']['ini_iter'] = 2
params['gibbs']['num_iter'] = 2
params['verbosity'] = 0
params['gibbs']['verbosity'] = 0
isa.initialize(randn(2, 1000))
isa.train(randn(2, 1000), params)