def test_1():
"Test the getters and setters, which transfer data to/from the GPU"
t1 = np.random.randn(10, 2).astype(np.float32)
n_features = 2
for n_states in [3, 4]:
hmm = GaussianHMMCUDAImpl(n_states, n_features)
hmm._sequences = [t1]
means = np.random.randn(n_states, n_features).astype(np.float32)
hmm.means_ = means
yield lambda: np.testing.assert_array_almost_equal(hmm.means_, means)
vars = np.random.rand(n_states, n_features).astype(np.float32)
hmm.vars_ = vars
yield lambda: np.testing.assert_array_almost_equal(hmm.vars_, vars)
transmat = np.random.rand(n_states, n_states).astype(np.float32)
hmm.transmat_ = transmat
yield lambda: np.testing.assert_array_almost_equal(
hmm.transmat_, transmat)
startprob = np.random.rand(n_states).astype(np.float32)
hmm.startprob_ = startprob
yield lambda: np.testing.assert_array_almost_equal(
hmm.startprob_, startprob)
def test_1():
"Test the getters and setters, which transfer data to/from the GPU"
t1 = np.random.randn(10, 2).astype(np.float32)
n_features = 2
for n_states in [3, 4]:
hmm = GaussianHMMCUDAImpl(n_states, n_features)
hmm._sequences = [t1]
means = np.random.randn(n_states, n_features).astype(np.float32)
hmm.means_ = means
yield lambda: np.testing.assert_array_almost_equal(hmm.means_, means)
vars = np.random.rand(n_states, n_features).astype(np.float32)
hmm.vars_ = vars
yield lambda: np.testing.assert_array_almost_equal(hmm.vars_, vars)
transmat = np.random.rand(n_states, n_states).astype(np.float32)
hmm.transmat_ = transmat
yield lambda: np.testing.assert_array_almost_equal(hmm.transmat_, transmat)
startprob = np.random.rand(n_states).astype(np.float32)
hmm.startprob_ = startprob
yield lambda: np.testing.assert_array_almost_equal(hmm.startprob_, startprob)
def test_2():
np.random.seed(42)
n_features = 32
length = 20
#for n_states in [3, 4, 5, 7, 8, 9, 15, 16, 17, 31, 32]:
for n_states in [8]:
t1 = np.random.randn(length, n_features)
means = np.random.randn(n_states, n_features)
vars = np.random.rand(n_states, n_features)
transmat = np.random.rand(n_states, n_states)
transmat = transmat / np.sum(transmat, axis=1)[:, None]
startprob = np.random.rand(n_states)
startprob = startprob / np.sum(startprob)
cuhmm = GaussianHMMCUDAImpl(n_states, n_features)
cuhmm._sequences = [t1]
pyhmm = GaussianHMM(n_components=n_states, init_params='', params='', covariance_type='diag')
cuhmm.means_ = means
cuhmm.vars_ = vars
cuhmm.transmat_ = transmat
cuhmm.startprob_ = startprob
logprob, custats = cuhmm.do_estep()
pyhmm.means_ = means
pyhmm.covars_ = vars
pyhmm.transmat_ = transmat
pyhmm.startprob_ = startprob
pyhmm._initialize_sufficient_statistics()
framelogprob = pyhmm._compute_log_likelihood(t1)
cuframelogprob = cuhmm._get_framelogprob()
yield lambda: np.testing.assert_array_almost_equal(framelogprob, cuframelogprob, decimal=3)
fwdlattice = pyhmm._do_forward_pass(framelogprob)[1]
cufwdlattice = cuhmm._get_fwdlattice()
yield lambda: np.testing.assert_array_almost_equal(fwdlattice, cufwdlattice, decimal=3)
bwdlattice = pyhmm._do_backward_pass(framelogprob)
cubwdlattice = cuhmm._get_bwdlattice()
yield lambda: np.testing.assert_array_almost_equal(bwdlattice, cubwdlattice, decimal=3)
gamma = fwdlattice + bwdlattice
posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T
cuposteriors = cuhmm._get_posteriors()
yield lambda: np.testing.assert_array_almost_equal(posteriors, cuposteriors, decimal=3)
stats = pyhmm._initialize_sufficient_statistics()
pyhmm._accumulate_sufficient_statistics(
stats, t1, framelogprob, posteriors, fwdlattice,
bwdlattice, 'stmc')
print 'ref transcounts'
print transitioncounts(cufwdlattice, cubwdlattice, cuframelogprob, np.log(transmat))
print 'cutranscounts'
print custats['trans']
yield lambda: np.testing.assert_array_almost_equal(stats['trans'], custats['trans'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post'], custats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'], custats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs**2'], custats['obs**2'], decimal=3)
def test_2():
np.random.seed(42)
n_features = 32
length = 20
#for n_states in [3, 4, 5, 7, 8, 9, 15, 16, 17, 31, 32]:
for n_states in [8]:
t1 = np.random.randn(length, n_features)
means = np.random.randn(n_states, n_features)
vars = np.random.rand(n_states, n_features)
transmat = np.random.rand(n_states, n_states)
transmat = transmat / np.sum(transmat, axis=1)[:, None]
startprob = np.random.rand(n_states)
startprob = startprob / np.sum(startprob)
cuhmm = GaussianHMMCUDAImpl(n_states, n_features)
cuhmm._sequences = [t1]
pyhmm = GaussianHMM(n_components=n_states,
init_params='',
params='',
covariance_type='diag')
cuhmm.means_ = means
cuhmm.vars_ = vars
cuhmm.transmat_ = transmat
cuhmm.startprob_ = startprob
logprob, custats = cuhmm.do_estep()
pyhmm.means_ = means
pyhmm.covars_ = vars
pyhmm.transmat_ = transmat
pyhmm.startprob_ = startprob
pyhmm._initialize_sufficient_statistics()
framelogprob = pyhmm._compute_log_likelihood(t1)
cuframelogprob = cuhmm._get_framelogprob()
yield lambda: np.testing.assert_array_almost_equal(
framelogprob, cuframelogprob, decimal=3)
fwdlattice = pyhmm._do_forward_pass(framelogprob)[1]
cufwdlattice = cuhmm._get_fwdlattice()
yield lambda: np.testing.assert_array_almost_equal(
fwdlattice, cufwdlattice, decimal=3)
bwdlattice = pyhmm._do_backward_pass(framelogprob)
cubwdlattice = cuhmm._get_bwdlattice()
yield lambda: np.testing.assert_array_almost_equal(
bwdlattice, cubwdlattice, decimal=3)
gamma = fwdlattice + bwdlattice
posteriors = np.exp(gamma.T - logsumexp(gamma, axis=1)).T
cuposteriors = cuhmm._get_posteriors()
yield lambda: np.testing.assert_array_almost_equal(
posteriors, cuposteriors, decimal=3)
stats = pyhmm._initialize_sufficient_statistics()
pyhmm._accumulate_sufficient_statistics(stats, t1, framelogprob,
posteriors, fwdlattice,
bwdlattice, 'stmc')
print 'ref transcounts'
print transitioncounts(cufwdlattice, cubwdlattice, cuframelogprob,
np.log(transmat))
print 'cutranscounts'
print custats['trans']
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], custats['trans'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['post'], custats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs'], custats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs**2'], custats['obs**2'], decimal=3)
