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)