def test_cal_likelihood():
dz = _rand_mat(len(test_vectorized_output), ntopics)
zw = _rand_mat(ntopics, test_vectorized_output.global_term_count)
p_dw = np.zeros((len(test_vectorized_output), test_vectorized_output.global_term_count))
p_dw = _cal_p_dw(words_in_docs, word_cts_in_docs, range(ntopics), zw, dz, 0.8, p_dw)
likelihood = _cal_likelihood(words_in_docs, word_cts_in_docs, p_dw)
nt.assert_less(likelihood, 0)
def test_cal_likelihood():
dz = _rand_mat(len(test_vectorized_output), ntopics)
zw = _rand_mat(ntopics, test_vectorized_output.global_term_count)
p_dw = np.zeros((len(test_vectorized_output),
test_vectorized_output.global_term_count))
p_dw = _cal_p_dw(words_in_docs, word_cts_in_docs, range(ntopics), zw, dz,
0.8, p_dw)
likelihood = _cal_likelihood(words_in_docs, word_cts_in_docs, p_dw)
nt.assert_less(likelihood, 0)
def test_em():
dz = _rand_mat(len(test_vectorized_output), ntopics)
dw_z = np.zeros((len(test_vectorized_output), test_vectorized_output.global_term_count, ntopics))
p_dw = np.zeros((len(test_vectorized_output), test_vectorized_output.global_term_count))
zw = _rand_mat(ntopics, test_vectorized_output.global_term_count)
p_dw = _cal_p_dw(words_in_docs, word_cts_in_docs, range(ntopics), zw, dz, 0.8, p_dw)
dw_z = _e_step(words_in_docs, dw_z, range(ntopics), zw, dz, 0.8, p_dw)
zw, dz = _m_step(words_in_docs, word_cts_in_docs, range(ntopics), zw, dw_z, dz)
for topic in zw:
nt.assert_almost_equal(sum(topic), 1)
for doc in dz:
nt.assert_almost_equal(sum(doc), 1)
def test_rand_mat():
# ntopics, nwords
rand_mat = _rand_mat(2, 5)
nt.assert_equal(len(rand_mat), 2)
for topic in rand_mat:
nt.assert_equal(len(topic), 5)
nt.assert_almost_equal(sum(topic), 1)
def test_em():
dz = _rand_mat(len(test_vectorized_output), ntopics)
dw_z = np.zeros((len(test_vectorized_output),
test_vectorized_output.global_term_count, ntopics))
p_dw = np.zeros((len(test_vectorized_output),
test_vectorized_output.global_term_count))
zw = _rand_mat(ntopics, test_vectorized_output.global_term_count)
p_dw = _cal_p_dw(words_in_docs, word_cts_in_docs, range(ntopics), zw, dz,
0.8, p_dw)
dw_z = _e_step(words_in_docs, dw_z, range(ntopics), zw, dz, 0.8, p_dw)
zw, dz = _m_step(words_in_docs, word_cts_in_docs, range(ntopics), zw, dw_z,
dz)
for topic in zw:
nt.assert_almost_equal(sum(topic), 1)
for doc in dz:
nt.assert_almost_equal(sum(doc), 1)
def test_rand_mat():
# ntopics, nwords
rand_mat = _rand_mat(2, 5)
nt.assert_equal(len(rand_mat), 2)
for topic in rand_mat:
nt.assert_equal(len(topic), 5)
nt.assert_almost_equal(sum(topic), 1)