def convert_be_tpm_to_le(be_tpm):
# Assumes state by node format
states, nodes = be_tpm.shape
le_tpm = np.zeros([states, nodes])
for i in range(states):
le_state = le_index2state(i, nodes)
be_tpm_row = state2be_index(le_state)
le_tpm[i, :] = be_tpm[be_tpm_row, :]
return le_tpm
def test_to_multidimensional():
# Identity
assert np.array_equal(convert.to_multidimensional(nd_state_by_node),
nd_state_by_node)
for tpm in [state_by_node, twod_state_by_node]:
N = tpm.shape[-1]
S = tpm.shape[0]
result = convert.to_multidimensional(tpm)
for i in range(S):
state = convert.le_index2state(i, N)
assert np.array_equal(result[state], tpm[i])
def pretty_print_tpm(node_tokens, tpm):
number_of_states, number_of_nodes = tpm.shape
for state_index in range(number_of_states):
current_state = le_index2state(state_index, number_of_nodes)
next_state = tpm[state_index, :]
pretty_tokens = format_node_tokens_by_state(node_tokens,
current_state,
mode='back')
pretty_tokens = format_node_tokens_by_state(pretty_tokens,
next_state,
mode='fore')
print(':'.join(pretty_tokens))
def test_to_multidimensional():
# Identity
assert np.array_equal(
convert.to_multidimensional(nd_state_by_node), nd_state_by_node
)
for tpm in [
state_by_node,
twod_state_by_node,
nonsquare_deterministic_1,
nonsquare_deterministic_2,
nonsquare_nondeterministic_1,
nonsquare_nondeterministic_2,
]:
S = tpm.shape[0]
N = int(log2(S))
result = convert.to_multidimensional(tpm)
for i in range(S):
state = convert.le_index2state(i, N)
assert np.array_equal(result[state], tpm[i])
def test_le_index2state():
assert convert.le_index2state(7, 8) == (1, 1, 1, 0, 0, 0, 0, 0)
assert convert.le_index2state(1, 3) == (1, 0, 0)
assert convert.le_index2state(8, 4) == (0, 0, 0, 1)