def test_project_yeast():
bn = BooleanNetwork(rules=dynpy.sample_nets.budding_yeast_bn)
bnensemble1 = MarkovChain.from_deterministic_system(bn, issparse=True)
bnensemble2 = MarkovChain.from_deterministic_system(bn, issparse=False)
marg1 = bnensemble1.project([0,1,2,3])
marg2 = bnensemble2.project([0,1,2,3])
assert_array_equal(marg1.transition_matrix.todense(), marg2.transition_matrix)
def test_project_yeast():
bn = BooleanNetwork(rules=dynpy.sample_nets.budding_yeast_bn)
bnensemble1 = MarkovChain.from_deterministic_system(bn, issparse=True)
bnensemble2 = MarkovChain.from_deterministic_system(bn, issparse=False)
marg1 = bnensemble1.project([0, 1, 2, 3])
marg2 = bnensemble2.project([0, 1, 2, 3])
assert_array_equal(marg1.transition_matrix.todense(),
marg2.transition_matrix)
def _test_project(issparse, initial_dist=None, expected=[[1., 0.], [0.5, 0.5]]):
bn = BooleanNetwork(rules=bnrules, mode='FUNCS')
bnensemble = MarkovChain.from_deterministic_system(
bn, issparse=issparse)
marg = bnensemble.project([0], initial_dist=initial_dist)
trans = dynpy.mx.todense(marg.transition_matrix)
assert_array_equal(trans, np.core.asarray(expected))
def _test_project(issparse,
initial_dist=None,
expected=[[1., 0.], [0.5, 0.5]]):
bn = BooleanNetwork(rules=bnrules, mode='FUNCS')
bnensemble = MarkovChain.from_deterministic_system(bn, issparse=issparse)
marg = bnensemble.project([0], initial_dist=initial_dist)
trans = dynpy.mx.todense(marg.transition_matrix)
assert_array_equal(trans, np.core.asarray(expected))
def test_from_deterministic():
bn = BooleanNetwork(rules=bnrules, mode='FUNCS')
mc = MarkovChain.from_deterministic_system(bn)
expected = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 1., 0., 0.],
[0., 0., 0., 1.]])
assert_array_equal(dynpy.mx.todense(mc.transition_matrix), expected)
def test_from_deterministic(): bn = BooleanNetwork(rules=bnrules, mode='FUNCS') mc = MarkovChain.from_deterministic_system(bn) expected = np.array([[ 1., 0., 0., 0.], [ 0., 1., 0., 0.], [ 0., 1., 0., 0.], [ 0., 0., 0., 1.]]) assert_array_equal(dynpy.mx.todense(mc.transition_matrix), expected)
def _test_attractors(issparse): bn = BooleanNetwork(rules=dynpy.sample_nets.budding_yeast_bn) bn_ensemble = MarkovChain.from_deterministic_system(bn, issparse=issparse) final_dist = bn_ensemble.iterate(bn_ensemble.get_uniform_distribution(), 100) bn.get_attractor_basins() total_states = 2 ** bn.num_vars dist2 = np.zeros(bn_ensemble.get_uniform_distribution().shape) for att, basin in zip(*bn.get_attractor_basins()): basin_size = len(basin) weight = float(basin_size) / total_states dist2[ bn_ensemble.state2ndx_map[att[0]] ] = weight assert_allclose(dist2, np.ravel(dynpy.mx.todense(final_dist)))
def _test_attractors(issparse):
bn = BooleanNetwork(rules=dynpy.sample_nets.budding_yeast_bn)
bn_ensemble = MarkovChain.from_deterministic_system(bn, issparse=issparse)
final_dist = bn_ensemble.iterate(bn_ensemble.get_uniform_distribution(),
100)
bn.get_attractor_basins()
total_states = 2**bn.num_vars
dist2 = np.zeros(bn_ensemble.get_uniform_distribution().shape)
for att, basin in zip(*bn.get_attractor_basins()):
basin_size = len(basin)
weight = float(basin_size) / total_states
dist2[bn_ensemble.state2ndx_map[att[0]]] = weight
assert_allclose(dist2, np.ravel(dynpy.mx.todense(final_dist)))
def test_check_transition_matrix_cont_sum():
MarkovChain(transition_matrix=np.zeros((2, 2)), discrete_time=False)
def test_check_transition_matrix_discrete_sum():
MarkovChain(transition_matrix=np.eye(2))
def test_check_transition_matrix_not_square():
MarkovChain(transition_matrix=np.zeros(shape=[1, 2]))
def test_check_transition_matrix_discrete_wrongsum():
MarkovChain(transition_matrix=np.ones((2, 2)))