def test_markov_chain(): # initialization and initial state (take n>=10000 to challenge the numerics) n = 100 mc = MarkovChain(mkm.line_lazy_transition_matrix(n)) assert(mc.get_n() == n) assert(mc.stationary_known() == False) assert(mc.get_stationary() == None) assert(mc.num_distributions() == 0) # distributions mc.add_distributions(mkm.delta_distribution(n,0)) assert (mc.get_distribution(0) == mkm.delta_distribution(n,0)).all() mc.add_random_delta_distributions(2) mc.add_distributions(mkm.delta_distribution(n,n-1)) assert(mc.num_distributions() == 4) # iterations assert(mc.last_iteration_time(1) == 0) assert (mc.closest_iteration_time(0,0) == 0) assert (mc.closest_iteration_time(0,5) == 0) # iterate mc.iterate_distributions([0],2) # this one will determine the stationary distribution assert(mc.last_iteration_time(0) == 2) assert(mc.next_iteration_time(0,1) == 2) mc.iterate_distributions([0,1,3],5) mc.iterate_distributions_to_stationarity([0,2]) mc.iterate_all_distributions_to_stationarity() # assert iteration time and prev & next iteration time mc.assert_iteration([0], 99) mc.assert_iteration([0], 101) assert(mc.previous_iteration_time(0,100) == 99) assert(mc.next_iteration_time(0,100) == 101) # stationary distribution # mixing (x,tv) = mc.distribution_tv_mixing(1) mc.compute_tv_mixing() # path sampling path = mc.sample_path(1,10) assert(path[0] == 1) assert(len(path) == 10) # print mc.print_info()
def test_markov_chain():
# initialization and initial state (take n>=10000 to challenge the numerics)
n = 100
mc = MarkovChain(mkm.line_lazy_transition_matrix(n))
assert (mc.get_n() == n)
assert (mc.stationary_distribution_known() == False)
assert (mc.get_stationary_distribution() == None)
assert (mc.num_distributions() == 0)
# distributions
mc.add_distributions(mkm.delta_distribution(n, 0))
assert (mc.get_distribution(0) == mkm.delta_distribution(n, 0)).all()
mc.add_distributions(mkm.random_delta_distributions(n, 2))
mc.add_distributions(mkm.delta_distribution(n, n - 1))
assert (mc.num_distributions() == 4)
# iterations
assert (mc.last_iteration_time(1) == 0)
# iterate
mc.iterate_distributions(
[0], 2) # this one will determine the stationary distribution
assert (mc.last_iteration_time(0) == 2)
assert (mc.next_iteration_time(0, 1) == 2)
mc.iterate_distributions([0, 1, 3], 5)
mc.iterate_distributions_to_stationarity([0, 2])
mc.iterate_all_distributions_to_stationarity()
# stationary distribution
# mixing
(x, tv) = mc.distribution_tv_mixing(1)
mc.compute_tv_mixing()
# print some stuff
mc.print_info()
def test_markov_chain(): # initialization and initial state (take n>=10000 to challenge the numerics) n = 100 mc = MarkovChain(mkm.line_lazy_transition_matrix(n)) assert(mc.get_n() == n) assert(mc.stationary_distribution_known() == False) assert(mc.get_stationary_distribution() == None) assert(mc.num_distributions() == 0) # distributions mc.add_distributions(mkm.delta_distribution(n,0)) assert (mc.get_distribution(0) == mkm.delta_distribution(n,0)).all() mc.add_distributions(mkm.random_delta_distributions(n,2)) mc.add_distributions(mkm.delta_distribution(n,n-1)) assert(mc.num_distributions() == 4) # iterations assert(mc.last_iteration_time(1) == 0) # iterate mc.iterate_distributions([0],2) # this one will determine the stationary distribution assert(mc.last_iteration_time(0) == 2) assert(mc.next_iteration_time(0,1) == 2) mc.iterate_distributions([0,1,3],5) mc.iterate_distributions_to_stationarity([0,2]) mc.iterate_all_distributions_to_stationarity() # stationary distribution # mixing (x,tv) = mc.distribution_tv_mixing(1) mc.compute_tv_mixing() # print some stuff mc.print_info()