def test_accept_change():
""" Check that move is accepted if second energy is lower """
from numpy import sqrt, count_nonzero, exp
mc = MonteCarlo(temperature=100.0)
# Should always be true. But do more than one draw, in case random incorrectly crept into
# implementation
for i in range(10):
assert_true(mc.accept_change(0.5, 0.4))
assert_true(mc.accept_change(0.5, 0.5))
# This should be accepted only part of the time, depending on exponential distribution
prior, successor = 0.4, 0.5
accepted = [mc.accept_change(prior, successor) for i in range(10000)]
assert_almost_equal(count_nonzero(accepted) / float(len(accepted)),
exp(-(successor - prior) / mc.temperature),
delta=3e0 / sqrt(len(accepted)))
def test_accept_change():
from numpy import sqrt, count_nonzero, exp
mc = MonteCarlo(temperature=100.0)
# Should always be true. But do more than one draw, in case random incorrectly crept into
# implementation
for i in range(10):
assert_true(mc.accept_change(0.5, 0.4))
assert_true(mc.accept_change(0.5, 0.5))
# This should be accepted only part of the time, depending on exponential distribution
prior, successor = 0.4, 0.5
accepted = [mc.accept_change(prior, successor) for i in range(10000)]
assert_almost_equal(
count_nonzero(accepted) / float(len(accepted)),
exp(-(successor - prior) / mc.temperature),
delta = 3e0 / sqrt(len(accepted))
)
def test_main_algorithm():
""" Check set path through main algorithm """
from mock import Mock, call
mc = MonteCarlo(temperature=100.0, itermax=4)
# Patch mc so that it takes a pre-determined path through
acceptance = [True, True, False, True]
mc.accept_change = Mock(side_effect=acceptance)
densities = (
[0, 0, 1, 0],
[0, 1, 1, 0],
[2, 2, 2, 2],
[2, 3, 3, 2],
[5, 3, 3, 5],
)
mc.change_density = Mock(side_effect=densities[1:])
mc.observe = Mock(return_value=True)
# Fake energy method
energies = [0.1, -0.1, -0.2, -0.15, -0.25]
energy = Mock(side_effect=energies)
# Call simulation
mc(energy, densities[0])
# Now, analyze path. First check length.
assert_equal(len(mc.accept_change.mock_calls), 4)
assert_equal(len(mc.change_density.mock_calls), 4)
assert_equal(len(mc.observe.mock_calls), 4)
assert_equal(len(energy.mock_calls),
5) # one extra call to get first energy
# Easiest to look at observe, since it should have all the info about the step
observe_path = [
call(0, acceptance[0], densities[1], energies[1]),
call(1, acceptance[1], densities[2], energies[2]),
call(2, acceptance[2], densities[2], energies[2]),
call(3, acceptance[3], densities[4], energies[4]),
]
assert_equal(observe_path, mc.observe.call_args_list)
def test_main_algorithm():
""" Check set path through main algorithm """
from mock import Mock, call
mc = MonteCarlo(temperature=100.0, itermax=4)
# Patch mc so that it takes a pre-determined path through
acceptance = [True, True, False, True]
mc.accept_change = Mock(side_effect=acceptance)
densities = (
[0, 0, 1, 0],
[0, 1, 1, 0],
[2, 2, 2, 2],
[2, 3, 3, 2],
[5, 3, 3, 5],
)
mc.change_density = Mock(side_effect=densities[1:])
mc.observe = Mock(return_value=True)
# Fake energy method
energies = [0.1, -0.1, -0.2, -0.15, -0.25]
energy = Mock(side_effect=energies)
# Call simulation
mc(energy, densities[0])
# Now, analyze path. First check length.
assert_equal(len(mc.accept_change.mock_calls), 4)
assert_equal(len(mc.change_density.mock_calls), 4)
assert_equal(len(mc.observe.mock_calls), 4)
assert_equal(len(energy.mock_calls), 5) # one extra call to get first energy
# Easiest to look at observe, since it should have all the info about the step
observe_path = [
call(0, acceptance[0], densities[1], energies[1]),
call(1, acceptance[1], densities[2], energies[2]),
call(2, acceptance[2], densities[2], energies[2]),
call(3, acceptance[3], densities[4], energies[4])
]
assert_equal(observe_path, mc.observe.call_args_list)