def test_opt0_traj9_1():
""" test_opt0_traj9_1 """
traj = simple_traj9()
start_theta = np.array([-1.0])
choices = [0, 0]
traj_choices = [(traj, choices)]
obj_fun_2 = trajs_obj_fun_2(traj_choices)
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0 = trajs_obj_fun_0(traj_choices)
obj_fun_0bis = trajs_obj_fun_0bis(traj_choices)
obj_fun_ref = trajs_obj_fun_ref(traj_choices)
max_iters = 5
(theta_ref, ys_ref) = \
optimize_function(obj_fun_ref, start_theta, max_iters, regularizer=1e-4)
print ys_ref, theta_ref
(theta_0bis, ys_0bis) = \
optimize_function(obj_fun_0bis, start_theta, max_iters, regularizer=1e-4)
(theta_0, ys_0) = \
optimize_function(obj_fun_0, start_theta, max_iters, regularizer=1e-4)
(theta_1, ys_1) = \
optimize_function(obj_fun_1, start_theta, max_iters, regularizer=1e-4)
(theta_2, ys_2) = \
optimize_function(obj_fun_2, start_theta, max_iters, regularizer=1e-4)
assert(np.abs(theta_0bis - theta_ref).max() < 1e-3), (theta_0bis, theta_ref)
assert(np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref)
assert(np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref)
assert(np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref)
assert within(ys_0bis[0], ys_ref[0], 1e-3), \
(theta_ref, ys_ref, theta_0bis, ys_0bis)
assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0)
assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2)
def test_opt0_traj4_1ref():
""" test_opt0_traj4_1 """
traj = simple_traj4()
start_theta = np.array([0.0, 0.0])
choices = [1, 0, 2]
estims = [np.array([0.0, 1.0]), np.array([1.0]), np.array([0.0, 0.0, 1.0])]
traj_choices = [(traj, choices)]
traj_estims = [(traj, estims)]
obj_fun_2 = trajs_obj_fun_2(traj_choices)
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0 = trajs_obj_fun_0(traj_choices)
obj_fun_ref = trajs_obj_fun_ref(traj_choices)
obj_fun_estim_ref = trajs_estim_obj_fun_ref(traj_estims)
max_iters = 5
(theta_0, ys_0) = optimize_function(obj_fun_0, start_theta, max_iters)
(theta_1, ys_1) = optimize_function(obj_fun_1, start_theta, max_iters)
(theta_2, ys_2) = optimize_function(obj_fun_2, start_theta, max_iters)
(theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta, max_iters)
(theta_estim_ref, ys_estim_ref) = optimize_function(obj_fun_estim_ref, \
start_theta, max_iters)
assert(np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref)
assert(np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref)
assert(np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref)
assert(np.abs(theta_estim_ref - theta_ref).max() < 1e-3), (theta_estim_ref, \
theta_ref)
assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0)
assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2)
assert within(ys_estim_ref[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, \
theta_estim_ref, \
ys_estim_ref)
def test_opt0_traj4_1ref():
""" test_opt0_traj4_1 """
traj = simple_traj4()
start_theta = np.array([0.0, 0.0])
choices = [1, 0, 2]
estims = [np.array([0.0, 1.0]), np.array([1.0]), np.array([0.0, 0.0, 1.0])]
traj_choices = [(traj, choices)]
traj_estims = [(traj, estims)]
obj_fun_2 = trajs_obj_fun_2(traj_choices)
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0 = trajs_obj_fun_0(traj_choices)
obj_fun_ref = trajs_obj_fun_ref(traj_choices)
obj_fun_estim_ref = trajs_estim_obj_fun_ref(traj_estims)
max_iters = 5
(theta_0, ys_0) = optimize_function(obj_fun_0, start_theta, max_iters)
(theta_1, ys_1) = optimize_function(obj_fun_1, start_theta, max_iters)
(theta_2, ys_2) = optimize_function(obj_fun_2, start_theta, max_iters)
(theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta,
max_iters)
(theta_estim_ref, ys_estim_ref) = optimize_function(obj_fun_estim_ref, \
start_theta, max_iters)
assert (np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref)
assert (np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref)
assert (np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref)
assert(np.abs(theta_estim_ref - theta_ref).max() < 1e-3), (theta_estim_ref, \
theta_ref)
assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0)
assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2)
assert within(ys_estim_ref[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, \
theta_estim_ref, \
ys_estim_ref)
def test_opt0_traj9_1():
""" test_opt0_traj9_1 """
traj = simple_traj9()
start_theta = np.array([-1.0])
choices = [0, 0]
traj_choices = [(traj, choices)]
obj_fun_2 = trajs_obj_fun_2(traj_choices)
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0 = trajs_obj_fun_0(traj_choices)
obj_fun_0bis = trajs_obj_fun_0bis(traj_choices)
obj_fun_ref = trajs_obj_fun_ref(traj_choices)
max_iters = 5
(theta_ref, ys_ref) = \
optimize_function(obj_fun_ref, start_theta, max_iters, regularizer=1e-4)
print ys_ref, theta_ref
(theta_0bis, ys_0bis) = \
optimize_function(obj_fun_0bis, start_theta, max_iters, regularizer=1e-4)
(theta_0, ys_0) = \
optimize_function(obj_fun_0, start_theta, max_iters, regularizer=1e-4)
(theta_1, ys_1) = \
optimize_function(obj_fun_1, start_theta, max_iters, regularizer=1e-4)
(theta_2, ys_2) = \
optimize_function(obj_fun_2, start_theta, max_iters, regularizer=1e-4)
assert (np.abs(theta_0bis - theta_ref).max() < 1e-3), (theta_0bis,
theta_ref)
assert (np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref)
assert (np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref)
assert (np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref)
assert within(ys_0bis[0], ys_ref[0], 1e-3), \
(theta_ref, ys_ref, theta_0bis, ys_0bis)
assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0)
assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2)
def test_opt0_traj5_1(): """ test_opt0_traj5_1 """ traj = simple_traj5() start_theta = np.array([-1.0]) choices = [1, 0, 2] traj_choices = [(traj, choices)] obj_fun_1 = trajs_obj_fun_1(traj_choices) obj_fun_0 = trajs_obj_fun_0(traj_choices) obj_fun_ref = trajs_obj_fun_ref(traj_choices) max_iters = 5 (theta_0, ys_0) = optimize_function(obj_fun_0, start_theta, max_iters) (theta_1, ys_1) = optimize_function(obj_fun_1, start_theta, max_iters) (theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta, max_iters) assert(np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref) assert(np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref) assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0) assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
def test_opt_traj4_1ref(): """ test_opt_traj4_1 """ traj = simple_traj4() start_theta = np.array([0.0, 0.0]) choices = [1, 0, 2] traj_choices = [(traj, choices)] obj_fun = trajs_obj_fun_ref(traj_choices) (theta, ys) = optimize_function(obj_fun, start_theta, max_iters=20) assert ys[0] <= ys[-1], (theta, ys, len(ys))
def test_1(): """ Basic test for the optimizer. """ start = np.zeros(2) opt_fun = wrap(fun1) (x, ys) = optimize_function(opt_fun, start, 10) assert(np.abs(x-np.ones_like(x)).max() < 1e-3) assert ys[-1] >= -1e-3 assert ys[-1] <= 0 assert np.abs(start).max() == 0, (start, x)
def test_opt_traj4_1ref():
""" test_opt_traj4_1 """
traj = simple_traj4()
start_theta = np.array([0.0, 0.0])
choices = [1, 0, 2]
traj_choices = [(traj, choices)]
obj_fun = trajs_obj_fun_ref(traj_choices)
(theta, ys) = optimize_function(obj_fun, start_theta, max_iters=20)
assert ys[0] <= ys[-1], (theta, ys, len(ys))
def test_1():
""" Basic test for the optimizer. """
start = np.zeros(2)
opt_fun = wrap(fun1)
(x, ys) = optimize_function(opt_fun, start, 10)
assert (np.abs(x - np.ones_like(x)).max() < 1e-3)
assert ys[-1] >= -1e-3
assert ys[-1] <= 0
assert np.abs(start).max() == 0, (start, x)
def test_opt0_traj5_1():
""" test_opt0_traj5_1 """
traj = simple_traj5()
start_theta = np.array([-1.0])
choices = [1, 0, 2]
traj_choices = [(traj, choices)]
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0 = trajs_obj_fun_0(traj_choices)
obj_fun_ref = trajs_obj_fun_ref(traj_choices)
max_iters = 5
(theta_0, ys_0) = optimize_function(obj_fun_0, start_theta, max_iters)
(theta_1, ys_1) = optimize_function(obj_fun_1, start_theta, max_iters)
(theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta,
max_iters)
assert (np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref)
assert (np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref)
assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0)
assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
def learn_em(opt_function_provider, trajs, start_value, \
max_iters=10, max_inner_iters=5):
""" Learn EM function.
Returns the history of the progression: list of:
- log likelihood
- learned parameter
Arguments:
- opt_function_provider: traj_estims -> (optimizable function)
- trajs: a set of trajectories
- start_value: numpy array, start vector
- max_iters: maximum number of iterations
"""
theta = np.array(start_value)
history = []
for step in range(max_iters):
traj_estims = get_traj_estims(trajs, theta)
opt_fun = opt_function_provider(traj_estims)
(theta1, ys) = optimize_function(opt_fun, theta, max_inner_iters)
print 'Epoch %i : %f' % (step, ys[-1])
history.append((ys[-1], theta1))
theta = theta1
return history
def learn_em(opt_function_provider, trajs, start_value, \
max_iters=10, max_inner_iters=5):
""" Learn EM function.
Returns the history of the progression: list of:
- log likelihood
- learned parameter
Arguments:
- opt_function_provider: traj_estims -> (optimizable function)
- trajs: a set of trajectories
- start_value: numpy array, start vector
- max_iters: maximum number of iterations
"""
theta = np.array(start_value)
history = []
for step in range(max_iters):
traj_estims = get_traj_estims(trajs, theta)
opt_fun = opt_function_provider(traj_estims)
(theta1, ys) = optimize_function(opt_fun, theta, max_inner_iters)
print 'Epoch %i : %f' % (step, ys[-1])
history.append((ys[-1], theta1))
theta = theta1
return history
