def test_sim_gill_net_par2(self):
net = me.Network('net_par2')
net.structure([{
'start': 'X_t',
'end': 'Y_t',
'rate_symbol': 'd4',
'type': 'S -> E',
'reaction_steps': 4
}, {
'start': 'X_t',
'end': 'Y_t',
'rate_symbol': 'd2',
'type': 'S -> E',
'reaction_steps': 2
}, {
'start': 'Y_t',
'end': 'Y_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 3
}])
simulation_variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
sim = me.Simulation(net)
# to generate symbolic attributes we call some methods manually
sim.prepare_simulation_variables(simulation_variables)
sim.sim_gillespie.prepare_gillespie_simulation(
sim.sim_variables_order, sim.sim_variables_identifier)
# check all testable attributes for the simulation of this network
assert (sim.sim_variables == {'X_t': ('X_t', ), 'Y_t': ('Y_t', )})
assert (sim.sim_variables_order == [[('V_0', ), ('V_1', )],
[('V_0', 'V_0'), ('V_0', 'V_1'),
('V_1', 'V_1')]])
assert (sim.sim_variables_identifier == {
'V_0': ('X_t', ('X_t', )),
'V_1': ('Y_t', ('Y_t', ))
})
assert (sim.sim_gillespie.net_main_node_order_without_env == [
'Z_0', 'Z_1'
])
assert (sim.sim_gillespie.net_hidden_node_order_without_env == [
'Z_0__centric', 'Z_0__module_0__0', 'Z_0__module_0__1',
'Z_0__module_0__2', 'Z_0__module_1__0', 'Z_1__centric',
'Z_1__module_2__0', 'Z_1__module_2__1'
])
assert (
sim.sim_gillespie.sim_gill_propensities_eval ==
'np.array([\n4.0 * theta_1 * nodes_state[0],\n2.0 * theta_0 * nodes_state[0],\n4.0 * theta_1 * nodes_state[1],\n4.0 * theta_1 * nodes_state[2],\n4.0 * theta_1 * nodes_state[3],\n2.0 * theta_0 * nodes_state[4],\n3.0 * theta_2 * nodes_state[5],\n3.0 * theta_2 * nodes_state[6],\n3.0 * theta_2 * nodes_state[7]\n])'
)
assert (sim.sim_gillespie.sim_gill_reaction_number == 9)
assert (
sim.sim_gillespie.sim_gill_reaction_update_str ==
'def reac_event_fct(nodes_state, reac_ind):\n\tif reac_ind==0:\n\t\tnodes_state[0] -= 1\n\t\tnodes_state[1] += 1\n\tif reac_ind==1:\n\t\tnodes_state[0] -= 1\n\t\tnodes_state[4] += 1\n\tif reac_ind==2:\n\t\tnodes_state[1] -= 1\n\t\tnodes_state[2] += 1\n\tif reac_ind==3:\n\t\tnodes_state[2] -= 1\n\t\tnodes_state[3] += 1\n\tif reac_ind==4:\n\t\tnodes_state[3] -= 1\n\t\tnodes_state[5] += 1\n\tif reac_ind==5:\n\t\tnodes_state[4] -= 1\n\t\tnodes_state[5] += 1\n\tif reac_ind==6:\n\t\tnodes_state[5] -= 1\n\t\tnodes_state[6] += 1\n\tif reac_ind==7:\n\t\tnodes_state[6] -= 1\n\t\tnodes_state[7] += 1\n\tif reac_ind==8:\n\t\tnodes_state[7] -= 1\n\t\tnodes_state[5] += 2\n\treturn nodes_state'
)
assert (sim.sim_gillespie.summation_indices_nodes == [(0, 1, 2, 3, 4),
(5, 6, 7)])
assert (sim.sim_gillespie.summation_indices_variables == [(0, ),
(1, )])
def test_plots_est_corner_weight_plot(self):
net = me.Network('net_div_g5')
net.structure([{'start': 'X_t', 'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S', 'reaction_steps': 5}])
num_iter = 5
initial_values_type = 'synchronous'
initial_values = {'X_t': 1}
theta_values = {'l': 0.22}
time_values = np.array([0.0, 10.0])
variables = {'X_t': ('X_t', )}
sim = me.Simulation(net)
res_list = list()
for __ in range(num_iter):
res_list.append(sim.simulate('gillespie', variables, theta_values, time_values,
initial_values_type, initial_gillespie=initial_values)[1])
sims = np.array(res_list)
data = me.Data('data_test_select_models')
data.load(['X_t',], time_values, sims, bootstrap_samples=5, basic_sigma=0.01)
# overwrite with known values (from a num_iter=100 simulation)
data.data_mean = np.array([[[1. , 3.73 ]],
[[0.01 , 0.15406761]]])
data.data_variance = np.array([[[0. , 2.36070707]],
[[0.01 , 0.32208202]]])
### define model(s) for selection
net2 = me.Network('net_div_g2')
net2.structure([{'start': 'X_t', 'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 2},
{'start': 'X_t', 'end': 'env',
'rate_symbol': 'd',
'type': 'S ->',
'reaction_steps': 1}])
# important note: theta_bounds are reduced here to
# prevent odeint warning at high steps
networks = [net2]
variables = [{'X_t': ('X_t', )}]*1
initial_values_types = ['synchronous']*1
initial_values = [{('X_t',): 1.0, ('X_t', 'X_t'): 0.0}]*1
theta_bounds = [{'l': (0.0, 0.3), 'd': (0.0, 0.1)}]*1
### run selection (sequentially)
est_res = me.selection.select_models(networks, variables,
initial_values_types, initial_values,
theta_bounds, data, parallel=False)
me.plots.est_corner_weight_plot(est_res[0], show=False)
plt.close('all')
def sim_l2_moments_setup(self):
net = me.Network('net_div_l2')
net.structure([{'start': 'X_t', 'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S', 'reaction_steps': 2}])
initial_values_type = 'synchronous'
initial_values = {('X_t',): 1.0, ('X_t','X_t'): 0.0}
theta_values = {'l': 0.15}
time_values = np.linspace(0.0, 10.0, endpoint=True, num=11)
variables = {'X_t': ('X_t', )}
sim = me.Simulation(net)
sim_res_mom = sim.simulate('moments', variables, theta_values, time_values,
initial_values_type, initial_moments=initial_values)
return sim
def test_sim_gill_net_diff_erl3(self):
t = [{
'start': 'X_t',
'end': 'Y_t',
'rate_symbol': 'k_xy',
'type': 'S -> E',
'reaction_steps': 3
}]
net = me.Network('net_diff_erl3')
net.structure(t)
simulation_variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
sim = me.Simulation(net)
# to generate symbolic attributes we call some methods manually
sim.prepare_simulation_variables(simulation_variables)
sim.sim_gillespie.prepare_gillespie_simulation(
sim.sim_variables_order, sim.sim_variables_identifier)
# check all testable attributes for the simulation of this network
assert (sim.sim_variables == {'X_t': ('X_t', ), 'Y_t': ('Y_t', )})
assert (sim.sim_variables_order == [[('V_0', ), ('V_1', )],
[('V_0', 'V_0'), ('V_0', 'V_1'),
('V_1', 'V_1')]])
assert (sim.sim_variables_identifier == {
'V_0': ('X_t', ('X_t', )),
'V_1': ('Y_t', ('Y_t', ))
})
assert (sim.sim_gillespie.net_main_node_order_without_env == [
'Z_0', 'Z_1'
])
assert (sim.sim_gillespie.net_hidden_node_order_without_env == [
'Z_0__centric', 'Z_0__module_0__0', 'Z_0__module_0__1',
'Z_1__centric'
])
assert (
sim.sim_gillespie.sim_gill_propensities_eval ==
'np.array([\n3.0 * theta_0 * nodes_state[0],\n3.0 * theta_0 * nodes_state[1],\n3.0 * theta_0 * nodes_state[2]\n])'
)
assert (sim.sim_gillespie.sim_gill_reaction_number == 3)
assert (
sim.sim_gillespie.sim_gill_reaction_update_str ==
'def reac_event_fct(nodes_state, reac_ind):\n\tif reac_ind==0:\n\t\tnodes_state[0] -= 1\n\t\tnodes_state[1] += 1\n\tif reac_ind==1:\n\t\tnodes_state[1] -= 1\n\t\tnodes_state[2] += 1\n\tif reac_ind==2:\n\t\tnodes_state[2] -= 1\n\t\tnodes_state[3] += 1\n\treturn nodes_state'
)
assert (sim.sim_gillespie.summation_indices_nodes == [(0, 1, 2),
(3, )])
assert (sim.sim_gillespie.summation_indices_variables == [(0, ),
(1, )])
def sim_l2_gillespie_setup(self):
net = me.Network('net_div_l2')
net.structure([{'start': 'X_t', 'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S', 'reaction_steps': 2}])
initial_values_type = 'synchronous'
initial_values = {'X_t': 1}
theta_values = {'l': 0.15}
time_values = np.linspace(0.0, 10.0, endpoint=True, num=11)
variables = {'X_t': ('X_t', )}
num_iter = 1
sim = me.Simulation(net)
res_list = list()
for __ in range(num_iter):
res_list.append(sim.simulate('gillespie', variables, theta_values, time_values,
initial_values_type, initial_gillespie=initial_values)[1])
return sim
def test_sim_gill_net_influx_markov(self):
t = [{
'start': 'env',
'end': 'Y_t',
'rate_symbol': 'din',
'type': '-> E',
'reaction_steps': 1
}]
net = me.Network('net_influx_exp')
net.structure(t)
simulation_variables = {'Y_t': ('Y_t', )}
sim = me.Simulation(net)
# to generate symbolic attributes we call some methods manually
sim.prepare_simulation_variables(simulation_variables)
sim.sim_gillespie.prepare_gillespie_simulation(
sim.sim_variables_order, sim.sim_variables_identifier)
# check all testable attributes for the simulation of this network
assert (sim.sim_variables == {'Y_t': ('Y_t', )})
assert (sim.sim_variables_order == [[('V_0', )], [('V_0', 'V_0')]])
assert (sim.sim_variables_identifier == {'V_0': ('Y_t', ('Y_t', ))})
assert (sim.sim_gillespie.net_main_node_order_without_env == ['Z_0'])
assert (sim.sim_gillespie.net_hidden_node_order_without_env == [
'Z_0__centric'
])
assert (sim.sim_gillespie.sim_gill_propensities_eval ==
'np.array([\n1.0 * theta_0 * 1.0\n])')
assert (sim.sim_gillespie.sim_gill_reaction_number == 1)
assert (
sim.sim_gillespie.sim_gill_reaction_update_str ==
'def reac_event_fct(nodes_state, reac_ind):\n\tif reac_ind==0:\n\t\tnodes_state[0] += 1\n\treturn nodes_state'
)
assert (sim.sim_gillespie.summation_indices_nodes == [(0, )])
assert (sim.sim_gillespie.summation_indices_variables == [(0, )])
def test_division_model_sequential(self):
### create data from 5-steps model
net = me.Network('net_div_g5')
net.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 5
}])
num_iter = 100
initial_values_type = 'synchronous'
initial_values = {'X_t': 1}
theta_values = {'l': 0.22}
time_values = np.array([0.0, 10.0])
variables = {'X_t': ('X_t', )}
sim = me.Simulation(net)
res_list = list()
for __ in range(num_iter):
res_list.append(
sim.simulate('gillespie',
variables,
theta_values,
time_values,
initial_values_type,
initial_gillespie=initial_values)[1])
sims = np.array(res_list)
data = me.Data('data_test_select_models')
data.load([
'X_t',
],
time_values,
sims,
bootstrap_samples=10000,
basic_sigma=1 / num_iter)
# overwrite with known values (from a num_iter=100 simulation)
data.data_mean = np.array([[[1., 3.73]], [[0.01, 0.15406761]]])
data.data_variance = np.array([[[0., 2.36070707]], [[0.01,
0.32208202]]])
### define models for selection
net2 = me.Network('net_div_g2')
net2.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 2
}])
net5 = me.Network('net_div_g5')
net5.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 5
}])
net15 = me.Network('net_div_g15')
net15.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 15
}])
# important note: theta_bounds are reduced here to
# prevent odeint warning at high steps
networks = [net2, net5, net15]
variables = [{'X_t': ('X_t', )}] * 3
initial_values_types = ['synchronous'] * 3
initial_values = [{('X_t', ): 1.0, ('X_t', 'X_t'): 0.0}] * 3
theta_bounds = [{'l': (0.0, 0.5)}] * 3
### run selection (sequentially)
est_res = me.selection.select_models(networks,
variables,
initial_values_types,
initial_values,
theta_bounds,
data,
parallel=False)
### assert log evidence values
evid_res = np.array([est.bay_est_log_evidence for est in est_res])
# expected solution
evid_summary = np.array(
[[-10.629916697804973, 4.7325886118420755, -5.956029975676918],
[-10.707752281691006, 4.648483811563277, -5.904935097433867],
[-10.660052905403797, 4.786992306046756, -5.865460195018101],
[-10.560456631689906, 4.712198906183986, -6.024845876715448],
[-10.67465188224791, 4.739031922471972, -5.832238436663494],
[-10.688286571898711, 4.796078612214922, -5.866083545090796],
[-10.674939299757629, 4.763798403008956, -5.869504378082593],
[-10.706803542779763, 4.758241853964541, -5.921262356541525],
[-10.55770560556853, 4.770112513127923, -5.9405505492747315],
[-10.660075345658038, 4.832557590020761, -5.869471476946568]])
evid_sol = np.mean(evid_summary, axis=0)
evid_tol = np.max(np.std(evid_summary, axis=0) * 6)
np.testing.assert_allclose(evid_sol,
evid_res,
rtol=evid_tol,
atol=evid_tol)
### assert maximal log likelihood value
logl_max_res = np.array(
[est.bay_est_log_likelihood_max for est in est_res])
logl_max_sol = np.array(
[-6.655917426977538, 8.536409735755383, -2.583752295846338])
np.testing.assert_allclose(logl_max_sol,
logl_max_res,
rtol=1.0,
atol=1.0)
### assert estimated parameter value (95% credible interval)
theta_cred_res = np.array([est.bay_est_params_cred for est in est_res])
theta_cred_sol = [[[0.15403995, 0.14659241, 0.16079126]],
[[0.21181266, 0.20239213, 0.22030757]],
[[0.23227569, 0.21840278, 0.24577387]]]
np.testing.assert_allclose(theta_cred_sol,
theta_cred_res,
rtol=0.02,
atol=0.02)
def test_division_model_parallel_fit_mean_only(self):
### create data from 5-steps model
t = [{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 5
}]
net = me.Network('net_div_g5')
net.structure(t)
num_iter = 100
initial_values_type = 'synchronous'
initial_values = {'X_t': 1}
theta_values = {'l': 0.22}
time_values = np.array([0.0, 10.0])
variables = {'X_t': ('X_t', )}
sim = me.Simulation(net)
res_list = list()
for __ in range(num_iter):
res_list.append(
sim.simulate('gillespie',
variables,
theta_values,
time_values,
initial_values_type,
initial_gillespie=initial_values)[1])
sims = np.array(res_list)
data = me.Data('data_test_select_models')
data.load([
'X_t',
],
time_values,
sims,
bootstrap_samples=10000,
basic_sigma=1 / num_iter)
# overwrite with known values (from a num_iter=100 simulation)
data.data_mean = np.array([[[1., 3.73]], [[0.01, 0.15406761]]])
data.data_variance = np.array([[[0., 2.36070707]], [[0.01,
0.32208202]]])
### define models for selection
net2 = me.Network('net_div_g2')
net2.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 2
}])
net5 = me.Network('net_div_g5')
net5.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 5
}])
net15 = me.Network('net_div_g15')
net15.structure([{
'start': 'X_t',
'end': 'X_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 15
}])
# important note: theta_bounds are reduced here to
# prevent odeint warning at high steps
networks = [net2, net5, net15]
variables = [{'X_t': ('X_t', )}] * 3
initial_values_types = ['synchronous'] * 3
initial_values = [{('X_t', ): 1.0, ('X_t', 'X_t'): 0.0}] * 3
theta_bounds = [{'l': (0.0, 0.5)}] * 3
### run selection (in parallel)
est_res = me.selection.select_models(networks,
variables,
initial_values_types,
initial_values,
theta_bounds,
data,
sim_mean_only=False,
fit_mean_only=True,
parallel=True)
### assert log evidence values
evid_res = np.array([est.bay_est_log_evidence for est in est_res])
# expected solution
evid_summary = np.array(
[[0.9419435322851446, 1.0106982945064, 1.1012240890675635],
[0.9566246018872815, 1.0030070861382694, 1.185808337623029],
[0.9907574912748793, 1.047671927169712, 1.19272285522771],
[0.9344626373613755, 1.0150202141481144, 1.1356276897852824],
[0.9957857383128643, 1.1147029967812334, 1.2813572453964095],
[0.9224870435679013, 1.135816900454853, 1.2377276605741654],
[0.9894778490215606, 1.0941277563587202, 1.1494814071976407],
[0.9845703717052129, 1.2037659025849716, 1.1818061600496066],
[0.9512176652730777, 1.0724397747692442, 1.2583570008429867],
[0.8883984291598804, 1.003479199497773, 1.1824954558159297]])
evid_sol = np.mean(evid_summary, axis=0)
evid_tol = np.max(np.std(evid_summary, axis=0) * 6)
np.testing.assert_allclose(evid_sol,
evid_res,
rtol=evid_tol,
atol=evid_tol)
### assert maximal log likelihood value
logl_max_res = np.array(
[est.bay_est_log_likelihood_max for est in est_res])
# expected solution
logl_max_summary = np.array(
[[4.637656866490975, 4.63765686651381, 4.637656866427406],
[4.637656866313304, 4.63765686645764, 4.637656866472604],
[4.637656866511199, 4.637656866434893, 4.637656866503798],
[4.637656866478411, 4.637656866496112, 4.637656866472503],
[4.637656866481246, 4.637656866487981, 4.637656866364033],
[4.63765686651618, 4.637656866167647, 4.637656866000495],
[4.6376568664946385, 4.637656866516615, 4.637656865509905],
[4.637656866397452, 4.63765686646518, 4.637656866516921],
[4.637656866513331, 4.637656866516135, 4.637656866481746],
[4.6376568665054085, 4.637656866400874, 4.63765686645308]])
logl_max_sol = np.mean(logl_max_summary, axis=0)
logl_max_tol = np.max(np.std(logl_max_summary, axis=0) * 6)
np.testing.assert_allclose(logl_max_sol,
logl_max_res,
rtol=logl_max_tol,
atol=logl_max_tol)
### assert estimated parameter value (95% credible interval)
theta_cred_res = np.array([est.bay_est_params_cred for est in est_res])
# expected solution
theta_cred_summary = np.array([
[[[0.17767525971322726, 0.16766484301297668, 0.1871859065530188]],
[[0.2116378072993456, 0.19980784768361473, 0.22221378166000505]],
[[0.22706308238730954, 0.21426688997969887,
0.23898897505750633]]],
[[[0.17783574075135636, 0.16745234255033054, 0.18709129910629485]],
[[0.21159566312843459, 0.20025740650972587, 0.22225069777450657]],
[[0.22685131261479294, 0.2148889188053539, 0.23873886327930333]]],
[[[0.17772495817619227, 0.16792866692656294, 0.18716373871632497]],
[[0.21147737507817208, 0.20013138837338232, 0.2221836017996362]],
[[0.22712942097758385, 0.21459786012153031, 0.239019411969675]]],
[[[0.17765310186185357, 0.16737710709424608, 0.18699853276265388]],
[[0.21152367704878855, 0.20017333735288512, 0.22195840272665873]],
[[0.22739299020772027, 0.21494870304871327,
0.23871190646445717]]],
[[[0.17774890954479852, 0.16785378992605757, 0.18692744095053854]],
[[0.2117422771562304, 0.20033115893162734, 0.221744778476685]],
[[0.22700636347057088, 0.21483175457317827,
0.23850802940459553]]],
[[[0.17770323548026393, 0.16762338985697867, 0.18722030610380444]],
[[0.21148199985918062, 0.20004025740164794, 0.22246417034066013]],
[[0.22726641468296005, 0.21508239943597135,
0.23893200778722384]]],
[[[0.17774638376606106, 0.16741064926107696, 0.18714861450676257]],
[[0.211685981167947, 0.20044959549495348, 0.2218134914679289]],
[[0.2272913418178502, 0.21488828222344508, 0.23918800766586332]]],
[[[0.17786567511829948, 0.16774372529113826, 0.18716669314766138]],
[[0.21176955766928585, 0.2003380716254387, 0.2220132491656906]],
[[0.2267721834898168, 0.2144859118131004, 0.23877758912985064]]],
[[[0.17774477002988257, 0.16777800470913823, 0.186737513643131]],
[[0.21140631049740677, 0.20034762799106276, 0.22201850676261048]],
[[0.22699475212678594, 0.21520075443250175,
0.23875173560623902]]],
[[[0.17768763634149626, 0.16748127097311355, 0.18726644765855052]],
[[0.21158086725786138, 0.20022479235379922, 0.22237544704503034]],
[[0.22692686604795786, 0.21503615490679384, 0.2388629470323908]]]
])
theta_cred_sol = np.mean(theta_cred_summary, axis=0)
theta_cred_tol = np.max(np.std(theta_cred_summary, axis=0) * 6)
np.testing.assert_allclose(theta_cred_sol,
theta_cred_res,
rtol=theta_cred_tol,
atol=theta_cred_tol)
def simple_est_setup_mean_only(self):
# see jupyter notebook ex_docs_tests for more info and plots
### define network
net = me.Network('net_min_2_4')
net.structure([{
'start': 'X_t',
'end': 'Y_t',
'rate_symbol': 'd',
'type': 'S -> E',
'reaction_steps': 2
}, {
'start': 'Y_t',
'end': 'Y_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 4
}])
### create data with known values
num_iter = 100
initial_values_type = 'synchronous'
initial_values = {'X_t': 1, 'Y_t': 0}
theta_values = {'d': 0.03, 'l': 0.07}
time_values = np.array([0.0, 20.0, 40.0])
variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
sim = me.Simulation(net)
res_list = list()
for __ in range(num_iter):
res_list.append(
sim.simulate('gillespie',
variables,
theta_values,
time_values,
initial_values_type,
initial_gillespie=initial_values)[1])
sims = np.array(res_list)
data = me.Data('data_test_est_min_2_4')
data.load(['X_t', 'Y_t'],
time_values,
sims,
bootstrap_samples=10000,
basic_sigma=1 / num_iter)
# overwrite with fixed values (from a num_iter = 100 simulation)
data.data_mean = np.array([[[1., 0.67, 0.37], [0., 0.45, 1.74]],
[[0.01, 0.0469473, 0.04838822],
[0.01, 0.07188642, 0.1995514]]])
data.data_variance = np.array([[[0., 0.22333333, 0.23545455],
[0., 0.51262626, 4.03272727]],
[[0.01, 0.01631605, 0.01293869],
[0.01, 0.08878719, 0.68612036]]])
data.data_covariance = np.array([[[0., -0.30454545, -0.65030303]],
[[0.01, 0.0303608, 0.06645246]]])
### run estimation and return object
variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
initial_values_type = 'synchronous'
initial_values = {('X_t', ): 1.0, ('Y_t', ): 0.0}
theta_bounds = {'d': (0.0, 0.15), 'l': (0.0, 0.15)}
time_values = None
sim_mean_only = True
fit_mean_only = True
nlive = 1000 # 250 # 1000
tolerance = 0.01 # 0.1 (COARSE) # 0.05 # 0.01 (NORMAL)
bound = 'multi'
sample = 'unif'
est = me.Estimation('est_min_2_4', net, data)
est.estimate(variables, initial_values_type, initial_values,
theta_bounds, time_values, sim_mean_only, fit_mean_only,
nlive, tolerance, bound, sample)
return est