def case2():
fvdp3 = experiment2.get_fvdp3(0)
event1 = experiment2.get_event1(0)
y0 = [[5,5,5], [2,2,2]]
stepsize = 0.004
maxorder = 2
boundary_order = 1
num_mode = 2
T = 5
ep = 0.01
mergeep = 0.2
method='tolmerge'
t_list, y_list = simulation_ode_2(fvdp3, event1, y0, T, stepsize)
# A, b1, b2, Y, ytuple = diff_method_backandfor(t_list, y_list, maxorder, stepsize)
P,G,C = infer_model(
t_list, y_list, stepsize=stepsize, maxorder=maxorder, boundary_order=boundary_order,
num_mode=num_mode, modelist=fvdp3, event=event1, ep=ep, mergeep= mergeep,method=method, verbose=False)
d_avg = test_model(
P, G, C, num_mode, y_list , fvdp3, event1, maxorder, boundary_order)
# d_avg = test_model2(
# P, G, C, num_mode, A, Y , fvdp3, event1, maxorder, boundary_order)
print(G)
print(C[0]/C[0],C[1]/C[0],C[2]/C[0],C[3]/C[0])
print(d_avg)
with open('f2tr.txt','ab') as f:
for ypoints in y_list:
np.savetxt(f, ypoints, delimiter=" ")
@eventAttr()
def eventtest(t,y):
y0, y1, y2 = y
return C[0] * y0 + C[1] * y1 + C[2]* y2 + C[3]
ttest_list, ytest_list = simulation_ode_2([ode_test(G[0],maxorder),ode_test(G[1],maxorder)], eventtest, y0, T, stepsize)
ax = plt.axes(projection='3d')
for temp_y in y_list[0:1]:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
y2_list = temp_y.T[2]
ax.plot3D(y0_list, y1_list, y2_list,c='b',label='Original')
for temp_y in ytest_list[0:1]:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
y2_list = temp_y.T[2]
ax.plot3D(y0_list, y1_list, y2_list,c='r',label='Inferred', linestyle='--')
ax.set_xlabel('x1')
ax.set_ylabel('x2')
ax.set_zlabel('x3')
plt.legend()
plt.show()
def compare1(id, eid, case_id, verbose=False):
np.random.seed(0)
if eid == 'A':
case_info = experiment1.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment1.get_mode2(params)
event = experiment1.get_event1(params)
maxorder = 1
boundary_order = 1
num_mode = 2
ep = 0.01
mergeep = 0.01
dy = dynamics.mode2t
elif eid == 'B':
case_info = experiment2.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment2.get_fvdp3(params)
event = experiment2.get_event1(params)
maxorder = 2
boundary_order = 1
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.fvdp3_3
elif eid == 'C':
case_info = experiment3.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment3.get_mode(params)
event = experiment3.get_event(params)
maxorder = 3
boundary_order = 2
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.modeex3
elif eid == 'D':
case_info = experiment4.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment4.get_mmode(params)
event = experiment4.get_event(params)
maxorder = 2
boundary_order = 1
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.modeex4
elif eid == 'E':
case_info = experiment5.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment5.get_modetr(params)
event = experiment5.get_event(params)
labeltest = experiment5.get_labeltest(params)
maxorder = 2
boundary_order = 1
num_mode = 3
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.modetrt
# Obtain simulated trajectory
start = time.time()
if num_mode == 2:
t_list, y_list = simulation_ode_2(modelist, event, y0, T, stepsize)
test_t_list, test_y_list = simulation_ode_2(modelist, event, y0_test,
T, stepsize)
elif num_mode == 3:
t_list, y_list = simulation_ode_3(modelist, event, labeltest, y0, T,
stepsize)
test_t_list, test_y_list = simulation_ode_3(modelist, event, labeltest,
y0_test, T, stepsize)
else:
raise NotImplementedError
end = time.time()
# A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize)
# np.savetxt("data/CA"+str(id)+".txt",A,fmt='%8f')
# np.savetxt("data/Cb"+str(id)+".txt",b,fmt='%8f')
# YT, FT = diff(t_list+test_t_list, y_list+test_y_list, dy)
# np.savetxt("data/CYT"+str(id)+".txt",YT,fmt='%8f')
# np.savetxt("data/CFT"+str(id)+".txt",FT,fmt='%8f')
for i in range(0, len(y_list)):
np.savetxt("data1/YLIST" + str(id) + "_" + str(i) + ".txt",
y_list[i],
fmt='%8f')
for i in range(0, len(test_y_list)):
np.savetxt("data1/YTLIST" + str(id) + "_" + str(i) + ".txt",
test_y_list[i],
fmt='%8f')
def compare_opt(id, eid, case_id, verbose=False):
np.random.seed(0)
if eid == 'A':
case_info = experiment1.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment1.get_mode2(params)
event = experiment1.get_event1(params)
maxorder = 1
boundary_order = 1
num_mode = 2
ep = 0.01
mergeep = 0.01
dy = dynamics.mode2t
elif eid == 'B':
case_info = experiment2.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment2.get_fvdp3(params)
event = experiment2.get_event1(params)
maxorder = 2
boundary_order = 1
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.fvdp3_3
elif eid == 'C':
case_info = experiment3.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment3.get_mode(params)
event = experiment3.get_event(params)
maxorder = 3
boundary_order = 2
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.modeex3
elif eid == 'D':
case_info = experiment4.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment4.get_mmode(params)
event = experiment4.get_event(params)
maxorder = 2
boundary_order = 1
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.modeex4
elif eid == 'E':
case_info = experiment5.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment5.get_modetr(params)
event = experiment5.get_event(params)
labeltest = experiment5.get_labeltest(params)
maxorder = 2
boundary_order = 1
num_mode = 3
ep = case_info['ep']
mergeep = case_info['mergeep']
dy = dynamics.modetrt
# Obtain simulated trajectory
print(id, eid)
start = time.time()
if num_mode == 2:
t_list, y_list = simulation_ode_2(modelist, event, y0, T, stepsize)
test_t_list, test_y_list = simulation_ode_2(modelist, event, y0_test,
T, stepsize)
elif num_mode == 3:
t_list, y_list = simulation_ode_3(modelist, event, labeltest, y0, T,
stepsize)
test_t_list, test_y_list = simulation_ode_3(modelist, event, labeltest,
y0_test, T, stepsize)
else:
raise NotImplementedError
end = time.time()
# start = time.time()
# A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize)
A, b1, b2, Y, ytuple = diff_method_backandfor(t_list, y_list, maxorder,
stepsize)
optA, optb1, optb2, drop = seg_droprow(A, b1, b2, ep)
x0 = np.zeros(num_mode * optA.shape[1] * optb1.shape[1])
for ini in range(0, 5):
print('initial', ini)
for i in range(len(x0)):
x0[i] = np.random.uniform(-5, 5)
for optmethod in ['nelder-mead', 'COBYLA', 'Powell', 'CG']:
try:
infer_optimizationmtest(x0, optA, optb1, num_mode)
except:
print(optmethod, ' timeout')
def run_test(id, eid, case_id, methods, verbose=False):
np.random.seed(0)
if eid == 'A':
case_info = experiment1.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment1.get_mode2(params)
event = experiment1.get_event1(params)
maxorder = 1
boundary_order = 1
num_mode = 2
ep = 0.01
mergeep = 0.01
elif eid == 'B':
case_info = experiment2.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment2.get_fvdp3(params)
event = experiment2.get_event1(params)
maxorder = 2
boundary_order = 1
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
elif eid == 'C':
case_info = experiment3.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment3.get_mode(params)
event = experiment3.get_event(params)
maxorder = 3
boundary_order = 2
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
elif eid == 'D':
case_info = experiment4.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment4.get_mmode(params)
event = experiment4.get_event(params)
maxorder = 2
boundary_order = 1
num_mode = 2
ep = case_info['ep']
mergeep = case_info['mergeep']
elif eid == 'E':
case_info = experiment5.cases[case_id]
params = case_info['params']
y0 = case_info['y0']
y0_test = case_info['y0_test']
T = case_info['t_tuple']
stepsize = case_info['stepsize']
modelist = experiment5.get_modetr(params)
event = experiment5.get_event(params)
labeltest = experiment5.get_labeltest(params)
maxorder = 2
boundary_order = 1
num_mode = 3
ep = case_info['ep']
mergeep = case_info['mergeep']
# Obtain simulated trajectory
start = time.time()
if num_mode == 2:
t_list, y_list = simulation_ode_2(modelist, event, y0, T, stepsize)
test_t_list, test_y_list = simulation_ode_2(modelist, event, y0_test,
T, stepsize)
elif num_mode == 3:
t_list, y_list = simulation_ode_3(modelist, event, labeltest, y0, T,
stepsize)
test_t_list, test_y_list = simulation_ode_3(modelist, event, labeltest,
y0_test, T, stepsize)
else:
raise NotImplementedError
end = time.time()
# print('eid:', eid, 'N_init:', len(y0), 't_step:', stepsize, 'ep:', ep, 'sim_time: %.3f' % (end - start))
d_avg = dict()
infer_time = dict()
for method in methods:
start = time.time()
if num_mode == 2:
P, G, boundary = infer_model(t_list,
y_list,
stepsize=stepsize,
maxorder=maxorder,
boundary_order=boundary_order,
num_mode=num_mode,
modelist=modelist,
event=event,
ep=ep,
mergeep=mergeep,
method=method,
verbose=verbose)
end = time.time()
d_avg[method] = test_model(P, G, boundary, num_mode,
y_list + test_y_list, modelist, event,
maxorder, boundary_order)
infer_time[method] = end - start
elif num_mode == 3:
P, G, boundary = infer_model(t_list,
y_list,
stepsize=stepsize,
maxorder=maxorder,
boundary_order=boundary_order,
num_mode=num_mode,
modelist=modelist,
event=event,
ep=ep,
mergeep=mergeep,
method=method,
verbose=verbose,
labeltest=labeltest)
end = time.time()
d_avg[method] = test_model(P,
G,
boundary,
num_mode,
y_list + test_y_list,
modelist,
event,
maxorder,
boundary_order,
labeltest=labeltest)
infer_time[method] = end - start
else:
raise NotImplementedError
# print('Method: %s, d_avg: %.6f, infer_time: %.3f' % (method, d_avg[method], infer_time[method]))
# best_method, best_avg = None, 1.0
# for method, avg in d_avg.items():
# total_d_avg[method] += avg
# if avg < best_avg:
# best_method, best_avg = method, avg
# for method, t in infer_time.items():
# total_time[method] += t
# total_win[best_method] += 1
print(
'%d & $%s$ & %d & %.3f & %d & %.3f & %.5f & %.5f & %.5f& & %.1f & %.1f & %.1f& & \\\\'
% (id, eid, len(y0), stepsize, T, mergeep, d_avg['dbscan'],
d_avg['tolmerge'], d_avg['piecelinear'], infer_time['dbscan'],
infer_time['tolmerge'], infer_time['piecelinear']))
return d_avg, infer_time