def validate(a_l,
b_l,
c_l,
a_u,
b_u,
c_u,
x_minus,
x_plus,
y_minus,
y_plus,
verify_and_modify_all=False,
max_iter=100,
plot=False,
eps=1e-5,
print_info=True):
# eps =1e-5
original_shape = c_l.shape
a_l_new = a_l.view(-1) #.data.clone()
b_l_new = b_l.view(-1) #.data.clone()
c_l_new = c_l.view(-1) #.data.clone()
a_u_new = a_u.view(-1) #.data.clone()
b_u_new = b_u.view(-1) #.data.clone()
c_u_new = c_u.view(-1) #.data.clone()
x_minus_new = x_minus.view(-1) #.data.clone()
x_plus_new = x_plus.view(-1) #.data.clone()
y_minus_new = y_minus.view(-1) #.data.clone()
y_plus_new = y_plus.view(-1) #.data.clone()
N = a_l_new.size(0)
if verify_and_modify_all:
max_iter = N
for i in range(max_iter):
if verify_and_modify_all:
n = i
else:
n = torch.randint(0, N, [1])
n = n.long()
hl_fl, hu_fu = plot_2_surface(x_minus_new[n],
x_plus_new[n],
y_minus_new[n],
y_plus_new[n],
a_l_new[n],
b_l_new[n],
c_l_new[n],
a_u_new[n],
b_u_new[n],
c_u_new[n],
plot=plot)
# print('hl-fl max', hl_fl.max())
# print('hu-fu min', hu_fu.min())
if print_info:
print(
'tanh sigmoid iter: %d num: %d hl-f max %.6f mean %.6f hu-f min %.6f mean %.6f'
% (i, n, hl_fl.max(), hl_fl.mean(), hu_fu.min(), hu_fu.mean()))
if hl_fl.max() > eps: #we want hl_fl.max() < 0
print(x_minus_new[n], x_plus_new[n], y_minus_new[n], y_plus_new[n],
a_l_new[n], b_l_new[n], c_l_new[n], a_u_new[n], b_u_new[n],
c_u_new[n])
plot_surface(x_minus_new[n], x_plus_new[n], y_minus_new[n],
y_plus_new[n], a_l_new[n], b_l_new[n], c_l_new[n])
print('hl-f max', hl_fl.max())
raise Exception('lower plane fail')
break
if hl_fl.max() > 0 and verify_and_modify_all:
c_l_new[n] = c_l_new[n] - hl_fl.max() * 2
if hu_fu.min() < -eps: # we want hu_fu.min()>0
print(x_minus_new[n], x_plus_new[n], y_minus_new[n], y_plus_new[n],
a_l_new[n], b_l_new[n], c_l_new[n], a_u_new[n], b_u_new[n],
c_u_new[n])
plot_surface(x_minus_new[n], x_plus_new[n], y_minus_new[n],
y_plus_new[n], a_u_new[n], b_u_new[n], c_u_new[n])
print('hu-f min', hu_fu.min())
raise Exception('upper plane fail')
break
if hu_fu.min() < 0 and verify_and_modify_all:
c_u_new[n] = c_u_new[n] - hu_fu.min() * 2
c_l_new = c_l_new.view(original_shape)
c_u_new = c_u_new.view(original_shape)
return c_l_new, c_u_new
length = 3
x_minus = torch.Tensor([-length])
x_plus = torch.Tensor([length])
y_minus = torch.Tensor([-1])
y_plus = torch.Tensor([length])
num = [0]
device = torch.device('cpu')
# x_minus = ((torch.rand(num, device=device) - 0.5) * 10)
# x_plus = (torch.rand(num, device=device)*5 + x_minus)
# y_minus = ((torch.rand(num, device=device)-0.5) * 10)
# y_plus = (torch.rand(num, device=device)*5 + y_minus)
print_info = False
start = time.time()
a_l, b_l, c_l, a_u, b_u, c_u = bound_tanh_sigmoid(x_minus,
x_plus,
y_minus,
y_plus,
fine_tune_c=False,
use_1D_line=False,
use_constant=False,
print_info=print_info)
end = time.time()
v1, v2 = plot_2_surface(x_minus[num], x_plus[num], y_minus[num],
y_plus[num], a_l[num], b_l[num], c_l[num],
a_u[num], b_u[num], c_u[num])
# validate(a_l,b_l,c_l,a_u,b_u,c_u,x_minus, x_plus, y_minus, y_plus,
# max_iter=100,plot=False, eps=1e-4, print_info = print_info)
print('time used:', end - start)
print_info=print_info)
increase = raise_upper_plane(loss1, loss2, loss3, loss4, a_best, b_best,
c_best, x_minus, x_plus, y_minus, y_plus)
c_best = c_best + increase
return a_best.detach(), b_best.detach(), c_best.detach()
if __name__ == '__main__':
x_minus = torch.Tensor([0.062])
x_plus = torch.Tensor([5])
y_minus = torch.Tensor([0.1032])
y_plus = torch.Tensor([5.3253])
print_info = False
a, b, c = main_lower(x_minus,
x_plus,
y_minus,
y_plus,
print_info=print_info)
a_best, b_best, c_best = main_upper(x_minus,
x_plus,
y_minus,
y_plus,
print_info=print_info)
num = 0
v1, v2 = plot_2_surface(x_minus[num], x_plus[num], y_minus[num],
y_plus[num], a[num], b[num], c[num], a_best[num],
b_best[num], c_best[num])
lr=1e-2,
max_iter=500,
print_info=print_info)
return a_upper.detach(), b_upper.detach(), c_upper.detach()
if __name__ == '__main__':
x_minus = torch.Tensor([-5.2])
x_plus = torch.Tensor([-0.1])
y_minus = torch.Tensor([0.1])
y_plus = torch.Tensor([5.2])
num = 0
print_info = False
a_lower, b_lower, c_lower = main_lower(x_minus,
x_plus,
y_minus,
y_plus,
print_info=print_info)
a_upper, b_upper, c_upper = main_upper(x_minus,
x_plus,
y_minus,
y_plus,
print_info=print_info)
v1, v2 = plot_2_surface(x_minus[num], x_plus[num], y_minus[num],
y_plus[num], a_lower[num], b_lower[num],
c_lower[num], a_upper[num], b_upper[num],
c_upper[num])
I_l = (X_l>=0).float()
I_u = (X_u>=0).float()
#k_l y + b_l <= sigmoid(y) <= k_u y + b_u
#X_l*k_l y + X_l*b_l <= tanh(x)sigmoid(y), when X_l>=0
#X_l*k_u y + X_l*b_u <= tanh(x)sigmoid(y), when X_l<0
alpha_l = torch.zeros(x_minus.shape, device=x_minus.device)
beta_l = I_l * X_l * kl + (1-I_l) * X_l * ku
gamma_l = I_l * X_l * bl + (1-I_l) * X_l * bu
#tanh(x)sigmoid(y) <= X_u*k_u y + X_u*b_u, when X_u>=0
#tanh(x)sigmoid(y) <= X_u*k_l y + X_u*b_l, when X_u<0
alpha_u = torch.zeros(x_plus.shape, device=x_minus.device)
beta_u = I_u * X_u * ku + (1-I_u) * X_u * kl
gamma_u = I_u * X_u * bu + (1-I_u) * X_u * bl
idx= (0,0)
# plot_surface(x_minus, x_plus,y_minus, y_plus, alpha_l,beta_l,gamma_l)
plot_2_surface(x_minus[idx], x_plus[idx],y_minus[idx], y_plus[idx],
alpha_l[idx],beta_l[idx],gamma_l[idx],
alpha_u[idx],beta_u[idx],gamma_u[idx], plot=True, num_points=20)