def oldmethod():
#Compare this method and CIRP method.
if scenario == INSPECT: #Scrap
#ropt = x[0:m]
#kopt = x[m:]
#sigmaopt = hp.sigma(E,F,ropt)
sigmacompare = np.array([0.09, 0.06, 0.1])
sigmaY_Taylorcompare = hp.sigmaY(sigmacompare, D, scenario, k)
rcompare = hp.sigmator(sigmacompare, E, F)
costcompare = hp.C(A, B, rcompare)
kcompare = np.array([2.47, 2.34,
2.83]) #np.array([2.450709, 1.9927, 3.1678])
#Update Lambda by simulation
#lamada = hp.updateLambda(D,sigmacompare,kcompare,miu,NSample)
#lamada = 0.876
U_compare = hp.U_scrap(costcompare, USY, miuY, sigmaY_Taylorcompare,
kcompare, Sp, Sc)
print('Old Method minimum value = ', U_compare)
elif scenario == NOINSPECT:
#ropt = x
#sigmaopt = hp.sigma(E,F,ropt)
sigmacompare = np.array([0.09, 0.06, 0.1])
sigmaY_Taylorcompare = hp.sigmaY(sigmacompare, D, scenario, k)
rcompare = hp.sigmator(sigmacompare, E, F)
costcompare = hp.C(A, B, rcompare)
U_compare = hp.U_noscrap(costcompare, USY, miuY, sigmaY_Taylorcompare,
Sp)
print('Old Method minimum value = ', U_compare)
def obj_scipy_noinspect(x):
#retrieve r and k
num_m = int(x.size / 2)
r = x[0:num_m]
sigmaX = hp.sigma(E, F, r)
sigmaY_Taylor = hp.sigmaY(sigmaX, D)
#Compute Unit Cost
C = hp.C(A, B, r)
U = hp.U_noscrap(C, USY, miuY, sigmaY_Taylor)
print(U)
return U
def obj_nlopt_noinspect(x, grad):
#retrieve r as the optimization variable x. (k will not be optimized, so just use const)
r = x[0:x.size]
sigmaX = hp.sigma(E, F, r)
sigmaY_Taylor = hp.sigmaY(sigmaX, D)
#Compute Unit Cost
C = hp.C(A, B, r)
U = hp.U_noscrap(C, USY, miuY, sigmaY_Taylor)
sigmaX = hp.sigma(E, F, r)
sigmaY_Taylor = hp.sigmaY(sigmaX, D)
for i in range(0, m): # Change this for loop to vectorization
dCi_dri_v = hp.dCi_dri(B[i], r[i])
dsigmai_dri_v = hp.dsigmai_dri(F[i], r[i])
dsigmaY_dri_v = hp.dsigmaY_dri(D, sigmaX, r, i, dsigmai_dri_v)
grad_r[i] = hp.dU_dri_noscrap(USY, miuY, sigmaY_Taylor, C, k, i,
dsigmaY_dri_v, dCi_dri_v)
if grad.size > 0:
grad[:] = grad_r
return U
def obj_nlopt_noinspect(x, grad, para):
#retrieve r as the optimization variable x. (k will not be optimized, so just use const)
A = para[0]
B = para[1]
E = para[2]
F = para[3]
r = x[0:m]
sigmaX = hp.sigma(E, F, r)
sigmaY_Taylor = hp.sigmaY(sigmaX, D, scenario, k)
#Compute Unit Cost
C = hp.C(A, B, r)
U = hp.U_noscrap(C, USY, miuY, sigmaY_Taylor, Sp)
for i in range(0, m): # Change this for loop to vectorization
dCi_dri_v = hp.dCi_dri(B[i], r[i])
dsigmai_dri_v = hp.dsigmai_dri(F[i], r[i])
dsigmaY_dri_v = hp.dsigmaY_dri(D, sigmaX, r, i, dsigmai_dri_v,
scenario, k)
grad_r[i] = hp.dU_dri_noscrap(USY, miuY, sigmaY_Taylor, C, k, i,
dsigmaY_dri_v, dCi_dri_v, Sp)
if grad.size > 0:
grad[:] = grad_r #Make sure to assign value using [:]
#print(U)
return U
grad_equation_k = np.zeros(m)
for i in range(0, m):
ri_add_epsilon = np.copy(r)
ri_minus_epsilon = np.copy(r)
ri_add_epsilon[i] += epsilon
ri_minus_epsilon[i] -= epsilon
sigmaX_plus = hp.sigma(E, F, ri_add_epsilon)
sigmaX_minus = hp.sigma(E, F, ri_minus_epsilon)
C_plus = hp.C(A, B, ri_add_epsilon)
C_minus = hp.C(A, B, ri_minus_epsilon)
sigmaY_Taylor_plus = hp.sigmaY(sigmaX_plus, D, scenario, k)
sigmaY_Taylor_minus = hp.sigmaY(sigmaX_minus, D, scenario, k)
if scenario == 1: #NO INSPECT
#gradient computed by numerical estimation
grad_numerical_r[i] = (hp.U_noscrap(
C_plus, USY, miuY, sigmaY_Taylor_plus, Sp) - hp.U_noscrap(
C_minus, USY, miuY, sigmaY_Taylor_minus, Sp)) / (2 * epsilon)
print('Numerical_No scrap_' + 'dr' + str(i), '=', grad_numerical_r[i])
#gradient computed by equation
dCi_dri_v = hp.dCi_dri(B[i], r[i])
dsigmai_dri_v = hp.dsigmai_dri(F[i], r[i])
dsigmaY_dri_v = hp.dsigmaY_dri(D, sigmaX, r, i, dsigmai_dri_v,
scenario, k)
grad_equation_r[i] = hp.dU_dri_noscrap(USY, miuY, sigmaY_Taylor, C, k,
i, dsigmaY_dri_v, dCi_dri_v, Sp)
print('Equation_No scrap_' + 'dr' + str(i), '=', grad_equation_r[i])
elif scenario == 2: #Inspection FIX k
#Varify dr
#gradient computed by numerical estimation
grad_numerical_r[i] = (
def gradientcheck(x, case):
if case == SCRAP:
grad_equation = obj_grad_scipy_inspect(x)
#retrieve grad of r and k
grad_equation_r = grad_equation[0:m]
grad_equation_k = grad_equation[m:]
grad_numerical_k = np.zeros(m)
grad_numerical_r = np.zeros(m)
elif case == NOSCRAP:
grad_equation_r = obj_grad_scipy_noinspect(x)
grad_numerical_r = np.zeros(m)
C = hp.C(A, B, r)
for i in range(0, m):
ri_add_epsilon = np.copy(r)
ri_minus_epsilon = np.copy(r)
ri_add_epsilon[i] += epsilon
ri_minus_epsilon[i] -= epsilon
ki_add_epsilon = np.copy(k)
ki_minus_epsilon = np.copy(k)
ki_add_epsilon[i] += epsilon
ki_minus_epsilon[i] -= epsilon
sigmaX_plus = hp.sigma(E, F, ri_add_epsilon)
sigmaX_minus = hp.sigma(E, F, ri_minus_epsilon)
C_plus = hp.C(A, B, ri_add_epsilon)
C_minus = hp.C(A, B, ri_minus_epsilon)
sigmaY_Taylor_plus = hp.sigmaY(sigmaX_plus, D)
sigmaY_Taylor_minus = hp.sigmaY(sigmaX_minus, D)
if case == SCRAP:
sigmaY_Taylor_p = lamada * sigmaY_Taylor
#Varify dr
sigmaY_Taylor_plus *= lamada
sigmaY_Taylor_minus *= lamada
#gradient computed by numerical estimation
grad_numerical_r[i] = (
hp.U_scrap(C_plus, USY, miuY, sigmaY_Taylor_plus, k) -
hp.U_scrap(C_minus, USY, miuY, sigmaY_Taylor_minus, k)) / (
2 * epsilon)
#varify dk
grad_numerical_k[i] = (
hp.U_scrap(C, USY, miuY, sigmaY_Taylor_p, ki_add_epsilon) -
hp.U_scrap(C, USY, miuY, sigmaY_Taylor_p,
ki_minus_epsilon)) / (2 * epsilon)
print('Numerical_scrap_' + 'dr' + str(i), '=', grad_numerical_r[i])
print('Equation_scrap_' + 'dr' + str(i), '=', grad_equation_r[i])
print('Numerical_scrap_' + 'dk' + str(i), '=', grad_numerical_k[i])
print('Equation_scrap_' + 'dk' + str(i), '=', grad_equation_k[i])
elif case == NOSCRAP:
#gradient computed by numerical estimation
grad_numerical_r[i] = (hp.U_noscrap(
C_plus, USY, miuY, sigmaY_Taylor_plus) - hp.U_noscrap(
C_minus, USY, miuY, sigmaY_Taylor_minus)) / (2 * epsilon)
print('Numerical_No scrap_' + 'dr' + str(i), '=',
grad_numerical_r[i])
print('Equation_No scrap_' + 'dr' + str(i), '=',
grad_equation_r[i])
distance12_r = distance.euclidean(grad_equation_r, grad_numerical_r)
length1_r = distance.euclidean(grad_equation_r,
np.zeros_like(grad_equation_r))
length2_r = distance.euclidean(grad_numerical_r,
np.zeros_like(grad_numerical_r))
graderror_r = distance12_r / (length1_r + length2_r)
print('error of dr=', graderror_r)
if case == SCRAP:
distance12_k = distance.euclidean(grad_equation_k, grad_numerical_k)
length1_k = distance.euclidean(grad_equation_k,
np.zeros_like(grad_equation_k))
length2_k = distance.euclidean(grad_numerical_k,
np.zeros_like(grad_numerical_k))
graderror_k = distance12_k / (length1_k + length2_k)
print('error of dk=', graderror_k)
obj_scipy_inspect,
x,
method='SLSQP',
jac=obj_grad_scipy_inspect, #Nelder-Mead #SLSQP
options={
'ftol': 1e-9,
'maxiter': 1000,
'disp': True
},
bounds=mbounds) #constraints=ineq_cons, #,callback=output
elif case == NOSCRAP:
#Define Upper and Lower boundaries
#The order is ([lower bnd for x1, lower bnd for x2], [Higher bnd for x1, Higher bnd for x2])
mbounds = Bounds([smallvalue, smallvalue, smallvalue],
[largevalue, largevalue, largevalue])
U_init = hp.U_noscrap(cost, USY, miuY, sigmaY_Taylor)
x = np.copy(r)
res = minimize(
obj_scipy_noinspect,
x,
method='SLSQP',
jac=obj_grad_scipy_noinspect, #Nelder-Mead #SLSQP
options={
'ftol': 1e-9,
'maxiter': 1000,
'disp': True
},
bounds=mbounds) #constraints=ineq_cons, #,callback=output
elif opt_lib == NLOPT:
if case == SCRAP: #Scrap
opt = nlopt.opt(nlopt.LD_MMA, 2 * m)
obj_scipy_inspect,
x,
method='SLSQP',
jac=obj_grad_scipy_inspect, #Nelder-Mead #SLSQP
options={
'ftol': 1e-9,
'maxiter': 1000,
'disp': True
},
bounds=mbounds) #constraints=ineq_cons, #,callback=output
elif scenario == NOINSPECT:
#Define Upper and Lower boundaries
#The order is ([lower bnd for x1, lower bnd for x2], [Higher bnd for x1, Higher bnd for x2])
mbounds = Bounds([smallvalue, smallvalue, smallvalue],
[largevalue, largevalue, largevalue])
U_init = hp.U_noscrap(cost, USY, miuY, sigmaY_Taylor, Sp)
x = np.copy(r)
res = minimize(
obj_scipy_noinspect,
x,
method='SLSQP',
jac=obj_grad_scipy_noinspect, #Nelder-Mead #SLSQP
options={
'ftol': 1e-9,
'maxiter': 1000,
'disp': True
},
bounds=mbounds) #constraints=ineq_cons, #,callback=output
elif opt_lib == NLOPT:
if scenario == INSPECT: #Scrap
opt = nlopt.opt(nlopt.LD_MMA,
