def main():
fdh_graph = FDC_Graph()
fwc_p1_vm = VM_Process1_DynamicSampling_시뮬레이터(A_p1, d_p1, C_p1, dM, dStart,
SEED)
fwc_p1_vm.DoE_Run(lamda_PLS=v_PLS, Z=Z_DoE, M=M) # DoE Run
VM_Output, ACT_Output, ez_run, y_act, y_prd = fwc_p1_vm.VM_Run(
lamda_PLS=v_PLS, Z=RUNS_CNT, M=M)
np.savetxt("output/VM_Output.csv", VM_Output, delimiter=",", fmt="%.4f")
np.savetxt("output/ACT_Output.csv", ACT_Output, delimiter=",", fmt="%.4f")
np.savetxt("output/ez_run.csv", ez_run, delimiter=",", fmt="%.4f")
np.savetxt("output/y_act.csv", y_act, delimiter=",", fmt="%.4f")
np.savetxt("output/y_prd.csv", y_prd, delimiter=",", fmt="%.4f")
fdh_graph.plt_show1(N, y_act[:, 0:1], y_prd[:, 0:1])
fdh_graph.plt_show2(RUNS_CNT, ez_run[:, 0:1], ez_run[:, 1:2], Noise=False)
ez_run_out = []
runM = M
ez_run_out.append(np.array([0, 0]))
for z in np.arange(1, RUNS_CNT + 1):
if z == dStart:
runM = dM
for k in np.arange(z * runM, (z + 1) * runM):
ez_run_out.append(ez_run[z])
#ez_run_out[0] = np.array([0, 0])
ez_run_out = np.array(ez_run_out)
np.savetxt("output/ez_run2.csv", ez_run_out, delimiter=",", fmt="%.4f")
fdh_graph.plt_show5(ez_run_out, N, M, dM, S1, Noise=False)
def main():
fdh_graph = FDC_Graph()
fwc_p1_vm = VM_Process1_DynamicSampling_노이즈시뮬레이터(A_p1, d_p1, C_p1, dM,
dStart, SEED)
fwc_p1_vm.DoE_Run(lamda_PLS=v_PLS, Z=Z_DoE, M=M) #DoE Run
VM_Output, ACT_Output, ez_run, y_act, y_prd = fwc_p1_vm.VM_Run(
lamda_PLS=v_PLS, Z=RUNS_CNT, M=M)
np.savetxt("output/VM_Output.csv", VM_Output, delimiter=",", fmt="%.4f")
np.savetxt("output/ACT_Output.csv", ACT_Output, delimiter=",", fmt="%.4f")
np.savetxt("output/ez_run.csv", ez_run, delimiter=",", fmt="%.4f")
np.savetxt("output/y_act.csv", y_act, delimiter=",", fmt="%.4f")
np.savetxt("output/y_prd.csv", y_prd, delimiter=",", fmt="%.4f")
fdh_graph.plt_show1(N, y_act[:, 0:1], y_prd[:, 0:1])
fdh_graph.plt_show2(RUNS_CNT, ez_run[:, 0:1], ez_run[:, 1:2], Noise=True)
ez_run_out = []
runM = M
ez_run_out.append(np.array([0, 0]))
for z in np.arange(1, RUNS_CNT + 1):
if z == dStart:
runM = dM
for k in np.arange(z * runM, (z + 1) * runM):
ez_run_out.append(ez_run[z])
ez_run_out = np.array(ez_run_out)
np.savetxt("output/ez_run2.csv", ez_run_out, delimiter=",", fmt="%.4f")
fdh_graph.plt_show5(ez_run_out, N, M, dM, S1, Noise=True)
p1_q1_mape_Queue = []
#metrology 마다 보여주는 MAPE 값이 의미가 없다.
for z in np.arange(Nz_RUN, 40, 1):
mape = fdh_graph.mean_absolute_percentage_error(
z + 1, y_act[((z + 1) * M) - 1][0], y_prd[((z + 1) * M) - 1][0])
p1_q1_mape_Queue.append(mape)
print('Process-1 q1 Every Metrology MAPE After 15 Lot : {0:.2f}%'.format(
np.mean(p1_q1_mape_Queue)))
p1_q1_mape_Queue = []
for i in np.arange(Nz_RUN * M, N, 1):
mape = fdh_graph.mean_absolute_percentage_error(
i + 1, y_act[i][0], y_prd[i][0])
p1_q1_mape_Queue.append(mape)
print('Process-1 q1 All MAPE After 15 Lot : {0:.2f}%'.format(
np.mean(p1_q1_mape_Queue)))
def main():
fdh_graph = FDC_Graph()
fwc_p1_vm = VM_Process1_시뮬레이터(A_p1, d_p1, C_p1, SEED1)
fwc_p1_vm.DoE_Run(lamda_PLS=v1_PLS, Z=Z_DoE, M=M) # DoE Run
Normal_VMResult, Normal_ACTResult, ez_run, o_y_act, o_y_prd = fwc_p1_vm.VM_Run(lamda_PLS=v1_PLS, Z=Z_VM, M=M)
fwc_p1_vm = VM_Process1_DynamicSampling_노이즈시뮬레이터(A_p1, d_p1, C_p1, dM, dStart, SEED1)
fwc_p1_vm.DoE_Run(lamda_PLS=v_PLS, Z=Z_DoE, M=M) # DoE Run
VM_Output, ACT_Output, ez_run, p1_y_act, p1_y_prd = fwc_p1_vm.VM_Run(lamda_PLS=v_PLS, Z=RUNS_CNT, M=M)
fdh_graph.plt_show1(N, p1_y_act[:, 0:1], p1_y_prd[:, 0:1])
ez_run_out = []
runM = M
ez_run_out.append(np.array([0, 0]))
for z in np.arange(1, RUNS_CNT + 1):
if z == dStart:
runM = dM
for k in np.arange(z * runM, (z + 1) * runM):
ez_run_out.append(ez_run[z])
ez_run_out = np.array(ez_run_out)
fdh_graph.plt_show5(ez_run_out, N, M, dM, S1, Noise=True)
p1_q1_mape_Queue = []
# metrology 마다 보여주는 MAPE 값이 의미가 없다.
for z in np.arange(Nz_RUN, Z_VM, 1):
mape = fdh_graph.mean_absolute_percentage_error(z + 1, p1_y_act[((z + 1) * M) - 1][0], p1_y_prd[((z + 1) * M) - 1][0])
p1_q1_mape_Queue.append(mape)
print('Process-1 q1 Every Metrology MAPE After 15 Lot : {0:.2f}%'.format(np.mean(p1_q1_mape_Queue)))
p1_q1_mape_Queue = []
for i in np.arange(Nz_RUN * M, Z_VM * M, 1):
mape = fdh_graph.mean_absolute_percentage_error(i + 1, p1_y_act[i][0], p1_y_prd[i][0])
p1_q1_mape_Queue.append(mape)
print('Process-1 q1 All MAPE After 15 Lot : {0:.2f}%'.format(np.mean(p1_q1_mape_Queue)))
np.savetxt("output/p1_mape.csv", p1_q1_mape_Queue, delimiter=",", fmt="%.4f")
np.savetxt("output/Error_VMresult1.csv", VM_Output, delimiter=",", fmt="%.4f")
np.savetxt("output/p1_y_prd.csv", p1_y_prd, delimiter=",", fmt="%.4f")
np.savetxt("output/p1_y_act.csv", p1_y_act, delimiter=",", fmt="%.4f")
np.savetxt("output/Error_ACTResult.csv", ACT_Output, delimiter=",", fmt="%.4f")
fwc_p2_act = VM_Process2_시뮬레이터(A_p2, d_p2, C_p2, F_p2, v1_PLS, p1_y_prd, p1_y_act, SEED2)
fwc_p2_act.DoE_Run(lamda_PLS=v2_PLS, Z=Z_DoE, M=M, f=o_y_act) # DoE Run ACT값으로 가능
p2_VM_Output, p2_ACT_Output, p2_ez_run, p2_y_act, p2_y_prd = fwc_p2_act.VM_Run(lamda_PLS=v2_PLS, Z=Z_VM, M=M)
fdh_graph.plt_show1(Z_VM * M, p2_y_act[:, 1:2], p2_y_prd[:, 1:2])
fdh_graph.plt_show2(Z_VM, p2_ez_run[:, 0:1], p2_ez_run[:, 1:2], Noise=True)
p2_q2_mape_Queue = []
# metrology 마다 보여주는 MAPE 값이 의미가 없다.
for z in np.arange(Nz_RUN, Z_VM, 1):
mape = fdh_graph.mean_absolute_percentage_error(z + 1, p2_y_act[((z + 1) * M) - 1][1], p2_y_prd[((z + 1) * M) - 1][1])
#print("으아아악 : ", p2_y_act[((z + 1) * M) - 1][1], p2_y_prd[((z + 1) * M) - 1][1])
p2_q2_mape_Queue.append(mape)
print('Process-2 q2 Every Metrology MAPE After 15 Lot : {0:.2f}%'.format(np.mean(p2_q2_mape_Queue)))
p2_q2_mape_Queue = []
for i in np.arange(Nz_RUN * M, Z_VM * M, 1):
mape = fdh_graph.mean_absolute_percentage_error(i + 1, p2_y_act[i][1], p2_y_prd[i][1])
p2_q2_mape_Queue.append(mape)
print('Process-2 q2 All MAPE After 15 Lot : {0:.2f}%'.format(np.mean(p2_q2_mape_Queue)))
def main():
fdh_graph = FDC_Graph()
fwc_p1_vm = VM_Process1_시뮬레이터(A_p1, d_p1, C_p1, SEED1)
fwc_p1_vm.DoE_Run(lamda_PLS=v1_PLS, Z=Z_DoE, M=M) # DoE Run
Normal_VMResult, Normal_ACTResult, ez_run, o_y_act, o_y_prd = fwc_p1_vm.VM_Run(
lamda_PLS=v1_PLS, Z=Z_VM, M=M)
fwc_p1_vm = VM_Process1_DynamicSampling_노이즈시뮬레이터(A_p1, d_p1, C_p1, dM,
dStart, SEED1)
fwc_p1_vm.DoE_Run(lamda_PLS=v_PLS, Z=Z_DoE, M=M) # DoE Run
VM_Output, ACT_Output, ez_run, p1_y_act, p1_y_prd = fwc_p1_vm.VM_Run(
lamda_PLS=v_PLS, Z=RUNS_CNT, M=M)
fdh_graph.plt_show1(Z_VM * M, p1_y_act[:, 0:1], p1_y_prd[:, 0:1])
ez_run_out = []
runM = M
ez_run_out.append(np.array([0, 0]))
for z in np.arange(1, RUNS_CNT + 1):
if z == dStart:
runM = dM
for k in np.arange(z * runM, (z + 1) * runM):
ez_run_out.append(ez_run[z])
ez_run_out = np.array(ez_run_out)
fdh_graph.plt_show5(ez_run_out, N, M, dM, S1, Noise=True)
np.savetxt("output/Error_VMresult2.csv",
VM_Output,
delimiter=",",
fmt="%.4f")
np.savetxt("output/Error_ACTResult2.csv",
ACT_Output,
delimiter=",",
fmt="%.4f")
# for z in np.arange(21, 32, 1):
# for k in np.arange(z * M, (z + 1) * M):
# Error_VMresult[k] = Error_VMresult[((z + 1) * M) - 1]
p1_q1_mape_Queue = []
# metrology 마다 보여주는 MAPE 값이 의미가 없다.
for z in np.arange(Nz_RUN, Z_VM, 1):
mape = fdh_graph.mean_absolute_percentage_error(
z + 1, p1_y_act[((z + 1) * M) - 1][0],
p1_y_prd[((z + 1) * M) - 1][0])
p1_q1_mape_Queue.append(mape)
print('Process-1 q1 Every Metrology MAPE After 15 Lot : {0:.2f}%'.format(
np.mean(p1_q1_mape_Queue)))
p1_q1_mape_Queue = []
for i in np.arange(Nz_RUN * M, Z_VM * M, 1):
mape = fdh_graph.mean_absolute_percentage_error(
i + 1, p1_y_act[i][0], p1_y_prd[i][0])
p1_q1_mape_Queue.append(mape)
print('Process-1 q1 All MAPE After 15 Lot : {0:.2f}%'.format(
np.mean(p1_q1_mape_Queue)))
p1_q1_mape_Queue = []
for z in np.arange(0, Z_VM, 1):
mape = fdh_graph.mean_absolute_percentage_error(
z + 1, p1_y_act[((z + 1) * M) - 1][0],
p1_y_prd[((z + 1) * M) - 1][0])
p1_q1_mape_Queue.append(mape)
np.savetxt("output/p1_mape.csv",
p1_q1_mape_Queue,
delimiter=",",
fmt="%.4f")
np.savetxt("output/Error_VMresult1.csv",
VM_Output,
delimiter=",",
fmt="%.4f")
np.savetxt("output/p1_y_prd.csv", p1_y_prd, delimiter=",", fmt="%.4f")
np.savetxt("output/p1_y_act.csv", p1_y_act, delimiter=",", fmt="%.4f")
np.savetxt("output/Error_ACTResult.csv",
ACT_Output,
delimiter=",",
fmt="%.4f")
for z in np.arange(Nz_RUN, Z_VM, 1):
if p1_q1_mape_Queue[z] >= Upstream_Rule_MAPE:
x1 = p1_y_act[(z * M) - 1]
x2 = p1_y_act[((z + 1) * M) - 1]
s = (x2 - x1) / M
act_q1 = np.abs(x2[0] - x1[0])
act_q2 = np.abs(x2[1] - x1[1])
if act_q1 <= 4 or act_q2 <= 4:
for k in np.arange(z * M, (z + 1) * M - 1, 1):
i = k % (z * M) + 1
#p1_y_prd[k] = x1 + s * i
p1_y_prd[k] = x2
#print('test : ', x1, x2, p1_y_prd[k])
#Error_VMresult[k] = Error_VMresult[((z + 1) * M) - 1]
# for z in np.arange(15, 40, 1):
# if p1_mape_Queue[z] >= 10:
# for k in np.arange(z * M, (z + 1) * M - 1):
# Error_VMresult[k] = Error_VMresult[((z + 1) * M) - 1]
np.savetxt("output/Error_VMresult_OK.csv",
p1_y_prd,
delimiter=",",
fmt="%.4f")
fwc_p2_act = VM_Process2_시뮬레이터(A_p2, d_p2, C_p2, F_p2, v1_PLS, p1_y_prd,
p1_y_act, SEED2)
fwc_p2_act.DoE_Run(lamda_PLS=v2_PLS, Z=Z_DoE, M=M,
f=o_y_act) # DoE Run ACT값으로 가능
p2_VM_Output, p2_ACT_Output, p2_ez_run, p2_y_act, p2_y_prd = fwc_p2_act.VM_Run(
lamda_PLS=v2_PLS, Z=Z_VM, M=M)
fdh_graph.plt_show1(Z_VM * M, p2_y_act[:, 1:2], p2_y_prd[:, 1:2])
fdh_graph.plt_show2(Z_VM, p2_ez_run[:, 0:1], p2_ez_run[:, 1:2], Noise=True)
p2_q2_mape_Queue = []
for i in np.arange(Nz_RUN * M, Z_VM * M, 1):
mape = fdh_graph.mean_absolute_percentage_error(
i + 1, p2_y_act[i][1], p2_y_prd[i][1])
p2_q2_mape_Queue.append(mape)
print('Process-2 q2 All MAPE After 15 Lot : {0:.2f}%'.format(
np.mean(p2_q2_mape_Queue)))
# metrology 마다 보여주는 MAPE 값이 의미가 없다.
for z in np.arange(Nz_RUN, Z_VM, 1):
mape = fdh_graph.mean_absolute_percentage_error(
z + 1, p2_y_act[((z + 1) * M) - 1][1],
p2_y_prd[((z + 1) * M) - 1][1])
p2_q2_mape_Queue.append(mape)
print('Process-2 q2 Every Metrology MAPE After 15 Lot : {0:.2f}%'.format(
np.mean(p2_q2_mape_Queue)))
p2_q2_mape_Queue = []
