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 = []