def case2(): modetr = get_modetr(0) event = get_event(0) labeltest = get_labeltest(0) y0 = [[-1, 1], [1, 4], [2, -3]] y1 = [[3, -1], [-1, 3]] T = 5 stepsize = 0.002 maxorder = 2 boundary_order = 1 num_mode = 3 ep = 0.0005 mergeep = 0.01 method = 'piecelinear' t_list, y_list = simulation_ode_3(modetr, event, labeltest, y0, T, stepsize) # A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) # np.savetxt("data/A5.txt",A,fmt='%8f') # np.savetxt("data/b5.txt",b,fmt='%8f') P, G, (coeff1, coeff2, [first, second, third ]) = infer_model(t_list, y_list, stepsize=stepsize, maxorder=maxorder, boundary_order=boundary_order, num_mode=num_mode, modelist=modetr, event=event, ep=ep, mergeep=mergeep, method=method, verbose=False, labeltest=labeltest) boundary = (coeff1, coeff2, [first, second, third]) t_test_list, y_test_list = simulation_ode_3(modetr, event, labeltest, y1, T, stepsize) # YT, FT = diff(t_list+t_test_list, y_list+y_test_list, dynamics.modetrt) # np.savetxt("data/YT5.txt",YT,fmt='%8f') # np.savetxt("data/FT5.txt",FT,fmt='%8f') d_avg = test_model(P, G, boundary, num_mode, y_list, modetr, event, maxorder, boundary_order, labeltest=labeltest) # print(G) print(d_avg)
def case1(): np.random.seed(0) modetr = get_modetr(0) event = get_event(0) labeltest = get_labeltest(0) y0 = [[1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0]] # y1 = [[3,-1], [-1,3]] T = 5 stepsize = 0.005 maxorder = 2 boundary_order = 1 num_mode = 5 ep = 0.005 mergeep = 0.01 t_list, y_list = simulation_ode_3(modetr, event, labeltest, y0, T, stepsize) # A, b1, b2, Y, ytuple = diff_method_backandfor(t_list, y_list, maxorder, stepsize) # res, drop, clfs = segment_and_fit(A, b1, b2, ytuple,ep=0.005) # np.savetxt("data/YY.txt",Y,fmt='%8f') # print(len(res)) # print(res) # P, G = merge_cluster_tol2(res, A, b1, num_mode, ep) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) np.savetxt("data/YY.txt", Y, fmt='%8f') P, G, D = infer_dynamic_modes_new(t_list, y_list, stepsize, maxorder, ep) # print(P) print(len(P)) if len(P) > num_mode: P, G = merge_cluster_tol2(P, A, b, num_mode, ep) P, _ = dropclass0(P, G, D, A, b, Y, ep, stepsize) print(P) print(len(P)) L_y = len(y_list[0][0]) boundary = svm_classify(P, Y, L_y, boundary_order, num_mode) print(boundary) d = test_model(P, G, boundary, num_mode, y_list, modetr, event, maxorder, boundary_order, labeltest=labeltest) print(d)
def case1(): modetr = get_modetr(0) event = get_event(0) labeltest = get_labeltest(0) y0 = [[-1, 1], [1, 4], [2, -3], [1, 1], [3, 1]] y1 = [[3, -1], [-1, 3]] T = 5 stepsize = 0.01 maxorder = 2 boundary_order = 1 num_mode = 3 ep = 0.005 mergeep = 0.01 method = 'piecelinear' t_list, y_list = simulation_ode_3(modetr, event, labeltest, y0, T, stepsize) A, b1, b2, Y = diff_method_backandfor(t_list, y_list, maxorder, stepsize) P, G, (coeff1, coeff2, [first, second, third ]) = infer_model(t_list, y_list, stepsize=stepsize, maxorder=maxorder, boundary_order=boundary_order, num_mode=num_mode, modelist=modetr, event=event, ep=ep, mergeep=mergeep, method=method, verbose=False, labeltest=labeltest) for i in range(0, len(P)): y0_list = [] y1_list = [] for j in range(0, len(P[i])): y0_list.append(Y[P[i][j], 0]) y1_list.append(Y[P[i][j], 1]) plt.scatter(y0_list, y1_list, s=1) plt.show()
def case10(): y0 = [[-1,1],[1,4],[2,-3]] t_tuple = [(0,5),(0,5),(0,5)] stepsize = 0.01 maxorder = 2 # start = time.time() def labeltest(y): if eventtr_1(0,y)<0 and eventtr_2(0,y)>0: return 0 elif eventtr_1(0,y)>=0 and eventtr_2(0,y)>0: return 1 else: return 2 t_list, y_list = simulation_ode_3([modetr_1, modetr_2, modetr_3], [eventtr_1,eventtr_2,eventtr_2], labeltest, y0, t_tuple, stepsize) draw2D(y_list) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) P,G,D = infer_dynamic_modes_new(t_list, y_list, stepsize, maxorder, 0.01) print(P) print(G) print(D) # for p in P: # print(len(p)) # tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3) # for temp in tpar_list: # print(temp[-1]) # labels = infer_dynamic_modes_ex_dbs(tpar_list, ypar_list, stepsize, maxorder, 0.02) # print(labels) # draw2D(ypar_list) for i in range(0,len(P)): y0_list = [] y1_list = [] for j in range(0,len(P[i])): y0_list.append(Y[P[i][j],0]) y1_list.append(Y[P[i][j],1]) plt.scatter(y0_list,y1_list,s=1) plt.show() P,G = reclass(A,b,P,0.01) for p in P: print(len(p)) print(G) for i in range(0,len(P)): y0_list = [] y1_list = [] for j in range(0,len(P[i])): y0_list.append(Y[P[i][j],0]) y1_list.append(Y[P[i][j],1]) plt.scatter(y0_list,y1_list,s=1) plt.show() P,D = dropclass(P,G,D,A,b,Y,0.01,0.01) print(D) for i in range(0,len(P)): y0_list = [] y1_list = [] for j in range(0,len(P[i])): y0_list.append(Y[P[i][j],0]) y1_list.append(Y[P[i][j],1]) plt.scatter(y0_list,y1_list,s=1) # plt.show() y=[] x=[] for j in range(0,len(P[2])): y.append(1) x.append({1:Y[P[2][j],0], 2:Y[P[2][j],1]}) for j in range(0,len(P[1])): y.append(-1) x.append({1:Y[P[1][j],0], 2:Y[P[1][j],1]}) for j in range(0,len(P[0])): y.append(-1) x.append({1:Y[P[0][j],0], 2:Y[P[0][j],1]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 100 -r 1 -b 0') m = svm_train(prob, param) svm_save_model('model_file', m) print("pred") p_label, p_acc, p_val = svm_predict(y, x, m) # print(p_label) nsv = m.get_nr_sv() svc = m.get_sv_coef() sv = m.get_SV() # print(nsv) # print(svc) # print(sv) # def clafun(x): # g = -m.rho[0] # for i in range(0,nsv): # g = g + svc[i][0] * ((0.5 * (x[0]*sv[i][1] + x[1]*sv[i][2]))**3) # return g g = -m.rho[0] a1 = 0 a2 = 0 for i in range(0,nsv): a1 = a1 + svc[i][0] * 0.5 * sv[i][1] a2 = a2 + svc[i][0] * 0.5 * sv[i][2] g = g + svc[i][0]*1 print(a1) print(a2) print(g) def f(x,y): g = -m.rho[0] for i in range(0,nsv): g = g + svc[i][0] * (0.5*(x*sv[i][1]+y*sv[i][2])+1) return g x=[] y=[] for j in range(0,len(P[1])): y.append(1) x.append({1:Y[P[1][j],0], 2:Y[P[1][j],1]}) for j in range(0,len(P[0])): y.append(-1) x.append({1:Y[P[0][j],0], 2:Y[P[0][j],1]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 100 -r 1 -b 0') m = svm_train(prob, param) svm_save_model('model_file', m) print("pred") p_label, p_acc, p_val = svm_predict(y, x, m) # print(p_label) nsv = m.get_nr_sv() svc = m.get_sv_coef() sv = m.get_SV() # print(nsv) # print(svc) # print(sv) # def clafun(x): # g = -m.rho[0] # for i in range(0,nsv): # g = g + svc[i][0] * ((0.5 * (x[0]*sv[i][1] + x[1]*sv[i][2]))**3) # return g g1 = -m.rho[0] b1 = 0 b2 = 0 for i in range(0,nsv): b1 = b1 + svc[i][0] * 0.5 * sv[i][1] b2 = b2 + svc[i][0] * 0.5 * sv[i][2] g1 = g1 + svc[i][0]*1 # print(a1) # print(a2) # print(g) def h(x,y): g = -m.rho[0] for i in range(0,nsv): g = g + svc[i][0] * (0.5*(x*sv[i][1]+y*sv[i][2])+1) return g x = np.linspace(-10,10,100) y = np.linspace(-10,10,100) X,Y = np.meshgrid(x,y)#将x,y指传入网格中 # plt.contourf(X,Y,f(X,Y),8,alpha=0.75,cmap=plt.cm.hot)#8指图中的8+1根线,绘制等温线,其中cmap指颜色 C = plt.contour(X,Y,f(X,Y),[0])#colors指等高线颜色 plt.clabel(C,inline=True,fontsize=10)#inline=True指字体在等高线中 D = plt.contour(X,Y,h(X,Y),[0])#colors指等高线颜色 plt.clabel(D,inline=True,fontsize=10)#inline=True指字体在等高线中 plt.xticks(()) plt.yticks(()) plt.show()
def case(y0, t_tuple, stepsize, maxorder, modelist, eventlist, labeltest, ep, method): t_list, y_list = simulation_ode_3(modelist, eventlist, labeltest, y0, t_tuple, stepsize) if method == "new": A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) P, G, D = infer_dynamic_modes_new(t_list, y_list, stepsize, maxorder, ep) P, G = reclass(A, b, P, ep) P, D = dropclass(P, G, D, A, b, Y, ep, stepsize) print(G) for i in range(0, len(P)): y0_list = [] y1_list = [] for j in range(0, len(P[i])): y0_list.append(Y[P[i][j], 0]) y1_list.append(Y[P[i][j], 1]) plt.scatter(y0_list, y1_list, s=1) plt.show() y = [] x = [] for j in range(0, len(P[2])): y.append(1) x.append({1: Y[P[2][j], 0], 2: Y[P[2][j], 1]}) for j in range(0, len(P[1])): y.append(-1) x.append({1: Y[P[1][j], 0], 2: Y[P[1][j], 1]}) for j in range(0, len(P[0])): y.append(-1) x.append({1: Y[P[0][j], 0], 2: Y[P[0][j], 1]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 100 -r 1 -b 0 -q') m = svm_train(prob, param) svm_save_model('model_file1', m) nsv = m.get_nr_sv() svc = m.get_sv_coef() sv = m.get_SV() g = -m.rho[0] a1 = 0 a2 = 0 for i in range(0, nsv): a1 = a1 + svc[i][0] * 0.5 * sv[i][1] a2 = a2 + svc[i][0] * 0.5 * sv[i][2] g = g + svc[i][0] * 1 print(a1 / a2, a2 / a2, g / a2) def f(x): g = -m.rho[0] for i in range(0, nsv): g = g + svc[i][0] * (0.5 * (x[0] * sv[i][1] + x[1] * sv[i][2]) + 1) return g > 0 x = [] y = [] for j in range(0, len(P[1])): y.append(1) x.append({1: Y[P[1][j], 0], 2: Y[P[1][j], 1]}) for j in range(0, len(P[0])): y.append(-1) x.append({1: Y[P[0][j], 0], 2: Y[P[0][j], 1]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 100 -r 1 -b 0 -q') n = svm_train(prob, param) svm_save_model('model_file2', n) # p_label, p_acc, p_val = svm_predict(y, x, n) nsv1 = n.get_nr_sv() svc1 = n.get_sv_coef() sv1 = n.get_SV() g1 = -n.rho[0] b1 = 0 b2 = 0 for i in range(0, nsv1): b1 = b1 + svc1[i][0] * 0.5 * sv1[i][1] b2 = b2 + svc1[i][0] * 0.5 * sv1[i][2] g1 = g1 + svc1[i][0] * 1 print(b1 / b1, b2 / b1, g1 / b1) def h(x): g = -n.rho[0] for i in range(0, nsv1): g = g + svc1[i][0] * (0.5 * (x[0] * sv1[i][1] + x[1] * sv1[i][2]) + 1) return g > 0 @eventAttr() def eventtest1(t, y): y0, y1 = y return a1 * y0 + a2 * y1 + g @eventAttr() def eventtest2(t, y): y0, y1 = y return b1 * y0 + b2 * y1 + g1 def labeltesttest(y): if eventtest1(0, y) > 0: return 2 elif eventtest2(0, y) > 0: return 1 else: return 0 ttest_list, ytest_list = simulation_ode_3([ ode_test(G[0], maxorder), ode_test(G[1], maxorder), ode_test(G[2], maxorder) ], [eventtest1, eventtest2], labeltesttest, y0, t_tuple, stepsize) for temp_y in y_list: y0_list = temp_y.T[0] y1_list = temp_y.T[1] plt.plot(y0_list, y1_list, c='b') for temp_y in ytest_list: y0_list = temp_y.T[0] y1_list = temp_y.T[1] plt.plot(y0_list, y1_list, c='r') plt.show() sum = 0 num = 0 def get_poly_pt(x): gene = generate_complete_polynomial(len(x), maxorder) val = [] for i in range(gene.shape[0]): val.append(1.0) for j in range(gene.shape[1]): val[i] = val[i] * (x[j]**gene[i, j]) poly_pt = np.mat(val) return poly_pt for ypoints in y_list: num = num + ypoints.shape[0] for i in range(ypoints.shape[0]): exact = modelist[labeltest(ypoints[i])](0, ypoints[i]) if f(ypoints[i]) == 1: predict = np.matmul(get_poly_pt(ypoints[i]), G[2].T) elif h(ypoints[i]) == 1: predict = np.matmul(get_poly_pt(ypoints[i]), G[1].T) else: predict = np.matmul(get_poly_pt(ypoints[i]), G[0].T) exact = np.mat(exact) diff = exact - predict c = 0 a = 0 b = 0 for j in range(diff.shape[1]): c = c + diff[0, j]**2 a = a + exact[0, j]**2 b = b + predict[0, j]**2 f1 = np.sqrt(c) f2 = np.sqrt(a) + np.sqrt(b) sum = sum + f1 / f2 return sum / num
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
def case5(): modetr = experiment5.get_modetr(0) event = experiment5.get_event(0) labeltest = experiment5.get_labeltest(0) y0 = [[-1,1],[1,4],[2,-3]] T = 5 stepsize = 0.01 maxorder = 2 boundary_order = 1 num_mode = 3 ep = 0.01 mergeep=0.01 method = 'piecelinear' t_list, y_list = simulation_ode_3(modetr, event, labeltest, y0, T, stepsize) P, G, (coeff1, coeff2, [first,second,third]) = infer_model( t_list, y_list, stepsize=stepsize, maxorder=maxorder, boundary_order=boundary_order, num_mode=num_mode, modelist=modetr, event=event, ep=ep, mergeep= mergeep,method=method, verbose=False, labeltest=labeltest) boundary = (coeff1, coeff2, [first,second,third]) d_avg = test_model( P, G, boundary, num_mode, y_list, modetr, event, maxorder, boundary_order, labeltest=labeltest) print(d_avg) print(coeff1[0]/coeff1[0],coeff1[1]/coeff1[0],coeff1[2]/coeff1[0]) print(coeff1[0]/coeff1[1],coeff1[1]/coeff1[1],coeff1[2]/coeff1[1]) print(coeff2[0]/coeff2[0],coeff2[1]/coeff2[0],coeff2[2]/coeff2[0]) print(coeff2[0]/coeff2[1],coeff2[1]/coeff2[1],coeff2[2]/coeff2[1]) @eventAttr() def eventtest1(t,y): y0, y1 = y return coeff1[0] * y0 + coeff1[1] * y1 + coeff1[2] @eventAttr() def eventtest2(t,y): y0, y1 = y return coeff2[0] * y0 + coeff2[1] * y1 + coeff2[2] def labeltesttest(y): if eventtest1(0,y)>0: return first elif eventtest2(0,y)>0: return second else: return third ttest_list, ytest_list = simulation_ode_3([ode_test(G[0],maxorder),ode_test(G[1],maxorder),ode_test(G[2],maxorder)], [eventtest1,eventtest2], labeltesttest, y0, T, stepsize) for i, temp_y in enumerate(y_list): y0_list = temp_y.T[0] y1_list = temp_y.T[1] if i == 0: plt.plot(y0_list,y1_list,c='b',label='Original') else: plt.plot(y0_list,y1_list,c='b') for i, temp_y in enumerate(ytest_list): y0_list = temp_y.T[0] y1_list = temp_y.T[1] if i == 0: plt.plot(y0_list,y1_list,c='r', label='Inferred',linestyle='--') else: plt.plot(y0_list,y1_list,c='r',linestyle='--') plt.xlabel('x1') plt.ylabel('x2') plt.legend() plt.show()