def case3(): y0 = [[5, 5, 5], [2, 2, 2]] stepsize = 0.01 maxorder = 2 boundary_order = 1 num_mode = 2 T = 5 ep = 0.01 mergeep = 0.01 method = 'merge' t_list, y_list = simulation_ode_2(get_fvdp3(0), get_event1(0), y0, T, stepsize) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) print("start") x0 = np.zeros(num_mode * A.shape[1] * b.shape[1]) re = infer_optimizationm(x0, A, b, num_mode) print(re.fun) print(re.success) print(re.x) A, b1, b2, Y, ytuple = diff_method_backandfor(t_list, y_list, maxorder, stepsize) A, b1, b2, _ = seg_droprow(A, b1, b2, ep) x0 = np.zeros(num_mode * A.shape[1] * b1.shape[1]) re = infer_optimizationm(x0, A, b1, num_mode) print(re.fun) print(re.success) print(re.x)
def case2(): y0 = [[5, 5, 5], [2, 2, 2]] stepsize = 0.01 maxorder = 2 boundary_order = 1 num_mode = 2 T = 5 ep = 0.01 mergeep = 0.01 method = 'merge' t_list, y_list = simulation_ode_2(get_fvdp3(0), get_event1(0), y0, T, stepsize) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) np.savetxt("data/A2.txt", A, fmt='%8f') np.savetxt("data/b2.txt", b, fmt='%8f') # print(y_list) # P,G,C = infer_model( # t_list, y_list, stepsize=stepsize, maxorder=maxorder, boundary_order=boundary_order, # num_mode=num_mode, modelist=fvdp3, event=event1, ep=ep, mergeep= mergeep, method=method, verbose=False) y1 = [[3, 3, 3], [4, 4, 4]] t_test_list, y_test_list = simulation_ode_2(get_fvdp3(0), get_event1(0), y0, T, stepsize) YT, FT = diff(t_list + t_test_list, y_list + y_test_list, dynamics.fvdp3_3) # np.savetxt("data/YT"+str(n)+".txt",YT,fmt='%8f') # np.savetxt("data/FT"+str(n)+".txt",FT,fmt='%8f') np.savetxt("data/YT2.txt", YT, fmt='%8f') np.savetxt("data/FT2.txt", FT, fmt='%8f')
def case3(): y0 = [[5,5,5],[2,2,2]] t_tuple = 5 stepsize = 0.01 maxorder = 2 t_list, y_list = simulation_ode(fvdp3_3, y0, t_tuple, stepsize, eps=0) draw3D(y_list) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) # A, b1, b2, Y = diff_method_backandfor(t_list, y_list, maxorder, stepsize) # print(b1) # print(b2) P,G,D = infer_dynamic_modes_new(t_list, y_list, stepsize, maxorder, 0.02) print(P) print(G) print(D)
def case(): y0 = [[5, 5, 5], [2, 2, 2]] stepsize = 0.01 maxorder = 2 T = 5 t_list, y_list = simulation_ode(fvdp3_1, y0, T, stepsize, eps=0.02) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) clf = linear_model.LinearRegression(fit_intercept=False) clf.fit(A, b) g = clf.coef_ print(g) A, b, Y = diff_method_new_6(t_list, y_list, maxorder, stepsize) clf.fit(A, b) g = clf.coef_ print(g)
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 case2(): mode2 = get_mode2(0) event1 = get_event1(0) y0 = [[99.5, 80], [97.5, 100]] y1 = [[100.5, 90], [96, 80]] stepsize = 0.1 maxorder = 1 boundary_order = 1 num_mode = 2 T = 50 ep = 0.01 mergeep = 0.01 method = 'piecelinear' t_list, y_list = simulation_ode_2(mode2, event1, y0, T, stepsize, noise=0) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) np.savetxt("data/A1.txt", A, fmt='%8f') np.savetxt("data/b1.txt", b, fmt='%8f')
def case1(): y0 = [[4, 0.1, 3.1, 0], [5.9, 0.2, -3, 0], [4.1, 0.5, 2, 0], [6, 0.7, 2, 0]] y0_test = [[4.6, 0.13, 2, 0], [5.3, 0.17, -2, 0]] T = 5 stepsize = 0.01 ep = 0.01 maxorder = 2 boundary_order = 1 num_mode = 2 method = 'tolmerge' t_list, y_list = simulation_ode_2(mmode, event1, y0, T, stepsize) t_test_list, y_test_list = simulation_ode_2(mmode, event1, y0_test, T, stepsize) A, b, Y = diff_method_new(t_list, y_list, maxorder, stepsize) np.savetxt("data/A4.txt", A, fmt='%8f') np.savetxt("data/b4.txt", b, fmt='%8f') YT, FT = diff(t_list + t_test_list, y_list + y_test_list, dynamics.modeex4) np.savetxt("data/YT4.txt", YT, fmt='%8f') np.savetxt("data/FT4.txt", FT, fmt='%8f')
def case(y0, t_tuple, stepsize, maxorder, modelist, event, ep, method): t_list, y_list = simulation_ode_2(modelist, event, 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, 0.01) P, D = dropclass(P, G, D, A, b, Y, 0.01, stepsize) # print(len(P)) print(G) y = [] x = [] 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], 3: Y[P[0][j], 2]}) 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], 3: Y[P[1][j], 2]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 10 -r 1 -b 0 -q') m = svm_train(prob, param) svm_save_model('model_file', m) nsv = m.get_nr_sv() svc = m.get_sv_coef() sv = m.get_SV() g = -m.rho[0] a1 = 0 a2 = 0 a3 = 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] a3 = a3 + svc[i][0] * 0.5 * sv[i][3] g = g + svc[i][0] print(a1 / a1, a2 / a1, a3 / a1, g / a1) def f(x): return a1 * x[0] + a2 * x[1] + a3 * x[2] + g > 0 @eventAttr() def eventtest(t, y): y0, y1, y2 = y return a1 * y0 + a2 * y1 + a3 * y2 + g ttest_list, ytest_list = simulation_ode_2( [ode_test(G[0], maxorder), ode_test(G[1], maxorder)], eventtest, y0, t_tuple, stepsize) ax = plt.axes(projection='3d') for temp_y in y_list[0:1]: y0_list = temp_y.T[0] y1_list = temp_y.T[1] y2_list = temp_y.T[2] ax.plot3D(y0_list, y1_list, y2_list, c='b') for temp_y in ytest_list[0:1]: y0_list = temp_y.T[0] y1_list = temp_y.T[1] y2_list = temp_y.T[2] ax.plot3D(y0_list, y1_list, y2_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]): if event(0, ypoints[i]) > 0: exact = modelist[0](0, ypoints[i]) else: exact = modelist[1](0, ypoints[i]) if f(ypoints[i]) == 1: predict = np.matmul(get_poly_pt(ypoints[i]), G[0].T) else: predict = np.matmul(get_poly_pt(ypoints[i]), G[1].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 case(y0, t_tuple, stepsize, maxorder, modelist, event, ep, method): t_list, y_list = simulation_ode_2(modelist, event, 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, 0.01) P, D = dropclass(P, G, D, A, b, Y, 0.01, stepsize) # print(len(P)) # print(G) y = [] x = [] 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]}) 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]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 2 -r 1 -c 10 -b 0 -q') m = svm_train(prob, param) svm_save_model('model_file', m) # p_label, p_acc, p_val = svm_predict(y, x, m) nsv = m.get_nr_sv() svc = m.get_sv_coef() sv = m.get_SV() 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)**2 return g > 0 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]): if event(0, ypoints[i]) > 0: exact = modelist[0](0, ypoints[i]) else: exact = modelist[1](0, ypoints[i]) if f(ypoints[i]) == 1: predict = np.matmul(get_poly_pt(ypoints[i]), G[0].T) else: predict = np.matmul(get_poly_pt(ypoints[i]), G[1].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 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 case1(): # y0 = [[1,3],[-1,-2],[-3,-5],[2,4]] # t_tuple = [(0,20),(0,10),(0,15),(0,15)] y0 = [[1,3],[-1,-2]] t_tuple = 20 stepsize = 0.01 order = 2 maxorder = 2 # start = time.time() t_list, y_list = simulation_ode(mode2_1, y0, t_tuple, stepsize, eps=0) # for temp_y in y_list: # y0_list = temp_y.T[0] # y1_list = temp_y.T[1] # plt.plot(y0_list,y1_list,'b') # plt.show() 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) y = [] x = [] 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]}) 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]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 10 -b 0 ') m = svm_train(prob, param) svm_save_model('model_file', m) yt = [-1,-1,1,1] xt = [{1:1, 2:1},{1:-1, 2:1},{1:1, 2:-1},{1:-1, 2:-1}] 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] print("a1",a1/a1) print("a2",a2/a1) print("g",g/a1) # ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap='rainbow') # plt.show() dim = G[0].shape[0] A = generate_complete_polynomial(dim,maxorder) def odepre(t,y): # print("in") basicf = [] for i in range(0,A.shape[0]): ap = 1 for j in range(0,A.shape[1]): ap = ap*(y[j]**A[i][j]) basicf.append(ap) b = np.array(basicf) dydt = np.zeros(dim) if a1 * y[0] + a2 * y[1] + g > 0: for l in range(0,dim): dydt[l] = G[0][l].dot(b) else: for l in range(0,dim): dydt[l] = G[1][l].dot(b) # print("out") return dydt py0 = [[2,4],[-1,-3]] pt_tuple = [(0,10),(0,10)] start = time.time() print("origin") tp_list, yp_list = simulation_ode(mode2_1, py0, pt_tuple, stepsize, eps=0) end1 = time.time() print("predict") tpre_list, ypre_list = simulation_ode(odepre, py0, pt_tuple, stepsize, eps=0) end2 = time.time() print("simutime",end1-start) print("predtime",end2-end1) for temp_y in yp_list: y0_list = temp_y.T[0] y1_list = temp_y.T[1] plt.plot(y0_list,y1_list,'b') for temp_y in ypre_list: y0_list = temp_y.T[0] y1_list = temp_y.T[1] plt.plot(y0_list,y1_list,'r') 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 compare(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')
def case(y0, t_tuple, stepsize, maxorder, modelist, event, ep, method): # print('Simulating') t_list, y_list = simulation_ode_2(modelist, event, y0, t_tuple, stepsize) draw2D(y_list) if method == "new": # print('Classifying') 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) print(G) P, D = dropclass(P, G, D, A, b, Y, ep, stepsize) # print('Number of modes:', len(P)) y = [] x = [] 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]}) 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]}) prob = svm_problem(y, x) param = svm_parameter('-t 1 -d 1 -c 10 -r 1 -b 0 -q') m = svm_train(prob, param) svm_save_model('model_file', 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] def f(x): return a1 * x[0] + a2 * x[1] + g > 0 print(a1 / a1, a2 / a1, g / a1) sum = 0 num = 0 @eventAttr() def eventtest(t, y): y0, y1 = y return a1 * y0 + a2 * y1 + g ttest_list, ytest_list = simulation_ode_2( [ode_test(G[0], maxorder), ode_test(G[1], maxorder)], eventtest, y0, t_tuple, 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') else: plt.plot(y0_list, y1_list, c='r') plt.xlabel('x1') plt.ylabel('x2') plt.legend() plt.show() 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]): if event(0, ypoints[i]) > 0: exact = modelist[0](0, ypoints[i]) else: exact = modelist[1](0, ypoints[i]) if f(ypoints[i]) == 1: predict = np.matmul(get_poly_pt(ypoints[i]), G[0].T) else: predict = np.matmul(get_poly_pt(ypoints[i]), G[1].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