def case2():
y0 = [[0,0],[1,0],[0.3,0],[2.7,0]]
t_tuple = 2
stepsize = 0.001
order = 2
maxorder = 2
# start = time.time()
t_list, y_list = simulation_ode(conti_test, y0, t_tuple, stepsize, eps=0)
# end_simulation = time.time()
# result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(t_list, y_list, stepsize, order)
# end_inference = time.time()
# print(result_coef)
# print()
# print("Total time: ", end_inference-start)
# print("Simulation time: ", end_simulation-start)
# print("Calc-diff time: ", calcdiff_time)
# print("Pseudoinv time: ", pseudoinv_time)
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()
P,G,D = infer_dynamic_modes_new(t_list, y_list, stepsize, maxorder, 0.01)
print(P)
print(G)
print(D)
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 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 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 case7():
y0 = [[4,0.1,3.1,0],[5.9,0.2,-3,0],[4.1,0.5,2,0],[6,0.7,2,0]]
t_tuple = [(0,5),(0,5),(0,5),(0,5)]
stepsize = 0.01
maxorder = 2
# start = time.time()
t_list, y_list = simulation_ode_2([mmode1, mmode2], event3, 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,'b')
# plt.show()
A, b, Y = diff_method_new1(t_list, y_list, maxorder, stepsize)
P,G,D = infer_dynamic_modes_new(t_list, y_list, stepsize, maxorder, 0.03)
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], 3:Y[P[0][j],2], 4:Y[P[0][j],3]})
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], 4:Y[P[1][j],3]})
prob = svm_problem(y, x)
param = svm_parameter('-t 1 -d 1 -c 10 -r 1 -b 1')
m = svm_train(prob, param)
svm_save_model('model_file', m)
# print("pred")
p_label, p_acc, p_val = svm_predict(y, x, m,'-q')
# print(p_label)
print('pre',p_acc[0])
# print(p_val)
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
a3 = 0
a4 = 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]
a4 = a4 + svc[i][0] * 0.5 * sv[i][4]
g = g + svc[i][0]*1
def case6():
y0 = [[0,1],[0,2],[0,3],[0,4],[0,5]]
t_tuple = 20
stepsize = 0.01
maxorder = 3
# start = time.time()
t_list, y_list = simulation_ode_2([mode1, mode2], event2, 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,'b')
plt.show()
# clfs, boundary = infer_multi_ch.infer_multi_linear_new(t_list, y_list, stepsize, maxorder)
# for clf in clfs:
# print('g',clf.coef_)
# print(boundary)
A, b, Y = diff_method_new1(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]})
y0_list = []
y1_list = []
for j in range(0,len(P[0])):
y0_list.append(Y[P[0][j],0])
y1_list.append(Y[P[0][j],1])
plt.scatter(y0_list,y1_list,s=1)
y0_list = []
y1_list = []
for j in range(0,len(P[1])):
y0_list.append(Y[P[1][j],0])
y1_list.append(Y[P[1][j],1])
plt.scatter(y0_list,y1_list,s=1)
prob = svm_problem(y, x)
param = svm_parameter('-t 1 -d 2 -r 1 -c 10 -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 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)**2
return g
x = np.linspace(-5,5,100)
y = np.linspace(-5,5,100)
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
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 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