def case2():
y0 = [[20]]
t_tuple = 4
stepsize = 0.001
order = 8
start = time.time()
t_points, y_list = simulation_ode(dydx2, y0, t_tuple, stepsize, eps=0)
end_simulation = time.time()
result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(
t_points, y_list, stepsize, order)
end_inference = time.time()
t_points, y_list_test = simulation_ode(ode_test(result_coef, order),
y0,
t_tuple,
stepsize,
eps=0)
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:
plt.scatter(t_points, temp_y, s=0.1, c='r')
for temp_y in y_list_test:
plt.scatter(t_points, temp_y, s=0.1, c='b')
plt.show()
def case3():
y0 = [[5, -3], [2, 0], [-2, 3]]
t_tuple = 10
stepsize = 0.01
order = 3
start = time.time()
t_points, y_list = simulation_ode(fvdp2, y0, t_tuple, stepsize, eps=0)
end_simulation = time.time()
result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(
t_points, 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)
draw2D(y_list)
def case1():
y0 = [[5], [1], [2], [3], [0], [-1], [-2]]
t_tuple = 4
stepsize = 0.01
order = 3
start = time.time()
t_points, y_list = simulation_ode(dydx1, y0, t_tuple, stepsize, eps=0)
end_simulation = time.time()
result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(
t_points, 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)
draw(t_points, y_list)
def case3():
y0 = [[1,0],[0.4,0],[0.5,0],[2.3,0]]
t_tuple = 3
stepsize = 0.005
order = 2
start = time.time()
t_points, y_list = simulation_ode(conti_test, y0, t_tuple, stepsize, eps=0)
end_simulation = time.time()
final_A_mat, final_b_mat = get_coef(t_points, y_list, order, stepsize)
end_coedf = time.time()
# x0 = np.ones((2*final_A_mat.shape[1], final_b_mat.shape[1]))*0.1
# x0 = np.ones((1,2*final_A_mat.shape[1]*final_b_mat.shape[1]))*0.1
print(3*final_A_mat.shape[1]*final_b_mat.shape[1])
x0 = np.random.uniform(-5,5,[3*final_A_mat.shape[1]*final_b_mat.shape[1]])
# print(x0)
x1 = np.array([0,0,0,1,0,0,0,0,0,0,0,1, -1,0,0,5,0,5,0,0,0,0,0,1, 0,0,0,-1,0,4,0,0,0,0,0,1])
# pr = cProfile.Profile()
# pr.enable()
results = infer_optimization3(x0, final_A_mat, final_b_mat)
# p = Stats(pr)
# p.strip_dirs()
# p.sort_stats('cumtime')
# p.print_stats(100)
end_optimization = time.time()
# print(results)
print(results.x)
print(lambda_three_modes(final_A_mat,final_b_mat)(x0))
print(lambda_three_modes(final_A_mat,final_b_mat)(results.x))
print(lambda_three_modes(final_A_mat,final_b_mat)(x1))
print(results.success)
print(results.message)
print("Simulation time: ", end_simulation-start)
print("Optimazation time: ", end_optimization-end_coedf)
# draw2D_dots(y_list)
# start_svm = time.time()
A_row = final_A_mat.shape[0]
A_col = final_A_mat.shape[1]
b_col = final_b_mat.shape[1]
x1, x2, x3 = np.array_split(results.x,3)
x1 = np.mat(x1.reshape([A_col,b_col],order='F'))
y1 = np.matmul(final_A_mat,x1) - final_b_mat
y1 = np.multiply(y1,y1)
y1 = y1.sum(axis=1)
x2 = np.mat(x2.reshape([A_col,b_col],order='F'))
y2 = np.matmul(final_A_mat,x2) - final_b_mat
y2 = np.multiply(y2,y2)
y2 = y2.sum(axis=1)
x3 = np.mat(x3.reshape([A_col,b_col],order='F'))
y3 = np.matmul(final_A_mat,x3) - final_b_mat
y3 = np.multiply(y3,y3)
y3 = y3.sum(axis=1)
modet = np.zeros(A_row)
for i in range(0,A_row):
if y1[i] < y2[i] and y1[i] < y3[i]:
modet[i] = 1
if y2[i] < y1[i] and y2[i] < y3[i]:
modet[i] = 2
if y3[i] < y1[i] and y3[i] < y2[i]:
modet[i] = 3
modett = np.array_split(modet,len(y0))
print(A_row)
for i in range(0,len(y0)):
plt.plot(t_points,y_list[i])
plt.plot(t_points[:len(modett[i])],modett[i])
plt.show()
def case1():
y0 = [[0,3]]
t_tuple = 20
stepsize = 0.01
order = 2
start = time.time()
t_points, y_list = simulation_ode(mode2_1, y0, t_tuple, stepsize, eps=0)
end_simulation = time.time()
final_A_mat, final_b_mat = get_coef(t_points, y_list, order, stepsize)
end_coedf = time.time()
# x0 = np.ones((2*final_A_mat.shape[1], final_b_mat.shape[1]))*0.1
# x0 = np.ones((1,2*final_A_mat.shape[1]*final_b_mat.shape[1]))*0.1
print(2*final_A_mat.shape[1]*final_b_mat.shape[1])
x0 = np.random.uniform(-1,1,[2*final_A_mat.shape[1]*final_b_mat.shape[1]])
# print(x0)
x1 = np.array([0,0,0,-0.26,0.26,0,0,0,0, 0,0,1.0, 0,0,0,-0.26,0.26,0, 0,0,0, 0,0,-1.0])
# pr = cProfile.Profile()
# pr.enable()
results = infer_optimization(x0, final_A_mat, final_b_mat)
# p = Stats(pr)
# p.strip_dirs()
# p.sort_stats('cumtime')
# p.print_stats(100)
end_optimization = time.time()
# print(results)
print(results.x)
print(lambda_two_modes(final_A_mat,final_b_mat)(x0))
print(lambda_two_modes(final_A_mat,final_b_mat)(results.x))
print(lambda_two_modes(final_A_mat,final_b_mat)(x1))
print(results.success)
print(results.message)
print("Simulation time: ", end_simulation-start)
print("Optimazation time: ", end_optimization-end_coedf)
# draw2D_dots(y_list)
start_svm = time.time()
A_row = final_A_mat.shape[0]
A_col = final_A_mat.shape[1]
b_col = final_b_mat.shape[1]
x1, x2 = np.array_split(results.x,2)
x1 = np.mat(x1.reshape([A_col,b_col],order='F'))
y1 = np.matmul(final_A_mat,x1) - final_b_mat
y1 = np.multiply(y1,y1)
y1 = y1.sum(axis=1)
x2 = np.mat(x2.reshape([A_col,b_col],order='F'))
y2 = np.matmul(final_A_mat,x2) - final_b_mat
y2 = np.multiply(y2,y2)
y2 = y2.sum(axis=1)
modet = np.zeros(A_row)
for i in range(0,A_row):
if y1[i] < y2[i]:
modet[i] = 1
else:
modet[i] = -1
# plt.plot(t_points,y_list[0])
# plt.plot(t_points[:A_row],modet)
# plt.show()
y = []
x = []
for i in range(0,A_row):
y.append(modet[i])
x_0 = y_list[0][i][0]
x_1 = y_list[0][i][1]
x.append({1:x_0, 2:x_1})
prob = svm_problem(y, x)
param = svm_parameter('-t 0 -c 200 -b 1')
m = svm_train(prob, param)
svm_save_model('model_file', m)
end_svm_training = time.time()
nsv = m.get_nr_sv()
y0cof = 0.0
y1cof = 0.0
for i in range(0,nsv):
if m.get_SV()[i].__contains__(1):
y0cof = y0cof + m.get_sv_coef()[i][0]*m.get_SV()[i][1]
if m.get_SV()[i].__contains__(2):
y1cof = y1cof + m.get_sv_coef()[i][0]*m.get_SV()[i][2]
print("y0 coef: ", y0cof)
print("y1 coef: ", y1cof)
print("constant: ", -m.rho[0])
mode1_y0 = []
mode1_y1 = []
mode2_y0 = []
mode2_y1 = []
for i in range(0,A_row):
if modet[i] > 0 :
mode1_y0.append(y_list[0][i][0])
mode1_y1.append(y_list[0][i][1])
else:
mode2_y0.append(y_list[0][i][0])
mode2_y1.append(y_list[0][i][1])
p_label, p_acc, p_val = svm_predict(y,x,m)
end_svm_predict = time.time()
print(p_acc)
print("SVM training time: ", end_svm_training-start_svm)
print("SVM predict time: ", end_svm_predict-end_svm_training)
print("total time: ", end_svm_predict-start)
plt.scatter(mode1_y0,mode1_y1,c='r',s=0.1)
plt.scatter(mode2_y0,mode2_y1,c='g',s=0.1)
yy1 = np.arange(-3,3,0.001)
yy0 = (m.rho[0] - y1cof*yy1)/y0cof
plt.plot(yy0,yy1)
plt.show()