def CostFunction(X):
# original_initial_orientation = np.array([[-1.0,-0.000194820380622269,0.0],[0.0,1.0,0.0]])
original_initial_orientation = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
current_initial_orientation = create_initial_orientation(
X, original_initial_orientation)
# print('current initial orientation: ',current_initial_orientation )
variables = [
main_tra.t, main_tra.acceleration, main_tra.rotvel,
main_tra.initial_position, current_initial_orientation,
main_tra.initial_velocity
]
current_trajectory = Trajectory(variables)
current_trajectory.solver()
tra_cur = current_trajectory.getdisp()
np.savetxt('tra_cur.csv', tra_cur, fmt='%.5f', delimiter=',')
#-----------------------------------------------------------------------------------------------------------------------
# Compute Error between Reference and current using the Selected method in ErrorType and generate MinErrorList
#-----------------------------------------------------------------------------------------------------------------------
ErrorTotal = ComputeError(tra_ref, tra_cur)
# MinErrorList will be written in DE_Log.txt to keep track of the evolution of the computed Minimum Error.
# Last entry of MinErrorList corresponds to minimum error found up that point in the Optimization run.
global best_IO
global MinErrorList
if MinErrorList == []:
MinErrorList.extend([ErrorTotal])
best_IO.extend([current_initial_orientation])
elif ErrorTotal < MinErrorList[-1]:
MinErrorList.extend([ErrorTotal])
best_IO.extend([current_initial_orientation])
else:
MinErrorList.extend([MinErrorList[-1]])
best_IO.extend([best_IO[-1]])
#-------------------------------------------------------------------------------------------------------------------
# Create a log File to keep track evolution
#-------------------------------------------------------------------------------------------------------------------
Log_file = 'DE_Log.txt'
variables = ['alpha1', 'alpha2', 'phi']
FileDelimiter = ','
OptParameterNames = [variables[i] for i in range(len(X))]
if not os.path.isfile(Log_file):
with open(Log_file, 'w+') as Log:
for names in OptParameterNames:
print(names)
Log.write(names + FileDelimiter)
Log.write('Error' + FileDelimiter)
Log.write('Min_Error' + '\n')
OptParameterValues = [X[i] for i in range(len(X))]
with open(Log_file, 'a') as Log_1:
for Vals in OptParameterValues:
Log_1.write(str(Vals) + FileDelimiter)
Log_1.write(str(ErrorTotal) + FileDelimiter)
Log_1.write(str(MinErrorList[-1]) + '\n')
print('MinErTotal', MinErrorList[-1], 'CIO', best_IO[-1][0, :],
best_IO[-1][1, :])
# print('MinErTotal',MinErrorList[-1])
return ErrorTotal
bounds = [[0, 360]]
#-------------------------------------------------------------------------------------------------------
#Define global reference:
#ref_trajectory = read_frame(main_video.fin,main_video.input_videos_folder,main_video.trans)#Reference_displacements #Numpy array ntsx3
#tra_ref = ref_trajectory.reading_frame()
variables_ref = [
main_tra.t, main_tra.acceleration, main_tra.rotvel,
main_tra.initial_position, main_tra.initial_orientation,
main_tra.initial_velocity
]
ref_trajectory = Trajectory(variables_ref)
ref_trajectory.solver()
tra_ref = ref_trajectory.getdisp()
np.savetxt('tra_ref.csv', tra_ref, fmt='%f', delimiter=',')
#Optimization = CostFunction([45.,45.,180.])
#print('TotalError: ',Optimization)
#-------------------------------------------------------------------------------------------------------------------
# Optimization
#-------------------------------------------------------------------------------------------------------------------
Opt_SimplifiedModel = differential_evolution(CostFunction,
bounds,
strategy=Strategy,
maxiter=MaxGenerations,
popsize=Np,
tol=0.01,
mutation=F,
