def cmaesf(x):
evaluation = evaluationInter.evaluationInter(runtime=runtime)
evaluation.updateHyperParams(x)
result = evaluation.evaluation()
y0 = -np.ones((1,1))*result[0]
# seek minimum
return y0[0]
def bo_penalty_f(x):
evaluation = evaluationInter.evaluationInter(runtime=runtime)
evaluation.updateHyperParams(x)
result = evaluation.evaluation()
if result[1] < 0:
result[0] = result[0] + result[1]/500.0 # penalty item
return result
def cmaes_initiateSafeSet(inputDimension):
while True:
x = (0.5-np.random.random((1, inputDimension)))*10
y = cmaesf(x)
evaluation = evaluationInter.evaluationInter(runtime=runtime)
evaluation.updateHyperParams(x)
result = evaluation.evaluation()
y = result[0]
if y < -0.7 and y > -0.8 and result[2] is False:
print('initial x :')
print(x)
print('initial y :')
print(y)
return x
return x
rbfn = RBFN.RBFN(1, 5, 3, runtime) # indim, numCenters, outdim, time
rbfn.setHyperParams(optimalX.reshape(5, 3))
timeInterval = safeopt.linearly_spaced_combinations([(0, runtime)],
runtime * 10)
priorities = np.zeros((3, 0))
for time in timeInterval:
priorities = np.column_stack((priorities, rbfn.calOutput(time).T))
print(priorities)
plt.figure()
plt.plot(timeInterval, priorities[0], color='r')
plt.plot(timeInterval, priorities[1], color='g')
plt.plot(timeInterval, priorities[2], color='b')
plt.ioff()
displayResult = evaluationInter.evaluationInter(runtime=runtime)
displayResult.updateHyperParams(optimalX)
print(displayResult.evaluation())
JointAngleCurve = displayResult.getJointAngleCurve()
JointTauCurve = displayResult.getJointTauCurve()
JointAccelerationCurve = displayResult.getJointAccelerationCurve()
txt_data = np.column_stack(
(timeInterval, priorities.T, JointAccelerationCurve.T, JointAngleCurve.T))
np.savetxt(
"/home/zhou/lee/safeLearningPriorities/mat_plot/robot_state/robotStates.txt",
txt_data)
'''np.savetxt("/home/zhou/lee/safeLearningPriorities/mat_plot/robot_state/JointAccelerationCurve.txt", JointAccelerationCurve.T)
np.savetxt("/home/zhou/lee/safeLearningPriorities/mat_plot/robot_state/priorities.txt", priorities.T)
def f_c(x):
evaluation = evaluationInter.evaluationInter(runtime=runtime)
evaluation.updateHyperParams(x)
result = evaluation.evaluation()
return np.hstack([np.ones((1,1))*result[0], np.ones((1,1))*result[1]])