def runNN(name):
'''
Takes the Brent trajectories as input, shuffles them, and then runs the NN regression algorithm
'''
rs = ReadSetupFile()
#state, command = getStateAndCommandData(BrentTrajectoriesFolder)
#stateAll, commandAll = dicToArray(state), dicToArray(command)
#print ("old:", stateAll[0])
stateAll, commandAll = stateAndCommandDataFromTrajs(
loadStateCommandPairsByStartCoords(pathDataFolder + "TrajRepository/"))
#stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(BrentTrajectoriesFolder))
#print ("len:", len(commandAll[0]))
stateAll = np.vstack(np.array(stateAll))
commandAll = np.vstack(np.array(commandAll))
#print ("len global:", len(commandAll))
#print ("new:", commandAll[0])
#this works because both shuffles generate the same order, due to the seed
np.random.seed(0)
np.random.shuffle(stateAll)
np.random.seed(0)
np.random.shuffle(commandAll)
print("nombre d'echantillons: ", len(stateAll))
fa = NeuralNet(rs.inputDim, rs.outputDim)
fa.getTrainingData(stateAll, commandAll)
fa.train()
fa.saveTheta(rs.NNpath + name + ".theta")
def UnitTestNNController():
'''
Tests the approximated command obtained from training states
'''
rs = ReadSetupFile()
fa = initNNController(rs)
stateAll, commandAll = stateAndCommandDataFromTrajs(
loadStateCommandPairsByStartCoords(pathDataFolder + "TrajRepository/"))
#stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(BrentTrajectoriesFolder))
#print ("len:", len(commandAll[0]))
state = np.vstack(np.array(stateAll))
command = np.vstack(np.array(commandAll))
fa.getTrainingData(state, command)
fa.train()
fa.saveTheta("test")
fa.loadTheta("test")
for el in os.listdir(BrentTrajectoriesFolder):
for i in range(len(state)):
if rd.random() < 0.06:
outNN = fa.computeOutput(np.array(state[i]))
print("Real :", command[i])
print("Learn :", outNN)
def runRBFN(name,fromStruct):
'''
Takes the Brent trajectories as input, shuffles them, and then runs the RBFN regression algorithm
'''
rs = ReadSetupFile()
#state, command = getStateAndCommandData(BrentTrajectoriesFolder)
#stateAll, commandAll = dicToArray(state), dicToArray(command)
#print ("old:", stateAll[0])
stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(pathDataFolder + "TrajRepository/"))
#stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(BrentTrajectoriesFolder))
#print ("len:", len(commandAll[0]))
stateAll = np.vstack(np.array(stateAll))
commandAll = np.vstack(np.array(commandAll))
#print ("len global:", len(commandAll))
#print ("new:", commandAll[0])
#this works because both shuffles generate the same order, due to the seed
np.random.seed(0)
np.random.shuffle(stateAll)
np.random.seed(0)
np.random.shuffle(commandAll)
print("nombre d'echantillons: ", len(stateAll))
fa = rbfn(rs.numfeats,rs.inputDim,rs.outputDim)
fa.getTrainingData(stateAll, commandAll)
if fromStruct == True:
initFromExistingStruct(fa,rs.RBFNpath+name+".struct")
else:
initFromData(fa,rs.RBFNpath+name+".struct")
fa.train_reg_rbfn(rs.lamb)
fa.saveTheta(rs.RBFNpath + name+".theta")
def runNN(name):
'''
Takes the Brent trajectories as input, shuffles them, and then runs the NN regression algorithm
'''
rs = ReadSetupFile()
#state, command = getStateAndCommandData(BrentTrajectoriesFolder)
#stateAll, commandAll = dicToArray(state), dicToArray(command)
#print ("old:", stateAll[0])
stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(pathDataFolder + "TrajRepository/"))
#stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(BrentTrajectoriesFolder))
#print ("len:", len(commandAll[0]))
stateAll = np.vstack(np.array(stateAll))
commandAll = np.vstack(np.array(commandAll))
#print ("len global:", len(commandAll))
#print ("new:", commandAll[0])
#this works because both shuffles generate the same order, due to the seed
np.random.seed(0)
np.random.shuffle(stateAll)
np.random.seed(0)
np.random.shuffle(commandAll)
print("nombre d'echantillons: ", len(stateAll))
fa = NeuralNet(rs.inputDim,rs.outputDim)
fa.getTrainingData(stateAll, commandAll)
fa.train()
fa.saveTheta(rs.NNpath + name+".theta")
def UnitTestController(fileName):
'''
Tests the approximated command obtained from training states
'''
rs = ReadXmlFile(fileName)
fa = initController(rs)
stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(pathDataFolder + "TrajRepository/"))
#stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(BrentTrajectoriesFolder))
#print ("len:", len(commandAll[0]))
state = np.vstack(np.array(stateAll))
command = np.vstack(np.array(commandAll))
fa.getTrainingData(state, command)
if(rs.regression=="RBFN"):
fa.train(rs.lamb)
else:
fa.train()
fa.saveTheta("test")
fa.loadTheta("test")
for _ in os.listdir(BrentTrajectoriesFolder):
for i in range(len(state)):
if rd.random()<0.06:
outNN = fa.computeOutput(np.array(state[i]))
print("Real :", command[i])
print("Learn :",outNN)
def runRBFN(name, fromStruct):
"""
Takes the Brent trajectories as input, shuffles them, and then runs the RBFN regression algorithm
"""
rs = ReadSetupFile()
# state, command = getStateAndCommandData(BrentTrajectoriesFolder)
# stateAll, commandAll = dicToArray(state), dicToArray(command)
# print ("old:", stateAll[0])
stateAll, commandAll = stateAndCommandDataFromTrajs(
loadStateCommandPairsByStartCoords(pathDataFolder + "TrajRepository/")
)
# stateAll, commandAll = stateAndCommandDataFromTrajs(loadStateCommandPairsByStartCoords(BrentTrajectoriesFolder))
# print ("len:", len(commandAll[0]))
stateAll = np.vstack(np.array(stateAll))
commandAll = np.vstack(np.array(commandAll))
# print ("len global:", len(commandAll))
# print ("new:", commandAll[0])
# this works because both shuffles generate the same order, due to the seed
np.random.seed(0)
np.random.shuffle(stateAll)
np.random.seed(0)
np.random.shuffle(commandAll)
print("nombre d'echantillons: ", len(stateAll))
fa = rbfn(rs.numfeats, rs.inputDim, rs.outputDim)
fa.getTrainingData(stateAll, commandAll)
if fromStruct == True:
initFromExistingStruct(fa, rs.RBFNpath + name + ".struct")
else:
initFromData(fa, rs.RBFNpath + name + ".struct")
fa.train_reg_rbfn(rs.lamb)
fa.saveTheta(rs.RBFNpath + name + ".theta")