def run(self):
data4D = open("data/RBFN_params.txt", 'w+')
d = Data() #new object of selected data
self.data = d.getData()
if (self.file_name == 'data/train4dAll.txt'):
x, y = d.getTrainData4d()
elif (self.file_name == 'data/train6dAll.txt'):
x, y = d.getTrainData6d()
else:
x, y = d.getTrainData4d()
#normalize training data
x = d.normalize_input(x)
y = d.normalize_Y(y)
#get input parameters
cross_rate = float(self.e4.get())
mutation_rate = float(self.e3.get())
pop_size = int(self.e2.get())
iterations = int(self.e1.get())
if (self.file_name != 'data/RBFN_params.txt'):
#training weight --start--
rbf = RBF(3, 50, 1) #new object of RBFN
x_dim, w, centers, beta = rbf.get_parameter()
ga = genetic(len(x), cross_rate, mutation_rate, pop_size, x_dim, x,
y, w, centers, beta) #new object of ga
temp = 100
for i in range(0, iterations):
fitness = ga.F()
best_idx = np.argmin(fitness)
if (fitness[best_idx] < temp):
temp = fitness[best_idx]
best_DNA = ga.get_best_DNA(best_idx)
ga.update(best_DNA)
#training weight --end--
ga.w, ga.centers, ga.beta = ga.get_data()
rbf.set_parameter(ga.w, ga.centers, ga.beta)
rbf.train(x, y)
z1 = rbf.predict(x, rbf.get_weight())
y = d.inverse_normalize_Y(y)
z = d.inverse_normalize_Y(z1)
dim, output_W, output_centers, output_beta = rbf.get_parameter()
for i in range(0, len(output_W)): #save trained parameters
print(str(output_W[i][0]),
str(output_centers[i][0]),
str(output_centers[i][1]),
str(output_centers[i][2]),
str(output_beta[i][0]),
file=data4D)
data4D.close()
else:
load_w, load_centers, load_beta = d.load_parameters()
rbf = RBF(3, len(load_centers), 1)
rbf.set_parameter(load_w, load_centers, load_beta)
m = map(self.data) #new object of map
self.edges = m.getEdges()
c = car(self.data[0]) #new object of car
Y = c.getPosition() #get the position of car
self.mapping(self.edges)
while (Y[1] < 43):
self.draw_car(c.getPosition()) #draw car on the window
c.sensor(self.edges) #calculate the distane of front, left, right
F, R, L = c.getDistance()
input_x = []
if (self.file_name == 'data/train4dAll.txt'):
input_x.append([F, R, L])
elif (self.file_name == 'data/train6dAll.txt'):
pos = c.getPosition()
input_x.append([pos[0], pos[1], F, R, L])
else:
input_x.append([F, R, L])
input_x = np.array(input_x)
input_x = d.normalize_input(input_x)
wheel = rbf.predict(input_x,
rbf.get_weight()) #predict the degree of wheel
wheel = d.inverse_normalize_Y(wheel)
if wheel < -40:
wheel = -40
elif wheel > 40:
wheel = 40
c.setWheel(wheel)
c.update_car_direction()
c.update_car_pos()
def run(self):
d = Data()
self.data = d.getData()
if(self.file_name == 'data/train4dAll.txt'):
x, y = d.getTrainData4d()
elif(self.file_name == 'data/train6dAll.txt'):
x, y = d.getTrainData6d()
else:
x, y = d.getTrainData4d()
print('---------')
print('data:',self.data)
x = d.normalize_input(x)
y = d.normalize_Y(y)
print('x:', x)
print('y:', y)
learn_rate1 = float(self.e4.get())
learn_rate2 = float(self.e3.get())
pop_size = int(self.e2.get())
iterations = int(self.e1.get())
if(self.file_name != 'data/RBFN_params.txt'):
data4D = open("data/RBFN_params.txt",'w+')
#training weight --start--
rbf = RBF(3, 50, 1)
x_dim, w, centers, beta = rbf.get_parameter()
tStart = time.time()
my_pso = pso(pop_size, iterations, learn_rate1, learn_rate2,x_dim, x, y, w, centers, beta)
my_pso.initialize()
print('pso!!!!!!!!!!!!!')
fitness = my_pso.training()
tEnd = time.time()
print('It cost', (tEnd - tStart), ' sec.')
print('fitness: ', fitness)
theta, w, centers, beta = my_pso.get_parameter()
rbf.set_parameter(theta, w, centers, beta)
rbf.train(x,y)
z1 = rbf.predict(x, rbf.get_weight())
print('predict_z', z1)
y = d.inverse_normalize_Y(y)
z = d.inverse_normalize_Y(z1)
print('inverse_z', z)
dim, output_W, output_centers, output_beta = rbf.get_parameter()
print('output_W:',output_W)
for i in range(0, len(output_W)):
print(str(output_W[i][0]), str(output_centers[i][0]),str(output_centers[i][1]),str(output_centers[i][2]), str(output_beta[i][0]),file=data4D)
data4D.close()
else:
load_w, load_centers, load_beta = d.load_parameters()
rbf = RBF(3, len(load_centers), 1)
rbf.set_parameter(0.5, load_w, load_centers, load_beta)
print('rbf_W:', rbf.get_weight())
m = map(self.data)
self.edges = m.getEdges()
c = car(self.data[0])
Y = c.getPosition()
self.mapping(self.edges)
data4 = open("/Users/chengshu-yu/Documents/train4D.txt",'w+')
data6 = open("/Users/chengshu-yu/Documents/train6D.txt",'w+')
while(Y[1] < 43):
self.draw_car(c.getPosition())
print('car direction:', c.getDirection(), ', wheel degree: ', c.getWheel())
c.sensor(self.edges)
F, R, L = c.getDistance()
input_x = []
if(self.file_name == 'data/train4dAll.txt'):
input_x.append([F, R, L])
elif(self.file_name == 'data/train6dAll.txt'):
pos = c.getPosition()
input_x.append([pos[0], pos[1], F, R, L])
else:
input_x.append([F, R, L])
input_x = np.array(input_x)
input_x = d.normalize_input(input_x)
print('input_x:', input_x)
wheel = rbf.predict(input_x, rbf.get_weight())
print('predict_wheel:', wheel)
wheel = d.inverse_normalize_Y(wheel)
print('inverse wheel:', wheel)
if wheel < -40:
wheel = -40
elif wheel > 40:
wheel = 40
print('-----------front right left:', input_x, '-----------')
print('-----------predict wheel:', wheel, '-----------')
c.setWheel(wheel)
c.update_car_direction()
c.update_car_pos()
print(str(F),' ',str(R),' ',str(L),' ',str(c.getDirection()),file=data4)
print(str(c.getPosX()),'',str(c.getPosY()),' ',str(F),' ',str(R),' ',str(L),' ',str(c.getDirection()),file=data6)
data4.close()
data6.close()