def run_test():
print 'starting ....'
Y = read_image_data('data/observations.arff')
print 'image reading is finished...'
print len(Y)
#Allocate an n-by-k matrix, X, to hold intrinsic vectors
X , K = list(), 2
for _ in xrange(len(Y)):
X.append([0.0*x for x in xrange(K)])
w,h= 64,48
us_mlp = UnsupervisedMLP([4, 12, 12, 3])
us_mlp.set_image_dim(w, h)
us_mlp.train(Y, X, K)
print 'USMLP training is finished'
plot_intrinsic(X, w, h)
print 'intrinsic plotting is done'
#2nd MLP
normalize(X)
print 'reading action file'
A = read_action_data('data/actions.arff')
print len(A), len(X)
for i in xrange(len(A)-1):
row = A[i]
row.extend(X[i])
row.extend(X[i+1])
del A[-1]
print 'training mlp with action '
mlp = MLP([6,6,2])
mlp.train1(A, K)
print 'training done'
#operate the crane now!
#'a':[1.0,0.0,0.0,0.0],'c': [0.0,0.0,1.0,0.0]
s = [1.0,0.0,0.0,0.0]
s.extend(X[0])
for i in range(5):
predict=mlp.predict(s)
s[4] = predict[0]
s[5] = predict[1]
# image_generation(us_mlp, s, w, h, 'frame'+str(i))
#up
s[0]=0 ; s[2]=1.0
for i in range(5,10):
predict=mlp.predict(s)
s[4] = predict[0]
s[5] = predict[1]
image_generation(us_mlp, s, w, h, 'frame'+str(i+1))
