def NAR_Invoke(Proximity, VisualEvents):
SeenSomethingMissionRelevant = False
if Proximity:
NAR.AddInput("<obstacle --> [observed]>. :|:") #TODO also encode color
else:
if closed_gripper:
NAR.AddInput("<gripper --> [closed]>. :|:")
else:
NAR.AddInput("<gripper --> [open]>. :|:")
if len(VisualEvents) > 0:
Observed = False
for obj in VisibleObjects:
for v in VisualEvents:
print(v)
if obj in v:
NAR.AddInput(v)
SeenSomethingMissionRelevant = True
if not SeenSomethingMissionRelevant:
NAR.AddInput("(! <obstacle --> [observed]>). :|:") #TODO also encode color
action = None
if Proximity and not SeenSomethingMissionRelevant: #Don't allow forward as a reflex to not damage hardware
executions = NAR.AddInput("(! <obstacle --> [observed]>)! :|:")["executions"]
action = ExecMotorCommands(executions, DisableForward=True)
else:
executions = NAR.AddInput("<mission --> [progressed]>! :|:" if SeenSomethingMissionRelevant else "<{SELF} --> [moved]>! :|:")["executions"]
executions += NAR.AddInput("5")["executions"]
action = ExecMotorCommands(executions)
if action == "forward":
NAR.AddInput("<{SELF} --> [moved]>. :|:")
def add_nars(self, s):
r = NAR.AddInput(s)
p = "\n==>> " + s
if 'raw' in r:
p += "\n<< " + r['raw']
else:
p += "\n<< " + str(r)
now_info = p
p += self.last_info + "\n" + "#" * 90 + "\n" + p
self.last_info = now_info
print(p)
p += '\n' * 6
self.text_browser.setPlainText(p)
self.text_browser.moveCursor(Qt.QTextCursor.End)
def NARAddInput(narsese):
print(narsese)
return NAR.AddInput(narsese)
BackgroundKnowledge = """
//Let's say robot already learned to navigate from previous experiment:
//meaning to move forward if nothing is seen (due to innate boredom/forward goal)
<(<nothing --> [observed]> &/ ^up) =/> forward>.
//and to move left when an obstacle is in front (due to innate collision pain to avoid)
<(<obstacle --> [observed]> &/ ^left) =/> (! collision)>.
//Mission description:
//1. Find a bottle
<(<bottle --> [smallerX]> &/ ^left) =/> <mission --> [progressed]>>.
<(<bottle --> [largerX]> &/ ^right) =/> <mission --> [progressed]>>.
//2. Grab it if it's in front
<((open &/ <bottle --> [equalX]>) &/ ^down) =/> <mission --> [progressed]>>.
//3. Put it to other bottle
<((closed &/ <bottle --> [equalX]>) &/ ^say) =/> <mission --> [progressed]>>.
"""
k=0
for bg in BackgroundKnowledge.split("\n"):
bgstr = bg.strip()
if len(bgstr) > 0:
NAR.AddInput(bgstr)
NARAddInput("*motorbabbling=false")
while True:
#1. Actively retrieve sensor input
Proximity = scan()
VisualEvents = subprocess.check_output("python3 vision_to_narsese.py once", shell=True, stderr=subprocess.STDOUT).decode("utf-8").split('\n')
#2. Let NARS decide what to do
NAR_Invoke(Proximity, VisualEvents)
k+=1
class ARMA:
"""
AutoRegressive–Moving-Average model
"""
max_order = 20
lbd = 7
nar = NAR()
def crossValidation(self,
views,
n_Day,
serials_episodes_records,
n_fold,
type=0):
q_min = 0
p_min = 0
rmse_min = 1000
w_min = 0
for q in range(2, self.max_order):
for p in range(2, self.max_order):
# print p,q
w, rmse = self.run(p, q, views, n_Day,
serials_episodes_records, n_fold, type)
# print rmse
# print p,q,rmse
if rmse < rmse_min:
q_min = q
p_min = p
rmse_min = rmse
w_min = w
# print p_min,q_min,w_min,rmse_min
return p_min, q_min, w_min, rmse_min
def run(self,
p,
q,
views,
n_Day,
serials_episodes_records,
n_fold,
type=0):
# p,w, rmse = self.nar.crossValidation(views,n_Day,serials_episodes_records, n_fold)
w, rmse = self.nar.run(p, views, n_Day, serials_episodes_records,
n_fold, type)
if type == 0:
x, epsilon = self.getRegressionMatrix_s(p, q, w, views, n_Day,
serials_episodes_records)
else:
x, epsilon = self.getRegressionMatrix_m(p, q, w, views, n_Day,
serials_episodes_records)
# print x.shape,epsilon.shape
# for e in epsilon:
# print e
rmse = 0
w = 0
for k in range(1, n_fold + 1):
training_x, test_x = divideDataset(x, n_fold, k)
training_epsilon, test_epsilon = divideDataset(epsilon, n_fold, k)
temp_w = self.training(training_x, training_epsilon)
t, temp_x = self.getKernelMatrix(test_x)
phi = hstack([temp_x, test_epsilon])
predicted = dot(phi, temp_w)
rmse += RMSE(predicted, t)
w += temp_w
rmse /= n_fold
# print rmse
w /= n_fold
return w, rmse
def getKernelMatrix(self, x):
m, d = shape(x)
t = x[:, d - 1] # target vector
phi = ones([m, d])
phi[:, 1:] = x[:, 0:(d - 1)]
return t, phi
def training(self, x, epsilon):
t, temp_x = self.getKernelMatrix(x)
phi = hstack([temp_x, epsilon])
A = dot(phi.T, phi)
A += self.lbd * identity(A.shape[0])
w = linalg.inv(A) * dot(phi.T, t)
return w
def getRegressionMatrix_s(self, p, q, w, views, n_Day,
serials_episodes_records):
"""
get the Regression Matrix when predicting in the single time interval
:param p: order of AR
:param q: order of MA
:param w: coefficients(w0,w1,....,wp)
:param views: raw popularity data(serial*episode*time)
:param n_Day: # predict the popularity during the n-th time interval, range: 0,1,2,.....
serials_episodes_records: the number of records of each episode of every serial
"""
teleplay_num = len(serials_episodes_records)
s1, s2, s3 = shape(views)
temp_epsilon = array([zeros([s2, s3]) for i in range(s1)])
# calculate residual errors
for s in range(teleplay_num):
episodes_num = len(serials_episodes_records[s])
for nth in range(p, episodes_num):
if serials_episodes_records[s][nth + 1] < n_Day:
break
instance = views[s, nth - p:nth + 1,
n_Day] # t-p,...,t-2,t-1,t
if instance.min() == 0:
continue
row_max = instance.max()
instance = instance * 1.0 / row_max
temp_epsilon[s][nth][0] = instance[-1] - (
w[0] + dot(instance[:-1], w[1:]))
# generate matrix
x = []
epsilon = []
for s in range(teleplay_num):
episodes_num = len(serials_episodes_records[s])
for nth in range(p + q, episodes_num):
if serials_episodes_records[s][nth + 1] < n_Day:
break
instance = views[s, nth - p:nth + 1,
n_Day] # t-p,...,t-2,t-1,t
if instance.min() == 0:
continue
row_max = instance.max()
instance = instance * 1.0 / row_max
x.append(instance)
epsilon.append(temp_epsilon[s, nth - q:nth, 0])
return mat(x), mat(epsilon)
def getRegressionMatrix_m(self, p, q, w, views, n_Day,
serials_episodes_records):
"""
get the Regression Matrix when predicting in the multiple time interval
:param p: order of AR
:param q: order of MA
:param w: coefficients(w0,w1,....,wp)
:param views: raw popularity data(serial*episode*time)
:param n_Day: # predict the popularity during the n-th time interval, range: 0,1,2,.....
serials_episodes_records: the number of records of each episode of every serial
"""
teleplay_num = len(serials_episodes_records)
l = int(math.floor(sqrt(2 * max([p, q]) + 1.1)))
s1, s2, s3 = shape(views)
temp_epsilon = array([zeros([s2, s3]) for i in range(s1)])
# calculate residual errors
for s in range(teleplay_num):
episodes_num = len(serials_episodes_records[s])
for nth in range(l, episodes_num):
if serials_episodes_records[s][nth + 1] < n_Day:
break
instance = zeros([1, p + 1])[0, :] # t-p,...,t-2,t-1,t
instance[p] = views[s, nth, n_Day]
k = 1
for i in range(1, l + l):
for j in range(0, i):
if k >= (p + 1):
break
instance[p - k] = views[s, nth - i, n_Day + j]
k += 1
if instance.min() == 0:
continue
row_max = instance.max()
instance = instance * 1.0 / row_max
temp_epsilon[s][nth][0] = instance[-1] - (
w[0] + dot(instance[:-1], w[1:]))
# generate matrix
x = []
epsilon = []
for s in range(teleplay_num):
episodes_num = len(serials_episodes_records[s])
for nth in range(p + q, episodes_num):
if serials_episodes_records[s][nth + 1] < n_Day:
break
instance = zeros([1, p + 1])[0, :] # t-p,...,t-2,t-1,t
instance[p] = views[s, nth, n_Day]
row_e = zeros([1, q])[0, :]
k = 1
for i in range(1, l + l):
for j in range(0, i):
if k < (p + 1):
instance[p - k] = views[s, nth - i, 0]
if k < (q + 1):
row_e[q - k] = temp_epsilon[s, nth - i, 0]
if (k >= (p + 1)) and (k >= (q + 1)):
break
k += 1
if (instance.min() == 0) or (row_e.max() == 0):
continue
row_max = instance.max()
instance = instance * 1.0 / row_max
x.append(instance)
epsilon.append(row_e)
# print x,'abc'
return mat(x), mat(epsilon)
//What's expected by the robot to learn:
//move forward if nothing is seen (due to innate boredom/move goal)
//<((! <obstacle --> [observed]>) &/ ^forward) =/> <{SELF} --> [moved]>>.
//move left when an obstacle is in front (due to innate collision pain to avoid)
//<(<obstacle --> [observed]> &/ ^left) =/> (! <obstacle --> [observed]>)>.
//How to focus on objects (comment out if it should also be learned!)
<(<$1 --> [smallerX]> &/ ^left) =/> <$1 --> [equalX]>>.
<(<$1 --> [largerX]> &/ ^right) =/> <$1 --> [equalX]>>.
//Mission description:
//1. Pick a bottle if it's in front
<((<gripper --> [open]> &/ <bottle --> [equalX]>) &/ ^pick) =/> <mission --> [progressed]>>.
//2. Drop grabbed to other bottles
<((<gripper --> [closed]> &/ <bottle --> [equalX]>) &/ ^drop) =/> <mission --> [progressed]>>.
"""
NAR.AddInput("*babblingops=3")
NAR.AddInput("*motorbabbling=0.3")
NAR.AddInput("*setopname 1 ^left")
NAR.AddInput("*setopname 2 ^right")
NAR.AddInput("*setopname 3 ^forward")
NAR.AddInput("*setopname 4 ^pick")
NAR.AddInput("*setopname 5 ^drop")
k=0
for bg in BackgroundKnowledge.split("\n"):
bgstr = bg.strip()
if len(bgstr) > 0:
NAR.AddInput(bgstr)
while True:
#1. Actively retrieve sensor input