def decision(image):
[label, score] = requestLabel(
image) # Output of API converted into a label and a score
print("Oooooh that's a", label, ". I'm", score, "percent sure!")
params = memory.getValue(
label
) # Array of paramaters [alpha, beta, survival, ex alpha, ex beta]
# return alpha = coward coefficient; beta = aggressor coefficient
alpha = params[0]
beta = params[1]
adjustment = params[2]
return alpha, beta, label, adjustment
def error(totalTime, label):
# We calculate our error based on the change in Survival Time = Duration 1000 sec
# if survival time decreases
# error = (old time - new time)/total
# if error is -ve --> Positive stimulus (Survival Time Has Increased)
# increase beta and decrease alpha
# if error is +ve --> Negative stimulus (Survival Time Has Decreased)
# increase alpha and decrease beta
params = memory.getValue(label)
delta = -1 * params[2]
survivalAdjustment = (delta) / (totalTime)
# if delta = -100 and totalTime = 1000 error should be 0.1
return survivalAdjustment, delta
def update_positions():
global imagex, imagey, survival_time, E, alpha, beta, index, ABL, imgplot, currenttime, survivalprogress, timeprogress, recentindex
sr.x = bug.x + sr.x_disp * sin(bug.angle) + sr.y_disp * cos(bug.angle)
sr.y = bug.y + -sr.x_disp * cos(bug.angle) + sr.y_disp * sin(bug.angle)
sl.x = bug.x + sl.x_disp * sin(bug.angle) + sl.y_disp * cos(bug.angle)
sl.y = bug.y - sl.x_disp * cos(bug.angle) + sl.y_disp * sin(bug.angle)
# sr.x = bug.x + sr.x_disp*cos(bug.angle-pi/2) - sr.y_disp*sin(bug.angle-pi/2)
# sr.y = bug.y + sr.x_disp*sin(bug.angle-pi/2) + sr.y_disp*cos(bug.angle-pi/2)
#
# sl.x = bug.x + sl.x_disp*cos(bug.angle-pi/2) - sl.y_disp*sin(bug.angle-pi/2)
# sl.y = bug.y + sl.x_disp*sin(bug.angle-pi/2) + sl.y_disp*cos(bug.angle-pi/2)
# totalTime = duration/ms
totalTime = 1000
index = distances(ABL, bugx, bugy)
imagex = ABL[index][4]
imagey = ABL[index][5]
apiradius = 1500
contactradius = 50
if ((bug.x - imagex)**2 + (bug.y - imagey)**2) > apiradius:
if ABL[index][6] == 1:
ABL[index][6] = 0
E = -80
sr.E = E
sl.E = E
sbr.E = E
sbl.E = E
if ((bug.x - imagex)**2 + (bug.y - imagey)**2) <= apiradius and (
(bug.x - imagex)**2 + (bug.y - imagey)**2) >= contactradius:
# ABL[index].append(1)
if ABL[index][6] == 0 and recentindex != index:
ABL[index][6] = 1
recentindex = index
[alpha, beta, label, adjustment] = decision(image[index])
img = Image.open(image[index])
if imgplot != 1:
imgplot.remove()
plt.subplot(122)
imgplot = plt.imshow(img)
plt.axis('off')
syn_ll_c.g_synmax = g_synmaxval * alpha
syn_rr_c.g_synmax = g_synmaxval * alpha
syn_ll_a.g_synmax = g_synmaxval * beta
syn_rr_a.g_synmax = g_synmaxval * beta
# print("The alpha value before backpropagation is", memory.bug_memory[label][0]) #print alpha and beta before adjustment
# print("The beta value before backpropagation is", memory.bug_memory[label][1])
print("The object is within", apiradius)
if ABL[index][0] > 0.5:
E = 0
elif ABL[index][0] < 0.5:
E = -80
sr.E = E
sl.E = E
sbr.E = E
sbl.E = E
print("The E value is", E)
if ((bug.x - imagex)**2 + (bug.y - imagey)**2) < contactradius:
ABL[index][6] = 0
[alpha, beta, label, adjustment] = decision(image[index])
img = Image.open(image[index])
if imgplot != 1:
imgplot.remove()
plt.subplot(122)
imgplot = plt.imshow(img)
plt.axis('off')
syn_ll_c.g_synmax = g_synmaxval * alpha
syn_rr_c.g_synmax = g_synmaxval * alpha
syn_ll_a.g_synmax = g_synmaxval * beta
syn_rr_a.g_synmax = g_synmaxval * beta
print("The alpha value before backpropagation is",
memory.bug_memory[label]
[0]) #print alpha and beta before adjustment
print("The beta value before backpropagation is",
memory.bug_memory[label][1])
[survivalAdjustment, delta] = error(totalTime, label)
memory.adjustValue(label, survivalAdjustment)
ABL[index][0] = memory.bug_memory[label][0]
ABL[index][1] = memory.bug_memory[label][1]
print("The alpha value after backpropagation is",
memory.bug_memory[label]
[0]) #print alpha and beta after adjustment
print("The beta value after backpropagation is",
memory.bug_memory[label][1])
print(memory.getValue(label))
syn_ll_c.g_synmax = g_synmaxval * memory.bug_memory[label][0]
syn_rr_c.g_synmax = g_synmaxval * memory.bug_memory[label][0]
syn_ll_a.g_synmax = g_synmaxval * memory.bug_memory[label][1]
syn_rr_a.g_synmax = g_synmaxval * memory.bug_memory[label][1]
if ABL[index][0] > 0.5:
E = 0
elif ABL[index][0] < 0.5:
E = -80
sr.E = E
sl.E = E
sbr.E = E
sbl.E = E
print("The E value is", E)
#update_decision(label) # Implement ud()
survival_time -= delta # Get Current clock time
survivalprogress.append(survival_time)
timeprogress.append(time.time() - currenttime)
print("The survival time is", survival_time, "and has changed by",
-1 * delta, "during this iteration\n")
# if survival_time <= 0:
# print('Elapsed Time: ' , time.time() - currenttime, 'seconds')
# plt.figure(2)
# plot(timeprogress, survivalprogress)
imagex = randint(-map_size + 10, map_size - 10)
imagey = randint(-map_size + 10, map_size - 10)
ABL[index][4] = imagex
ABL[index][5] = imagey
# else:
# E=0
# sr.E = E
# sl.E = E
# sbr.E = E
# sbl.E = E
if (bug.x < -map_size):
bug.x = -map_size
bug.angle = pi - bug.angle
if (bug.x > map_size):
bug.x = map_size
bug.angle = pi - bug.angle
if (bug.y < -map_size):
bug.y = -map_size
bug.angle = -bug.angle
if (bug.y > map_size):
bug.y = map_size
bug.angle = -bug.angle
sr.imagex = imagex
sr.imagey = imagey
sl.imagex = imagex
sl.imagey = imagey
