def lossfunction__old1(tr, sub):
#@Borja(orginal)1
# Where to create the loss using data from the subject class
# First simple loss function---------------------------------------
# Based in simple distance to subject base
(xt, yt), radius = tr.pf.circle
(xs, ys), radius = sub.circle
distance = int(np.sqrt(np.power(xt - xs, 2) + np.power(yt - ys, 2)))
# Second loss function---------------------------------------------
# Based in normal probability density function of particles for
# position and detection size
(x, y), (h, w), a = sub.rot_box
p = tr.pf.p
p_star = tr.pf.p_star
p_mean = tr.pf.p_mean
# optimizacion --> no calcular constantemente mean y std, sino hacerlo antes de entrar aqui
# loss = -x -y -vx -vy
loss = - np.log(normpdf(x, np.mean(p[:, 0]), np.std(p[:, 0]))) \
- np.log(normpdf(y, np.mean(p[:, 1]), np.std(p[:, 1]))) \
- np.log(normpdf(x - p_star[0], np.mean(p[:, 4]), np.std(p[:, 4]))) \
- np.log(normpdf(y - p_star[1], np.mean(p[:, 5]), np.std(p[:, 5])))
#- np.log(normpdf(sub.h, np.mean(p[:, 4]), np.std(p[:, 4])))
debug_flag=0
if debug_flag:
"""
print '###'
print normpdf(p_star[1] - y, np.mean(p[:, 5]), np.std(p[:, 5]))
print x
print y
"""
"""
print '----'
print 'x, y, h: %s, %s, %s' % (x, y, sub.h)
#print 'x: %s' % - np.log(normpdf(x, np.mean(p[:, 0]), np.std(p[:, 0])))
print 'vx: %s' % - np.log(normpdf(p_star[0] - x, np.mean(p[:, 4]), np.std(p[:, 4])))
print 'vy: %s' % - np.log(normpdf(p_star[1] - y, np.mean(p[:, 5]), np.std(p[:, 5])))
print '----'
"""
return loss, distance
def lossfunction__old1(tr, sub):
#@Borja(orginal)1
# Where to create the loss using data from the subject class
# First simple loss function---------------------------------------
# Based in simple distance to subject base
(xt, yt), radius = tr.pf.circle
(xs, ys), radius = sub.circle
distance = int(np.sqrt(np.power(xt - xs, 2) + np.power(yt - ys, 2)))
# Second loss function---------------------------------------------
# Based in normal probability density function of particles for
# position and detection size
(x, y), (h, w), a = sub.rot_box
p = tr.pf.p
p_star = tr.pf.p_star
p_mean = tr.pf.p_mean
# optimizacion --> no calcular constantemente mean y std, sino hacerlo antes de entrar aqui
# loss = -x -y -vx -vy
loss = - np.log(normpdf(x, np.mean(p[:, 0]), np.std(p[:, 0]))) \
- np.log(normpdf(y, np.mean(p[:, 1]), np.std(p[:, 1]))) \
- np.log(normpdf(x - p_star[0], np.mean(p[:, 4]), np.std(p[:, 4]))) \
- np.log(normpdf(y - p_star[1], np.mean(p[:, 5]), np.std(p[:, 5])))
#- np.log(normpdf(sub.h, np.mean(p[:, 4]), np.std(p[:, 4])))
debug_flag = 0
if debug_flag:
"""
print '###'
print normpdf(p_star[1] - y, np.mean(p[:, 5]), np.std(p[:, 5]))
print x
print y
"""
"""
print '----'
print 'x, y, h: %s, %s, %s' % (x, y, sub.h)
#print 'x: %s' % - np.log(normpdf(x, np.mean(p[:, 0]), np.std(p[:, 0])))
print 'vx: %s' % - np.log(normpdf(p_star[0] - x, np.mean(p[:, 4]), np.std(p[:, 4])))
print 'vy: %s' % - np.log(normpdf(p_star[1] - y, np.mean(p[:, 5]), np.std(p[:, 5])))
print '----'
"""
return loss, distance
def lossfunction(tr, sub):
# Where to create the loss using data from the subject class
# First simple loss function
# Based in simple distance to subject base
"""
(xt, yt), radius = tr.subject.circle
(xs, ys), radius = sub.circle
loss = int(np.sqrt(np.power(xt - xs, 2) + np.power(yt - ys, 2)))
"""
# Second loss function
# Based in normal probability density function of particles for
# position and detection size
(x, y), (h, w), a = sub.rot_box
p = tr.pf.p
"""
loss = stats.norm.pdf(
h,
np.mean(p[:, 2]),
np.std(p[:, 2])
) * stats.multivariate_normal.pdf(
np.array([x, y]),
np.mean(p[:, 0:2], axis=0),
np.cov(p[:, 0:2].T)
)
"""
loss = stats.norm.pdf(
x,
np.mean(p[:, 0]),
np.std(p[:, 0])
) * stats.norm.pdf(
y,
np.mean(p[:, 1]),
np.std(p[:, 1])
)
if loss == 0.0:
loss = 100000000000000000000000000000000000
else:
loss = 1 / loss
return loss
def updatescore(self, score):
if len(self.score) > self.score_max:
self.score.pop(0)
self.score.append(score)
self.score_sigma = np.std(self.score)
self.score_mu = np.mean(self.score)
def updatescore(self, score):
if len(self.score) > self.score_max:
self.score.pop(0)
self.score.append(score)
self.score_sigma = np.std(self.score)
self.score_mu = np.mean(self.score)