def calc_ll(self,theta):
"""
Computes the ll per measurments.
"""
cpa_space = self.cpa_space
src = self.src
dst = self.dst
params_flow_int = self.params_flow_int
transformed = self.transformed
sigma_lm = self.sigma_lm
ll = self.ll
if src.shape[1]!=cpa_space.dim_domain:
raise ValueError(src.shape)
cpa_space.theta2Avees(theta)
cpa_space.update_pat()
cpa_space.calc_T_fwd(pts = src, out=transformed,
**params_flow_int)
if transformed.shape != src.shape:
raise ValueError(transformed.shape , src.shape)
if 1:
calc_err_per_sample(transformed.gpu,dst.gpu,self.err.gpu)
# print np.allclose(transformed.gpu.get()-dst.gpu.get(),
# self.err.gpu.get())
calc_ll_per_sample(self.err.gpu,sigma_lm ,ll.gpu)
# print np.allclose((-0.5/(sigma_lm**2)*
# (transformed.gpu.get()-dst.gpu.get())**2).sum(axis=1),
# ll.gpu.get())
# idx = (dst.cpu<0.5).nonzero()[0][-1]
# idx = 499
#
# weight_it(ll.gpu,np.int32(idx))
# ipshell('hi')
# 1/0
else:
raise Exception("BAD IDEA")
ll_by_der = self.ll_by_der
der_dt = 0.002
calc_err_by_der_per_sample(transformed.gpu,dst.gpu,self.err_by_der.gpu,
der_dt)
calc_ll_by_der_per_sample(self.err_by_der.gpu,.1 ,ll_by_der.gpu)
ll = ll_by_der
def calc_ll(self,theta):
"""
Returns the ll per measurments.
"""
cpa_space = self.cpa_space
src = self.src
dst = self.dst
params_flow_int = self.params_flow_int
transformed = self.transformed
sigma_lm = self.sigma_lm
ll = self.ll
if src.shape[1]!=cpa_space.dim_domain:
raise ValueError(src.shape)
# Avees = cpa_space.theta2Avees(theta)
# pat = self._pat
# pat.update(Avees=Avees)
# cpa_space.calc_T(pat,pts = src, mysign=1,out=transformed,
# **params_flow_int)
cpa_space.theta2Avees(theta)
cpa_space.update_pat()
cpa_space.calc_T_fwd(pts = src,out=transformed,
**params_flow_int)
if transformed.shape != src.shape:
raise ValueError(transformed.shape , src.shape)
calc_err_per_sample(transformed.gpu,dst.gpu,self.err.gpu)
# print np.allclose(transformed.gpu.get()-dst.gpu.get(),
# self.err.gpu.get())
calc_ll_per_sample(self.err.gpu,sigma_lm ,ll.gpu)
# print np.allclose((-0.5/(sigma_lm**2)*
# (transformed.gpu.get()-dst.gpu.get())**2).sum(axis=1),
# ll.gpu.get())
return ll
def calc_ll(self, theta):
"""
Returns the ll per measurments.
"""
cpa_space = self.cpa_space
src = self.src
dst = self.dst
params_flow_int = self.params_flow_int
transformed = self.transformed
sigma_lm = self.sigma_lm
ll = self.ll
if src.shape[1] != cpa_space.dim_domain:
raise ValueError(src.shape)
# Avees = cpa_space.theta2Avees(theta)
# pat = self._pat
# pat.update(Avees=Avees)
# cpa_space.calc_T(pat,pts = src, mysign=1,out=transformed,
# **params_flow_int)
cpa_space.theta2Avees(theta)
cpa_space.update_pat()
cpa_space.calc_T_fwd(pts=src, out=transformed, **params_flow_int)
if transformed.shape != src.shape:
raise ValueError(transformed.shape, src.shape)
calc_err_per_sample(transformed.gpu, dst.gpu, self.err.gpu)
# print np.allclose(transformed.gpu.get()-dst.gpu.get(),
# self.err.gpu.get())
calc_ll_per_sample(self.err.gpu, sigma_lm, ll.gpu)
# print np.allclose((-0.5/(sigma_lm**2)*
# (transformed.gpu.get()-dst.gpu.get())**2).sum(axis=1),
# ll.gpu.get())
return ll
