def sample_normal_in_one_level(self,level,Avees,theta,mu,scale=None):
"""
Modifies Avees and theta.
theta refers to the values in the cpa space.
"""
cpa_space = self.ms.L_cpa_space[level]
if mu is None:
mu = cpa_space.get_zeros_theta()
# ipshell('oops')
# raise NotImplementedError
else:
if len(mu) != cpa_space.d:
raise ValueError(mu.shape,cpa_space.d)
Sigma = self.L_cpa_space_covs[level].cpa_cov
# sample in the subspace
if scale is None:
theta[:] = draw_from_mvnormal(mean=mu,cov=Sigma)
else:
# ipshell('debug')
# raise ValueError("Is this code still current?")
# theta[:] = draw_from_mvnormal(mean=mu,cov=scale**2 * self.L_cpa_space_cov[level])
theta[:]=draw_from_mvnormal(mean=mu,cov=scale**2 * Sigma )
# move to the joint Lie algebra
cpa_space.theta2Avees(theta=theta,Avees=Avees)
def __call__(self,
theta_current,
theta_proposed,
prob_larger=0.1 * 0,
nullify_cells=None):
"""
Modifies theta_proposed
"""
if nullify_cells:
raise ValueError("That was a stupid idea. Don't do it.")
theta = theta_proposed
level = self.level
ms = self.ms
msp = self.msp
cpa_space = ms.L_cpa_space[level]
scale = self.scale
if prob_larger == 0.0:
pass
else:
u = np.random.random()
if u > 1 - prob_larger:
scale = self.scale * 10
else:
pass
if nullify_cells is None:
if 0:
msp.sample_normal_in_one_level(level,
cpa_space.Avees,
theta,
mu=theta_current,
scale=scale)
else:
if self.use_local == False:
covs = msp.L_cpa_space_covs[level]
cpa_cov = covs.cpa_cov
cpa_cov_diagonal = np.diag(cpa_cov.diagonal())
# ipshell('hi')
# 1/0
theta[:] = draw_from_mvnormal(mean=theta_current,
cov=scale**2 *
cpa_cov_diagonal)
else:
if cpa_space.dim_domain != 1 and cpa_space.tess != 'I':
raise NotImplementedError(cpa_space.tess)
cpa_space.theta2Avees(theta_current)
Avees = cpa_space.Avees
dim_range = cpa_space.dim_range
velTess = cpa_space.zeros_velTess()
cpa_space.Avees2velTess(Avees=Avees, velTess=velTess)
# vals +=1000*np.random.standard_normal(vals.shape)
# TODO: switch to a more intelligent proposal
V = cpa_space.local_stuff.vert_tess[:, :cpa_space.
dim_domain]
scale_local_proposal = self.scale_local_proposal
for i in range(1):
idx = np.random.random_integers(0, len(velTess) - 1)
# 1/0
# velTess[idx]+=1000*np.random.standard_normal(size=dim_range)
value = np.random.standard_normal(size=dim_range)
if cpa_space.dim_domain > 1:
s = scale_local_proposal * cpa_space.XMAXS.min()
velTess[idx] += s * value
else:
velTess[idx] += .01 * value
# velTess[idx]+=1*1.0*cpa_space.XMAXS.min()*np.random.standard_normal(size=dim_range)
# velTess[idx]+=1.0*cpa_space.XMAXS.min()*np.random.standard_normal(size=dim_range)
if any(cpa_space.zero_v_across_bdry):
# in principle, the constraint is only about
# normals to the bdry.
# however, if the variance is too large,
# the tangential comp can cause problems,
# in the sense it will drive everything
# to the corner. So use a hack: force zero.
# the projection later may still give values,
# but these are faily benign
# that are nonzero. Like I said, a hack.
for coo in range(cpa_space.dim_domain):
if V[idx, coo] in (0, cpa_space.XMAXS[coo]):
velTess[idx] = 0
# ipshell('hi')
# 1/0
cpa_space.project_velTess(velTess, velTess)
# ipshell('hi')
# 1/0
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
cpa_space.Avees2theta(theta=theta)
if nullify_cells is not None:
raise NotImplementedError
covs = msp.L_cpa_space_covs[level]
cpa_cov = covs.cpa_cov
pa_cov = covs.pa_cov
w = np.ones(cpa_space.d_no_constraints)
w.reshape(cpa_space.nC, -1)[nullify_cells] = 0
print w
pa_cov2 = np.diag(w).dot(pa_cov).dot(np.diag(w))
cpa_cov2 = cpa_space.B.T.dot(pa_cov2).dot(cpa_space.B)
theta[:] = draw_from_mvnormal(mean=theta_current * 0,
cov=scale**2 * cpa_cov2)
def sample_normal_in_one_level_velTess(self,level,Avees=None,
velTess=None,mu=None,scale=None):
"""
Modifies Avees and velTess.
Unless Avees is None, in which case only
vellTess and cpa_space.Avees are modified.
"""
if velTess is None:
raise ValueError
cpa_space = self.ms.L_cpa_space[level]
if mu is None:
mu = cpa_space.zeros_velTess().ravel()
else:
if len(mu) != cpa_space.d:
raise ValueError(mu.shape,cpa_space.d)
Sigma = self.L_cpa_space_covs[level].velTess_cov_byB
# Sigma = self.L_cpa_space_covs[level].velTess_cov
if Sigma.shape[0]!=len(mu):
raise ValueError(Sigma.shape,mu.shape)
if scale is None:
velTess.ravel()[:] = draw_from_mvnormal(mean=mu,cov=Sigma)
else:
# ipshell('debug')
# raise ValueError("Is this code still current?")
# theta[:] = draw_from_mvnormal(mean=mu,cov=scale**2 * self.L_cpa_space_cov[level])
velTess.ravel()[:]=draw_from_mvnormal(mean=mu,cov=scale**2 * Sigma )
if cpa_space.dim_domain==1:
"""
Terrible hack... I need to project it instead
But the differece is mild anyway.
"""
if scale is None:
S = Sigma
else:
S = Sigma * scale**2
if any(cpa_space.zero_v_across_bdry):
# velTess[0,:]=velTess[-1,:]=0
Nv = len(velTess)
idx_y=[0,Nv-1]
velTess[idx_y,:]=0
if 0:
idx_x=np.delete(np.arange(Nv),idx_y)
Sxgiveny = cond_gaussian.compute_cond_cov(Sigma,idx_x,idx_y)
# the mean and the value we condition on are zero
# so don't bother to compute cond mean. It is just zeros.
velTess[idx_x,0]=draw_from_mvnormal(mu[idx_x],Sxgiveny)
# 1/0
# ipshell('h');1/0
if Avees is not None:
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
def __call__(self,theta_current,theta_proposed,prob_larger=0.1*0,
nullify_cells=None):
"""
Modifies theta_proposed
"""
if nullify_cells:
raise ValueError("That was a stupid idea. Don't do it.")
theta = theta_proposed
level=self.level
ms = self.ms
msp = self.msp
cpa_space = ms.L_cpa_space[level]
scale=self.scale
if prob_larger==0.0:
pass
else:
u = np.random.random()
if u>1-prob_larger:
scale = self.scale * 10
else:
pass
if nullify_cells is None:
if 0:
msp.sample_normal_in_one_level(level,cpa_space.Avees,theta,
mu=theta_current,scale=scale)
else:
if self.use_local == False:
covs=msp.L_cpa_space_covs[level]
cpa_cov = covs.cpa_cov
cpa_cov_diagonal = np.diag(cpa_cov.diagonal())
# ipshell('hi')
# 1/0
theta[:]=draw_from_mvnormal(mean=theta_current,cov=scale**2 * cpa_cov_diagonal)
else:
if cpa_space.dim_domain!=1 and cpa_space.tess !='I':
raise NotImplementedError(cpa_space.tess)
cpa_space.theta2Avees(theta_current)
Avees=cpa_space.Avees
dim_range = cpa_space.dim_range
velTess = cpa_space.zeros_velTess()
cpa_space.Avees2velTess(Avees=Avees,velTess=velTess)
# vals +=1000*np.random.standard_normal(vals.shape)
# TODO: switch to a more intelligent proposal
V = cpa_space.local_stuff.vert_tess[:,:cpa_space.dim_domain]
scale_local_proposal=self.scale_local_proposal
for i in range(1):
idx = np.random.random_integers(0,len(velTess)-1)
# 1/0
# velTess[idx]+=1000*np.random.standard_normal(size=dim_range)
value = np.random.standard_normal(size=dim_range)
if cpa_space.dim_domain>1:
s=scale_local_proposal*cpa_space.XMAXS.min()
velTess[idx]+=s*value
else:
velTess[idx]+=.01*value
# velTess[idx]+=1*1.0*cpa_space.XMAXS.min()*np.random.standard_normal(size=dim_range)
# velTess[idx]+=1.0*cpa_space.XMAXS.min()*np.random.standard_normal(size=dim_range)
if any(cpa_space.zero_v_across_bdry):
# in principle, the constraint is only about
# normals to the bdry.
# however, if the variance is too large,
# the tangential comp can cause problems,
# in the sense it will drive everything
# to the corner. So use a hack: force zero.
# the projection later may still give values,
# but these are faily benign
# that are nonzero. Like I said, a hack.
for coo in range(cpa_space.dim_domain):
if V[idx,coo] in (0,cpa_space.XMAXS[coo]):
velTess[idx]=0
# ipshell('hi')
# 1/0
cpa_space.project_velTess(velTess,velTess)
# ipshell('hi')
# 1/0
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
cpa_space.Avees2theta(theta=theta)
if nullify_cells is not None:
raise NotImplementedError
covs=msp.L_cpa_space_covs[level]
cpa_cov = covs.cpa_cov
pa_cov = covs.pa_cov
w = np.ones(cpa_space.d_no_constraints)
w.reshape(cpa_space.nC,-1)[nullify_cells]=0
print w
pa_cov2=np.diag(w).dot(pa_cov).dot(np.diag(w))
cpa_cov2=cpa_space.B.T.dot(pa_cov2).dot(cpa_space.B)
theta[:]=draw_from_mvnormal(mean=theta_current*0,cov=scale**2 * cpa_cov2 )
