G = cholmod.into_matrix_type(G)
M = np.zeros(n)
kappa = pm.Exponential('kappa', 1, value=3)
alpha = pm.DiscreteUniform('alpha', 1, 10, value=2.)
@pm.deterministic
def Q(kappa=kappa, alpha=alpha):
out = operators.mod_frac_laplacian_precision(Ctilde, G, kappa, alpha,
cholmod)
return out
# Nailing this ahead of time reduces time to compute logp from .18 to .13s for n=25000.
pattern_products = cholmod.pattern_to_products(Q.value)
# @pm.deterministic
# def pattern_products(Q=Q):
# return cholmod.pattern_to_products(Q)
@pm.deterministic
def precision_products(Q=Q, p=pattern_products):
return cholmod.precision_to_products(Q, **p)
S = pymc_objects.SparseMVN('S', M, precision_products, cholmod)
def map_S(S):
# Make a map
# Operator generation
Ctilde = cholmod.into_matrix_type(Ctilde)
G = cholmod.into_matrix_type(G)
M = np.random.normal(size=n)
kappa = pm.Exponential('kappa',1,value=3)
alpha = pm.DiscreteUniform('alpha',1,10,value=2.)
@pm.deterministic
def Q(kappa=kappa, alpha=alpha):
out = operators.mod_frac_laplacian_precision(Ctilde, G, kappa, alpha, cholmod)
return out
# Nailing this ahead of time reduces time to compute logp from .18 to .13s for n=25000.
pattern_products = cholmod.pattern_to_products(Q.value)
# @pm.deterministic
# def pattern_products(Q=Q):
# return cholmod.pattern_to_products(Q)
@pm.deterministic
def precision_products(Q=Q, p=pattern_products):
return cholmod.precision_to_products(Q, **p)
S=pymc_objects.SparseMVN('S',M, precision_products, cholmod)
vals = X[:,0]
vars = pm.rgamma(4,4,size=n)/10
Qobs = sparse.csc_matrix((n,n))
def make_model(X):
neighbors, triangles, trimap, b = spherical.triangulate_sphere(X)
# spherical.plot_triangulation(X,neighbors)
# Matrix generation
triangle_areas = [spherical.triangle_area(X, t) for t in triangles]
Ctilde = spherical.Ctilde(X, triangles, triangle_areas)
C = spherical.C(X, triangles, triangle_areas)
G = spherical.G(X, triangles, triangle_areas)
# Operator generation
Ctilde = cholmod.into_matrix_type(Ctilde)
G = cholmod.into_matrix_type(G)
# amp is the overall amplitude. It's a free variable that will probably be highly confounded with kappa.
amp = pm.Exponential('amp', .0001, value=100)
# A constant mean.
m = pm.Uninformative('m', value=0)
@pm.deterministic(trace=False)
def M(m=m, n=len(X)):
"""The mean vector"""
return np.ones(n) * m
kappa = pm.Exponential('kappa', 1, value=3)
alpha = pm.DiscreteUniform('alpha', 1, 10, value=2., observed=True)
@pm.deterministic(trace=False)
def Q(kappa=kappa, alpha=alpha, amp=amp):
out = operators.mod_frac_laplacian_precision(
Ctilde, G, kappa, alpha, cholmod) / np.asscalar(amp)**2
return out
# Nailing this ahead of time reduces time to compute logp from .18 to .13s for n=25000.
pattern_products = cholmod.pattern_to_products(Q.value)
# @pm.deterministic
# def pattern_products(Q=Q):
# return cholmod.pattern_to_products(Q)
@pm.deterministic(trace=False)
def precision_products(Q=Q, p=pattern_products):
try:
return cholmod.precision_to_products(Q, **p)
except cholmod.NonPositiveDefiniteError:
return None
S = pymc_objects.SparseMVN('S', M, precision_products, cholmod)
vars = pm.rgamma(4, 4, size=n)
vals = X[:, 2]
data = pm.Normal('data', S, 1. / vars, value=vals, observed=True)
Qobs = sparse.csc_matrix((n, n))
Qobs.setdiag(1. / vars)
@pm.deterministic(trace=False)
def true_evidence(Q=Q, M=M, vals=vals, vars=vars):
C = np.array(Q.todense().I + np.diag(vars))
return pm.mv_normal_cov_like(vals, M, C)
# Stuff for the scoring algorithm-based full conditional
def first_likelihood_derivative(x, vals=vals, vars=vars):
return -(x - vals) / vars
def second_likelihood_derivative(x, vals=vals, vars=vars):
return -1. / vars
return locals()
def make_model(X):
neighbors, triangles, trimap, b = spherical.triangulate_sphere(X)
# spherical.plot_triangulation(X,neighbors)
# Matrix generation
triangle_areas = [spherical.triangle_area(X, t) for t in triangles]
Ctilde = spherical.Ctilde(X, triangles, triangle_areas)
C = spherical.C(X, triangles, triangle_areas)
G = spherical.G(X, triangles, triangle_areas)
# Operator generation
Ctilde = cholmod.into_matrix_type(Ctilde)
G = cholmod.into_matrix_type(G)
# amp is the overall amplitude. It's a free variable that will probably be highly confounded with kappa.
amp = pm.Exponential('amp', .0001, value=100)
# A constant mean.
m = pm.Uninformative('m',value=0)
@pm.deterministic(trace=False)
def M(m=m,n=len(X)):
"""The mean vector"""
return np.ones(n)*m
kappa = pm.Exponential('kappa',1,value=3)
alpha = pm.DiscreteUniform('alpha',1,10,value=2., observed=True)
@pm.deterministic(trace=False)
def Q(kappa=kappa, alpha=alpha, amp=amp):
out = operators.mod_frac_laplacian_precision(Ctilde, G, kappa, alpha, cholmod)/np.asscalar(amp)**2
return out
# Nailing this ahead of time reduces time to compute logp from .18 to .13s for n=25000.
pattern_products = cholmod.pattern_to_products(Q.value)
# @pm.deterministic
# def pattern_products(Q=Q):
# return cholmod.pattern_to_products(Q)
@pm.deterministic(trace=False)
def precision_products(Q=Q, p=pattern_products):
try:
return cholmod.precision_to_products(Q, **p)
except cholmod.NonPositiveDefiniteError:
return None
S=pymc_objects.SparseMVN('S',M, precision_products, cholmod)
vars = pm.rgamma(4,4,size=n)
vals = X[:,2]
data = pm.Normal('data', S, 1./vars, value=vals, observed=True)
Qobs = sparse.csc_matrix((n,n))
Qobs.setdiag(1./vars)
@pm.deterministic(trace=False)
def true_evidence(Q=Q, M=M, vals=vals, vars=vars):
C = np.array(Q.todense().I+np.diag(vars))
return pm.mv_normal_cov_like(vals, M, C)
# Stuff for the scoring algorithm-based full conditional
def first_likelihood_derivative(x, vals=vals, vars=vars):
return -(x-vals)/vars
def second_likelihood_derivative(x, vals=vals, vars=vars):
return -1./vars
return locals()