def observe(self, obs_mesh, obs_V):
__doc__ = Covariance.observe.__doc__
ndim = obs_mesh.shape[1]
nobs = obs_mesh.shape[0]
if self.ndim is not None:
if not ndim==self.ndim:
raise ValueError, "Dimension of observation mesh is not equal to dimension of base mesh."
else:
self.ndim = ndim
# bases_o is the basis evaluated on the observation mesh.
basis_o = asmatrix(self.eval_basis(obs_mesh, regularize=False))
# chol(basis_o.T * coef_cov * basis_o)
chol_inner = self.coef_U * basis_o
# chol(basis_o.T * coef_cov * basis_o + diag(obs_V)). Really should do this as low-rank update of covariance
# of V.
U, piv, m = ichol_basis(basis=chol_inner, nug=obs_V, reltol=self.relative_precision)
U = asmatrix(U)
piv_new = piv[:m]
self.obs_piv = piv_new
obs_mesh_new = obs_mesh[piv_new,:]
self.Uo = U[:m,:m]
# chol(basis_o.T * coef_cov * basis_o + diag(obs_V)) ^ -T * basis_o.T
self.Uo_cov = trisolve(self.Uo, basis_o[:,piv[:m]].T, uplo='U', transa='T')
# chol(basis_o.T * coef_cov * basis_o + diag(obs_V)).T.I * basis_o.T * coef_cov
self.Uo_cov = self.Uo_cov * self.coef_cov
# coef_cov -= coef_cov * basis_o * (basis_o.T * coef_cov * basis_o + diag(obs_V)).I * basis_o.T * coef_cov
self.coef_cov -= self.Uo_cov.T * self.Uo_cov
# coef_U = chol(coef_cov)
U, m, piv = ichol_full(c=self.coef_cov, reltol=self.relative_precision)
U = asmatrix(U)
self.coef_U = U[:m,argsort(piv)]
self.m = m
return piv_new, obs_mesh_new, None
def __init__(self, basis, coef_cov, relative_precision = 1.0E-15, **params):
self.observed = False
self.obs_mesh = None
self.obs_V = None
self.Uo = None
self.obs_piv = None
self.obs_len = None
self.full_piv = None
self.full_obs_mesh = None
self.basiscov=True
self.basis = ravel(basis)
self.shape = self.get_shape_from_basis(basis)
self.n = prod(self.shape)
self.ndim = len(self.shape)
self.relative_precision = relative_precision
self.params = params
if coef_cov.shape == self.shape:
self.coef_cov = asmatrix(diag(coef_cov.ravel()))
elif coef_cov.shape == self.shape*2:
self.coef_cov = asmatrix(coef_cov.reshape((self.n, self.n)))
else:
raise ValueError, "Covariance tensor's shape must be basis.shape or basis.shape*2 (using tuple multiplication)."
# Cholesky factor the covariance matrix of the coefficients.
U, m, piv = ichol_full(c=self.coef_cov, reltol=relative_precision)
self.coef_U = asmatrix(U[:m,argsort(piv)])
# Rank of the coefficient covariance.
self.unobs_m = m
self.m = m
# Record the unobserved Cholesky factor of the coefficient covariance.
self.unobs_coef_U = self.coef_U.copy()
self.observed = False
def __init__(self, basis, coef_cov, relative_precision=1.0E-15, **params):
self.observed = False
self.obs_mesh = None
self.obs_V = None
self.Uo = None
self.obs_piv = None
self.obs_len = None
self.full_piv = None
self.full_obs_mesh = None
self.basiscov = True
self.basis = ravel(basis)
self.shape = self.get_shape_from_basis(basis)
self.n = prod(self.shape)
self.ndim = len(self.shape)
self.relative_precision = relative_precision
self.params = params
if coef_cov.shape == self.shape:
self.coef_cov = asmatrix(diag(coef_cov.ravel()))
elif coef_cov.shape == self.shape * 2:
self.coef_cov = asmatrix(coef_cov.reshape((self.n, self.n)))
else:
raise ValueError, "Covariance tensor's shape must be basis.shape or basis.shape*2 (using tuple multiplication)."
# Cholesky factor the covariance matrix of the coefficients.
U, m, piv = ichol_full(c=self.coef_cov, reltol=relative_precision)
self.coef_U = asmatrix(U[:m, argsort(piv)])
# Rank of the coefficient covariance.
self.unobs_m = m
self.m = m
# Record the unobserved Cholesky factor of the coefficient covariance.
self.unobs_coef_U = self.coef_U.copy()
self.observed = False
def cholesky(self, x, apply_pivot = True, observed=True, nugget=None, regularize=True):
"""
U = C.cholesky(x[, observed=True, nugget=None])
{'pivots': piv, 'U': U} = \
C.cholesky(x, apply_pivot = False[, observed=True, nugget=None])
Computes incomplete Cholesky factorization of self(x,x).
:Arguments:
- `x`: The input array on which to evaluate the covariance.
- `apply_pivot`: A flag. If it's set to 'True', it returns a
matrix U (not necessarily triangular) such that U.T*U=C(x,x).
If it's set to 'False', the return value is a dictionary.
Item 'pivots' is a vector of pivots, and item 'U' is an
upper-triangular matrix (not necessarily square) such that
U[:,argsort(piv)].T * U[:,argsort(piv)] = C(x,x).
- `observed`: If 'True', any observations are taken into account
when computing the Cholesky factor. If not, the unobserved
version of self is used.
- `nugget`: The 'nugget' parameter, which will essentially be
added to the diagonal of C(x,x) before Cholesky factorizing.
"""
if regularize:
x=regularize_array(x)
# Number of points in x.
N_new = x.shape[0]
# Special fast version for single points.
if N_new==1:
U=asmatrix(sqrt(self.__call__(x, regularize = False, observed = observed)))
# print U
if not apply_pivot:
return {'pivots': array([0]), 'U': U}
else:
return U
C = self.__call__(x, x, regularize=False, observed=observed)
if nugget is not None:
for i in xrange(N_new):
C[i,i] += nugget[i]
# =======================================
# = Call to Fortran function ichol_full =
# =======================================
U, m, piv = ichol_full(c=C, reltol=self.relative_precision)
U = asmatrix(U)
# Arrange output matrix and return.
if m<0:
raise ValueError, "Matrix does not appear to be positive semidefinite"
if not apply_pivot:
# Useful for self.observe and Realization.__call__. U is upper triangular.
U = U[:m,:]
return {'pivots': piv, 'U': U}
else:
# Useful for users. U.T*U = C(x,x)
return U[:m,argsort(piv)]
def continue_cholesky(self, x, x_old, chol_dict_old, apply_pivot = True, observed=True, nugget=None, regularize=True, assume_full_rank=False):
"""
U = C.continue_cholesky(x, x_old, chol_dict_old[, observed=True, nugget=None])
{'pivots': piv, 'U': U} = \
C.cholesky(x, x_old, chol_dict_old, apply_pivot = False[, observed=True, nugget=None])
Computes incomplete Cholesky factorization of self(z,z). Here z is the
concatenation of x and x_old. Assumes the Cholesky factorization of
self(x_old, x_old) has already been computed.
:Arguments:
- `x`: The input array on which to evaluate the Cholesky factorization.
- `x_old`: The input array on which the Cholesky factorization has been
computed.
- `chol_dict_old`: A dictionary with kbasis_ys ['pivots', 'U']. Would be the
output of either this method or C.cholesky().
- `apply_pivot`: A flag. If it's set to 'True', it returns a
matrix U (not necessarily triangular) such that U.T*U=C(x,x).
If it's set to 'False', the return value is a dictionary.
Item 'pivots' is a vector of pivots, and item 'U' is an
upper-triangular matrix (not necessarily square) such that
U[:,argsort(piv)].T * U[:,argsort(piv)] = C(x,x).
- `observed`: If 'True', any observations are taken into account
when computing the Cholesky factor. If not, the unobserved
version of self is used.
- `nugget`: The 'nugget' parameter, which will essentially be
added to the diagonal of C(x,x) before Cholesky factorizing.
"""
if regularize:
x=regularize_array(x)
# Concatenation of the old points and new points.
xtot = vstack((x_old,x))
# Extract information from chol_dict_old.
U_old = chol_dict_old['U']
m_old = U_old.shape[0]
piv_old = chol_dict_old['pivots']
# Number of old points.
N_old = x_old.shape[0]
# Number of new points.
N_new = x.shape[0]
# Compute off-diagonal part of Cholesky factor
offdiag = self.__call__(x=x_old[piv_old[:m_old],:], y=x, observed=observed, regularize=False)
trisolve(U_old[:,:m_old],offdiag,uplo='U',transa='T', inplace=True)
# Compute new diagonal part of Cholesky factor
C_new = self.__call__(x=x, y=x, observed=observed, regularize=False)
if nugget is not None:
for i in xrange(N_new):
C_new[i,i] += nugget[i]
C_new -= offdiag.T*offdiag
if not assume_full_rank:
U_new, m_new, piv_new = ichol_full(c=C_new, reltol=self.relative_precision)
else:
U_new = cholesky(C_new).T
m_new = U_new.shape[0]
piv_new = arange(m_new)
U_new = asmatrix(U_new[:m_new,:])
U = asmatrix(zeros((m_new + m_old, N_old + N_new), dtype=float, order='F'))
# Top portion of U
U[:m_old, :m_old] = U_old[:,:m_old]
U[:m_old,N_new+m_old:] = U_old[:,m_old:]
offdiag=offdiag[:,piv_new]
U[:m_old, m_old:N_new+m_old] = offdiag
# Lower portion of U
U[m_old:,m_old:m_old+N_new] = U_new
if m_old < N_old and m_new > 0:
offdiag_lower = self.__call__( x=x[piv_new[:m_new],:],
y=x_old[piv_old[m_old:],:], observed=observed, regularize=False)
offdiag_lower -= offdiag[:,:m_new].T*U[:m_old,m_old+N_new:]
trisolve(U_new[:,:m_new],offdiag_lower,uplo='U',transa='T', inplace=True)
U[m_old:,m_old+N_new:] = offdiag_lower
# Rank and pivots
m=m_old+m_new
piv=hstack((piv_old[:m_old],piv_new+N_old,piv_old[m_old:]))
# Arrange output matrix and return.
if m<0:
raise ValueError, 'Matrix does not appear positive semidefinite.'
if not apply_pivot:
# Useful for self.observe. U is upper triangular.
return {'pivots': piv, 'U': U}
else:
# Useful for the user. U.T * U = C(x,x).
return U[:,argsort(piv)]
def cholesky(self, x, apply_pivot = True, observed=True, nugget=None, regularize=True, rank_limit=0):
"""
U = C.cholesky(x[, observed=True, nugget=None, rank_limit=0])
{'pivots': piv, 'U': U} = \
C.cholesky(x, apply_pivot = False[, observed=True, nugget=None])
Computes incomplete Cholesky factorization of self(x,x).
:Arguments:
- `x`: The input array on which to evaluate the covariance.
- `apply_pivot`: A flag. If it's set to 'True', it returns a
matrix U (not necessarily triangular) such that U.T*U=C(x,x).
If it's set to 'False', the return value is a dictionary.
Item 'pivots' is a vector of pivots, and item 'U' is an
upper-triangular matrix (not necessarily square) such that
U[:,argsort(piv)].T * U[:,argsort(piv)] = C(x,x).
- `observed`: If 'True', any observations are taken into account
when computing the Cholesky factor. If not, the unobserved
version of self is used.
- `nugget`: The 'nugget' parameter, which will essentially be
added to the diagonal of C(x,x) before Cholesky factorizing.
- `rank_limit`: If rank_limit > 0, the factor will have at most
rank_limit rows.
"""
if rank_limit > 0:
raise ValueError, 'NearlyFullRankCovariance does not accept a rank_limit argument. Use Covariance instead.'
if regularize:
x=regularize_array(x)
# Number of points in x.
N_new = x.shape[0]
# Special fast version for single points.
if N_new==1:
V = self.__call__(x, regularize = False, observed = observed)
if nugget is not None:
V += nugget
U=asmatrix(sqrt(V))
# print U
if not apply_pivot:
return {'pivots': array([0]), 'U': U}
else:
return U
C = self.__call__(x, x, regularize=False, observed=observed)
if nugget is not None:
for i in xrange(N_new):
C[i,i] += nugget[i]
# =======================================
# = Call to Fortran function ichol_full =
# =======================================
U, m, piv = ichol_full(c=C, reltol=self.relative_precision)
U = asmatrix(U)
# Arrange output matrix and return.
if m<0:
raise ValueError, "Matrix does not appear to be positive semidefinite"
if not apply_pivot:
# Useful for self.observe and Realization.__call__. U is upper triangular.
U = U[:m,:]
return {'pivots': piv, 'U': U}
else:
# Useful for users. U.T*U = C(x,x)
return U[:m,argsort(piv)]
def continue_cholesky(self, x, x_old, chol_dict_old, apply_pivot = True, observed=True, nugget=None, regularize=True, assume_full_rank=False, rank_limit=0):
"""
U = C.continue_cholesky(x, x_old, chol_dict_old[, observed=True, nugget=None,
rank_limit=0])
Returns {'pivots': piv, 'U': U}
Computes incomplete Cholesky factorization of self(z,z). Here z is the
concatenation of x and x_old. Assumes the Cholesky factorization of
self(x_old, x_old) has already been computed.
:Arguments:
- `x`: The input array on which to evaluate the Cholesky factorization.
- `x_old`: The input array on which the Cholesky factorization has been
computed.
- `chol_dict_old`: A dictionary with kbasis_ys ['pivots', 'U']. Would be the
output of either this method or C.cholesky().
- `apply_pivot`: A flag. If it's set to 'True', it returns a
matrix U (not necessarily triangular) such that U.T*U=C(x,x).
If it's set to 'False', the return value is a dictionary.
Item 'pivots' is a vector of pivots, and item 'U' is an
upper-triangular matrix (not necessarily square) such that
U[:,argsort(piv)].T * U[:,argsort(piv)] = C(x,x).
- `observed`: If 'True', any observations are taken into account
when computing the Cholesky factor. If not, the unobserved
version of self is used.
- `nugget`: The 'nugget' parameter, which will essentially be
added to the diagonal of C(x,x) before Cholesky factorizing.
- `rank_limit`: If rank_limit > 0, the factor will have at most
rank_limit rows.
"""
if regularize:
x=regularize_array(x)
if rank_limit > 0:
raise ValueError, 'NearlyFullRankCovariance does not accept a rank_limit argument. Use Covariance instead.'
if rank_limit > 0:
raise ValueError, 'NearlyFullRankCovariance does not accept a rank_limit argument. Use Covariance instead.'
# Concatenation of the old points and new points.
xtot = vstack((x_old,x))
# Extract information from chol_dict_old.
U_old = chol_dict_old['U']
m_old = U_old.shape[0]
piv_old = chol_dict_old['pivots']
# Number of old points.
N_old = x_old.shape[0]
# Number of new points.
N_new = x.shape[0]
# Compute off-diagonal part of Cholesky factor
offdiag = self.__call__(x=x_old[piv_old[:m_old],:], y=x, observed=observed, regularize=False)
trisolve(U_old[:,:m_old],offdiag,uplo='U',transa='T', inplace=True)
# Compute new diagonal part of Cholesky factor
C_new = self.__call__(x=x, y=x, observed=observed, regularize=False)
if nugget is not None:
for i in xrange(N_new):
C_new[i,i] += nugget[i]
C_new -= offdiag.T*offdiag
if not assume_full_rank:
U_new, m_new, piv_new = ichol_full(c=C_new, reltol=self.relative_precision)
else:
U_new = cholesky(C_new).T
m_new = U_new.shape[0]
piv_new = arange(m_new)
U_new = asmatrix(U_new[:m_new,:])
U = asmatrix(zeros((m_new + m_old, N_old + N_new), dtype=float, order='F'))
# Top portion of U
U[:m_old, :m_old] = U_old[:,:m_old]
U[:m_old,N_new+m_old:] = U_old[:,m_old:]
offdiag=offdiag[:,piv_new]
U[:m_old, m_old:N_new+m_old] = offdiag
# Lower portion of U
U[m_old:,m_old:m_old+N_new] = U_new
if m_old < N_old and m_new > 0:
offdiag_lower = self.__call__( x=x[piv_new[:m_new],:],
y=x_old[piv_old[m_old:],:], observed=observed, regularize=False)
offdiag_lower -= offdiag[:,:m_new].T*U[:m_old,m_old+N_new:]
trisolve(U_new[:,:m_new],offdiag_lower,uplo='U',transa='T', inplace=True)
U[m_old:,m_old+N_new:] = offdiag_lower
# Rank and pivots
m=m_old+m_new
piv=hstack((piv_old[:m_old],piv_new+N_old,piv_old[m_old:]))
# Arrange output matrix and return.
if m<0:
raise ValueError, 'Matrix does not appear positive semidefinite.'
if not apply_pivot:
# Useful for self.observe. U is upper triangular.
if assume_full_rank:
return {'pivots':piv,'U':U,'C_eval':C_new,'U_new':U_new}
else:
return {'pivots': piv, 'U': U}
else:
# Useful for the user. U.T * U = C(x,x).
return U[:,argsort(piv)]
def observe(self, obs_mesh, obs_V, output_type='o'):
__doc__ = Covariance.observe.__doc__
ndim = obs_mesh.shape[1]
nobs = obs_mesh.shape[0]
if self.ndim is not None:
if not ndim == self.ndim:
raise ValueError, "Dimension of observation mesh is not equal to dimension of base mesh."
else:
self.ndim = ndim
# bases_o is the basis evaluated on the observation mesh.
basis_o = asmatrix(self.eval_basis(obs_mesh, regularize=False))
# chol(basis_o.T * coef_cov * basis_o)
chol_inner = self.coef_U * basis_o
if output_type == 's':
C_eval = dot(chol_inner.T, chol_inner).copy('F')
U_eval = linalg.cholesky(C_eval).T.copy('F')
U = U_eval
m = U_eval.shape[0]
piv = arange(m)
else:
# chol(basis_o.T * coef_cov * basis_o + diag(obs_V)). Really should do this as low-rank update of covariance
# of V.
U, piv, m = ichol_basis(basis=chol_inner,
nug=obs_V,
reltol=self.relative_precision)
U = asmatrix(U)
piv_new = piv[:m]
self.obs_piv = piv_new
obs_mesh_new = obs_mesh[piv_new, :]
self.Uo = U[:m, :m]
# chol(basis_o.T * coef_cov * basis_o + diag(obs_V)) ^ -T * basis_o.T
self.Uo_cov = trisolve(self.Uo,
basis_o[:, piv[:m]].T,
uplo='U',
transa='T')
# chol(basis_o.T * coef_cov * basis_o + diag(obs_V)).T.I * basis_o.T * coef_cov
self.Uo_cov = self.Uo_cov * self.coef_cov
# coef_cov = coef_cov - coef_cov * basis_o * (basis_o.T * coef_cov * basis_o + diag(obs_V)).I * basis_o.T * coef_cov
self.coef_cov = self.coef_cov - self.Uo_cov.T * self.Uo_cov
# coef_U = chol(coef_cov)
U, m, piv = ichol_full(c=self.coef_cov, reltol=self.relative_precision)
U = asmatrix(U)
self.coef_U = U[:m, argsort(piv)]
self.m = m
if output_type == 'o':
return piv_new, obs_mesh_new
if output_type == 's':
return U_eval, C_eval, basis_o
raise ValueError, 'Output type not recognized.'
