/usr/share/pyshared/pymc/gp/GPutils.py is in python-pymc 2.2+ds-1.
This file is owned by root:root, with mode 0o644.
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__docformat__='reStructuredText'
__all__ = ['observe', 'plot_envelope', 'predictive_check', 'regularize_array', 'trimult', 'trisolve', 'vecs_to_datmesh', 'caching_call', 'caching_callable',
'fast_matrix_copy', 'point_predict','square_and_sum','point_eval']
# TODO: Implement lintrans, allow obs_V to be a huge matrix or an ndarray in observe().
from numpy import *
from numpy.linalg import solve, cholesky, eigh
from numpy.linalg.linalg import LinAlgError
from .linalg_utils import *
from threading import Thread, Lock
import sys
from pymc import thread_partition_array, map_noreturn
from pymc.gp import chunksize
import pymc
from pymc import six
from pymc.six import print_
xrange = six.moves.xrange
try:
from PyMC2 import ZeroProbability
except ImportError:
class ZeroProbability(ValueError):
pass
half_log_2pi = .5 * log(2. * pi)
def fast_matrix_copy(f, t=None, n_threads=1):
"""
Not any faster than a serial copy so far.
"""
if not f.flags['F_CONTIGUOUS']:
raise RuntimeError('This will not be fast unless input array f is Fortran-contiguous.')
if t is None:
t=asmatrix(empty(f.shape, order='F'))
elif not t.flags['F_CONTIGUOUS']:
raise RuntimeError('This will not be fast unless input array t is Fortran-contiguous.')
# Figure out how to divide job up between threads.
dcopy_wrap(ravel(asarray(f.T)),ravel(asarray(t.T)))
return t
def zero_lower_triangle(C):
pass
def caching_call(f, x, x_sofar, f_sofar):
"""
Computes f(x) given that f(x_sofar) = x_sofar.
returns f(x), and new versions of x_sofar and f_sofar.
"""
lenx = x.shape[0]
nr,rf,rt,nu,xu,ui = remove_duplicates(x)
unique_indices=ui[:nu]
x_unique=xu[:nu]
repeat_from=rf[:nr]
repeat_to=rt[:nr]
# Check which observations have already been made.
if x_sofar is not None:
f_unique, new_indices, N_new_indices = check_repeats(x_unique, x_sofar, f_sofar)
# If there are any new input points, draw values over them.
if N_new_indices>0:
x_new = x_unique[new_indices[:N_new_indices]]
f_new = f(x_new)
f_unique[new_indices[:N_new_indices]] = f_new
# Record the new values
x_sofar = vstack((x_sofar, x_new))
f_sofar = hstack((f_sofar, f_new))
else:
f=f_unique
# If no observations have been made, don't check.
else:
f_unique = f(x_unique)
x_sofar = x_unique
f_sofar = f_unique
f=empty(lenx)
f[unique_indices]=f_unique
f[repeat_to]=f[repeat_from]
return f, x_sofar, f_sofar
def square_and_sum(a,s):
"""
Writes np.sum(a**2,axis=0) into s
"""
cmin, cmax = thread_partition_array(a)
map_noreturn(asqs, [(a,s,cmin[i],cmax[i]) for i in xrange(len(cmax))])
return a
class caching_callable(object):
"""
F = caching_callable(f[, x_sofar, f_sofar, update_cache=True])
f is the function whose output should be cached.
x_sofar, if provided, is an initial list of caching locations.
f_sofar, if provided, is the value of f at x_sofar.
update_cache tells whether x_sofar and f_sofar should be updated as additional calls are made.
"""
def __init__(self, f, x_sofar=None, f_sofar=None, update_cache=True):
self.f = f
self.x_sofar = x_sofar
self.f_sofar = f_sofar
self.update_cache = update_cache
if self.x_sofar is not None and self.f_sofar is None:
junk, self.x_sofar, self.f_sofar = caching_call(self.f, self.x_sofar)
self.last_x = x_sofar
self.last_f = f_sofar
def __call__(self, x):
if x is self.x_sofar:
return self.f_sofar
elif x is self.last_x:
return self.last_f
f, x_sofar, f_sofar = caching_call(self.f, x, self.x_sofar, self.f_sofar)
self.last_x = x
self.last_f = f
if self.update_cache:
self.x_sofar = x_sofar
self.f_sofar = f_sofar
return f
def vecs_to_datmesh(x, y):
"""
Converts input arguments x and y to a 2d meshgrid,
suitable for calling Means, Covariances and Realizations.
"""
x,y = meshgrid(x,y)
out = zeros(x.shape + (2,), dtype=float)
out[:,:,0] = x
out[:,:,1] = y
return out
def trimult(U,x,uplo='U',transa='N',alpha=1.,inplace=False):
"""
b = trimult(U,x, uplo='U')
Multiplies U x, where U is upper triangular if uplo='U'
or lower triangular if uplo = 'L'.
"""
if inplace:
b=x
else:
b = x.copy('F')
dtrmm_wrap(a=U,b=b,uplo=uplo,transa=transa,alpha=alpha)
return b
def trisolve(U,b,uplo='U',transa='N',alpha=1.,inplace=False):
"""
x = trisolve(U,b, uplo='U')
Solves U x = b, where U is upper triangular if uplo='U'
or lower triangular if uplo = 'L'.
If a degenerate column is found, an error is raised.
"""
if inplace:
x=b
else:
x = b.copy('F')
if U.shape[0] == 0:
raise ValueError('Attempted to solve zero-rank triangular system')
dtrsm_wrap(a=U,b=x,side='L',uplo=uplo,transa=transa,alpha=alpha)
return x
def regularize_array(A):
"""
Takes an ndarray as an input.
- If the array is one-dimensional, it's assumed to be an array of input values.
- If the array is more than one-dimensional, its last index is assumed to curse
over spatial dimension.
Either way, the return value is at least two dimensional. A.shape[-1] gives the
number of spatial dimensions.
"""
# Make sure A is an array.
if not isinstance(A,ndarray):
A = array(A, dtype=float)
elif A.__class__ is not ndarray:
A = asarray(A, dtype=float)
# If A is one-dimensional, interpret it as an array of points on the line.
if len(A.shape) <= 1:
return A.reshape(-1,1)
# Otherwise, interpret it as an array of n-dimensional points, where n
# is the size of A along its last index.
elif A.shape[-1]>1 and len(A.shape) > 2:
return A.reshape(-1, A.shape[-1])
else:
return A
def plot_envelope(M,C,mesh):
"""
plot_envelope(M,C,mesh)
plots the pointwise mean +/- sd envelope defined by M and C
along their base mesh.
:Arguments:
- `M`: A Gaussian process mean.
- `C`: A Gaussian process covariance
- `mesh`: The mesh on which to evaluate the mean and cov.
"""
try:
from pylab import fill, plot, clf, axis
x=concatenate((mesh, mesh[::-1]))
mean, var = point_eval(M,C,mesh)
sig = sqrt(var)
mean = M(mesh)
y=concatenate((mean-sig, (mean+sig)[::-1]))
# clf()
fill(x,y,facecolor='.8',edgecolor='1.')
plot(mesh, mean, 'k-.')
except ImportError:
print_("Matplotlib is not installed; plotting is disabled.")
def observe(M, C, obs_mesh, obs_vals, obs_V = 0, lintrans = None, cross_validate = True):
"""
(M, C, obs_mesh, obs_vals[, obs_V = 0, lintrans = None, cross_validate = True])
Imposes observation of the value of obs_vals on M and C, where
obs_vals ~ N(lintrans * f(obs_mesh), V)
f ~ GP(M,C)
:Arguments:
- `M`: The mean function
- `C`: The covariance function
- `obs_mesh`: The places where f has been evaluated.
- `obs_vals`: The values of f that were observed there.
- `obs_V`: The observation variance. If None, assumed to be infinite
(observations made with no error).
- `lintrans`: A linear transformation. If None, assumed to be the
identity transformation (pretend it doesn't exist).
- `cross_validate`: A flag indicating whether a check should be done to
see if the data could have arisen from M and C with positive probability.
"""
obs_mesh = regularize_array(obs_mesh)
# print_(obs_mesh)
obs_V = resize(obs_V, obs_mesh.shape[0])
obs_vals = resize(obs_vals, obs_mesh.shape[0])
# First observe C.
relevant_slice, obs_mesh_new = C.observe(obs_mesh, obs_V, output_type='o')
# Then observe M from C.
M.observe(C, obs_mesh_new, obs_vals.ravel()[relevant_slice])
# Cross-validate if not asked not to.
if obs_mesh_new.shape[0] < obs_mesh.shape[0]:
if cross_validate:
if not predictive_check(obs_vals, obs_mesh, M, C.obs_piv, sqrt(C.relative_precision)):
raise ValueError("These data seem extremely improbable given your GP prior. \n Suggestions: decrease observation precision, or adjust the covariance to \n allow the function to be less smooth.")
def predictive_check(obs_vals, obs_mesh, M, posdef_indices, tolerance):
"""
OK = predictive_check(obs_vals, obs_mesh, M, posdef_indices, tolerance)
If an internal covariance is low-rank, make sure the observations
are consistent. Returns True if good, False if bad.
:Arguments:
- `obs_vals`: The observed values.
- `obs_mesh`: The mesh on which the observed values were observed.
- `M`: The mean function, observed at obs_vals[posdef_indices].
- `tolerance`: The maximum allowable deviation at M(obs_mesh[non_posdef_indices]).
"""
non_posdef_indices = array(list(set(range(len(obs_vals))) - set(posdef_indices)),dtype=int)
if len(non_posdef_indices)>0:
M_under = M(obs_mesh[non_posdef_indices,:]).ravel()
dev = abs((M_under - obs_vals[non_posdef_indices]))
if dev.max()>tolerance:
return False
return True
def point_predict(f, x, size=1, nugget=None):
"""
point_predict(f, x[, size, nugget])
Makes 'size' simulations for f(x) + N(0,nugget).
Simulated values of f(x_i) are uncorrelated for different i.
Useful for geostatistical predictions.
"""
orig_shape = x.shape
x = regularize_array(x)
mu = f.M_internal(x, regularize=False)
V = f.C_internal(x, regularize=False)
if nugget is not None:
V += nugget
out= random.normal(size=(size, x.shape[0])) * sqrt(V) + mu
return out.reshape((size,)+ orig_shape[:-1]).squeeze()
def point_eval(M, C, x):
"""
Evaluates M(x) and C(x).
Minimizes computation; evaluating M(x) and C(x) separately would
evaluate the off-diagonal covariance term twice, but callling
point_eval(M,C,x) would only evaluate it once.
Also chunks the evaluations if the off-diagonal term.
"""
x_ = regularize_array(x)
M_out = empty(x_.shape[0])
V_out = empty(x_.shape[0])
if isinstance(C, pymc.gp.BasisCovariance):
y_size = len(C.basis)
elif C.obs_mesh is not None:
y_size = C.obs_mesh.shape[0]
else:
y_size = 1
n_chunks = ceil(y_size*x_.shape[0]/float(chunksize))
bounds = array(linspace(0,x_.shape[0],n_chunks+1),dtype='int')
cmin=bounds[:-1]
cmax=bounds[1:]
for (cmin,cmax) in zip(bounds[:-1],bounds[1:]):
x__ = x_[cmin:cmax]
V_out[cmin:cmax], Uo_Cxo = C(x__, regularize=False, return_Uo_Cxo=True)
M_out[cmin:cmax] = M(x__, regularize=False, Uo_Cxo=Uo_Cxo)
if len(x.shape) > 1:
targ_shape = x.shape[:-1]
else:
targ_shape = x.shape
return M_out.reshape(targ_shape), V_out.reshape(targ_shape)
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