# Path: pygp/learning/sampling.py
# def sample(gp, priors, n, raw=True, rng=None):
#     rng = rstate(rng)
#     priors = dict(priors)
#     active = np.ones(gp.nhyper, dtype=bool)
#     logged = np.ones(gp.nhyper, dtype=bool)
# 
#     for (key, block, log) in get_params(gp):
#         inactive = (key in priors) and (priors[key] is None)
#         logged[block] = log
#         active[block] = not inactive
#         if inactive:
#             del priors[key]
#         else:
#             priors[key] = (block, log, priors[key])
# 
#     # priors is now just a list of the form (block, log, prior).
#     priors = priors.values()
# 
#     # get the initial hyperparameters and transform into the non-log space.
#     hyper0 = gp.get_hyper()
#     hyper0[logged] = np.exp(hyper0[logged])
# 
#     def logprob(x):
#         # copy the initial hyperparameters and then assign the "active"
#         # parameters that come from x.
#         hyper = hyper0.copy()
#         hyper[active] = x
#         logprob = 0
# 
#         # compute the prior probabilities. we do this first so that if there
#         # are any infs they'll be caught in the least expensive computations
#         # first.
#         for block, log, prior in priors:
#             logprob += prior.logprior(hyper[block])
#             if np.isinf(logprob):
#                 break
# 
#         # now compute the likelihood term. note that we'll have to take the log
#         # of any logspace parameters before calling set_hyper.
#         if not np.isinf(logprob):
#             hyper[logged] = np.log(hyper[logged])
#             gp.set_hyper(hyper)
#             logprob += gp.loglikelihood()
# 
#         return logprob
# 
#     # create a big list of the hyperparameters so that we can just assign to
#     # the components that are active. also get an initial sample x
#     # corresponding only to the active parts of hyper0.
#     hypers = np.tile(hyper0, (n, 1))
#     x = hyper0.copy()[active]
# 
#     # do the sampling.
#     for i in xrange(n):
#         x = _slice_sample(logprob, x, rng=rng)
#         hypers[i][active] = x
# 
#     # change the logspace components back into logspace.
#     hypers[:, logged] = np.log(hypers[:, logged])
# 
#     # make sure the gp gets updated to the last sampled hyperparameter.
#     gp.set_hyper(hypers[-1])
# 
#     if raw:
#         return hypers
#     else:
#         return [gp.copy(h) for h in hypers]
# 
# Path: pygp/utils/models.py
# def get_params(obj):
#     """
#     Helper function which translates the values returned by _params() into
#     something more meaningful.
#     """
#     offset = 0
#     for param in obj._params():
#         key, size, log = param
#         block = slice(offset, offset+size)
#         offset += size
#         yield key, block, log


import numpy as np
from scipy.misc import logsumexp
from mwhutils.random import rstate
from ..learning.sampling import sample
from ..utils.models import get_params
# local imports

# exported symbols
__all__ = ['SMC']


def _sample_prior(model, priors, n, rng=None):
    rng = rstate(rng)

    # unpack priors
    # TODO -- Bobak: This snippet is copied from learning/sampling.py
    # and should probably be put into a Prior base class.
    priors = dict(priors)
    active = np.ones(model.nhyper, dtype=bool)
    logged = np.ones(model.nhyper, dtype=bool)

    for (key, block, log) in get_params(model):
        inactive = (key in priors) and (priors[key] is None)
        logged[block] = log
        active[block] = not inactive
        if inactive:
            del priors[key]
        else:
            priors[key] = (block, log, priors[key])
    priors = priors.values()

    # sample hyperparameters from prior
    hypers = np.tile(model.get_hyper(), (n, 1))
    for (block, log, prior) in priors:
        hypers[:, block] = (np.log(prior.sample(n, rng=rng)) if log else

# Path: pygp/learning/sampling.py
# def sample(gp, priors, n, raw=True, rng=None):
#     rng = rstate(rng)
#     priors = dict(priors)
#     active = np.ones(gp.nhyper, dtype=bool)
#     logged = np.ones(gp.nhyper, dtype=bool)
# 
#     for (key, block, log) in get_params(gp):
#         inactive = (key in priors) and (priors[key] is None)
#         logged[block] = log
#         active[block] = not inactive
#         if inactive:
#             del priors[key]
#         else:
#             priors[key] = (block, log, priors[key])
# 
#     # priors is now just a list of the form (block, log, prior).
#     priors = priors.values()
# 
#     # get the initial hyperparameters and transform into the non-log space.
#     hyper0 = gp.get_hyper()
#     hyper0[logged] = np.exp(hyper0[logged])
# 
#     def logprob(x):
#         # copy the initial hyperparameters and then assign the "active"
#         # parameters that come from x.
#         hyper = hyper0.copy()
#         hyper[active] = x
#         logprob = 0
# 
#         # compute the prior probabilities. we do this first so that if there
#         # are any infs they'll be caught in the least expensive computations
#         # first.
#         for block, log, prior in priors:
#             logprob += prior.logprior(hyper[block])
#             if np.isinf(logprob):
#                 break
# 
#         # now compute the likelihood term. note that we'll have to take the log
#         # of any logspace parameters before calling set_hyper.
#         if not np.isinf(logprob):
#             hyper[logged] = np.log(hyper[logged])
#             gp.set_hyper(hyper)
#             logprob += gp.loglikelihood()
# 
#         return logprob
# 
#     # create a big list of the hyperparameters so that we can just assign to
#     # the components that are active. also get an initial sample x
#     # corresponding only to the active parts of hyper0.
#     hypers = np.tile(hyper0, (n, 1))
#     x = hyper0.copy()[active]
# 
#     # do the sampling.
#     for i in xrange(n):
#         x = _slice_sample(logprob, x, rng=rng)
#         hypers[i][active] = x
# 
#     # change the logspace components back into logspace.
#     hypers[:, logged] = np.log(hypers[:, logged])
# 
#     # make sure the gp gets updated to the last sampled hyperparameter.
#     gp.set_hyper(hypers[-1])
# 
#     if raw:
#         return hypers
#     else:
#         return [gp.copy(h) for h in hypers]
# 
# Path: pygp/utils/models.py
# def get_params(obj):
#     """
#     Helper function which translates the values returned by _params() into
#     something more meaningful.
#     """
#     offset = 0
#     for param in obj._params():
#         key, size, log = param
#         block = slice(offset, offset+size)
#         offset += size
#         yield key, block, log


# Path: pygp/meta/smc.py
import numpy as np
from scipy.misc import logsumexp
from mwhutils.random import rstate
from ..learning.sampling import sample
from ..utils.models import get_params

# local imports

# exported symbols
__all__ = ['SMC']


def _sample_prior(model, priors, n, rng=None):
    rng = rstate(rng)

    # unpack priors
    # TODO -- Bobak: This snippet is copied from learning/sampling.py
    # and should probably be put into a Prior base class.
    priors = dict(priors)
    active = np.ones(model.nhyper, dtype=bool)
    logged = np.ones(model.nhyper, dtype=bool)

    for (key, block, log) in get_params(model):
        inactive = (key in priors) and (priors[key] is None)
        logged[block] = log
        active[block] = not inactive
        if inactive:
            del priors[key]
        else:
            priors[key] = (block, log, priors[key])
    priors = priors.values()

    # sample hyperparameters from prior
    hypers = np.tile(model.get_hyper(), (n, 1))
    for (block, log, prior) in priors:
        hypers[:, block] = (np.log(prior.sample(n, rng=rng)) if log else

                            prior.sample(n, rng=rng))