# Path: distributions/dbg/special.py

# 
# Path: distributions/dbg/random.py
# LOG = logging.getLogger(__name__)
#     S = inner(inner(z, inv(sigma)), z)
#     X = multivariate_normal(mean=numpy.zeros(d), cov=Lambda, size=nu)
#     S = numpy.dot(X.T, X)
#     T = numpy.zeros((d, d))
#     D, = mu0.shape
#     Z = 0.
# def seed(x):
# def sample_discrete_log(scores):
# def sample_bernoulli(prob):
# def sample_discrete(probs, total=None):
# def sample_normal(mu, sigmasq):
# def sample_chi2(nu):
# def sample_student_t(dof, mu, Sigma):
# def score_student_t(x, nu, mu, sigma):
# def sample_wishart_naive(nu, Lambda):
# def sample_wishart(nu, Lambda):
# def sample_wishart_v2(nu, Lambda):
# def sample_inverse_wishart(nu, S):
# def sample_normal_inverse_wishart(mu0, lambda0, psi0, nu0):
# def sample_partition_from_counts(items, counts):
# def sample_stick(gamma, tol=1e-3):
# def sample_negative_binomial(p, r):


from distributions.dbg.special import log, factorial, gammaln
from distributions.dbg.random import sample_gamma, sample_poisson
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin

    def sample_value(self, shared):
        sampler = Sampler()
        sampler.init(shared, self)
        return sampler.eval(shared)

    def load(self, raw):
        self.count = int(raw['count'])
        self.sum = int(raw['sum'])
        self.log_prod = float(raw['log_prod'])

    def dump(self):
        return {
            'count': self.count,
            'sum': self.sum,
            'log_prod': self.log_prod,
        }

    def protobuf_load(self, message):
        self.count = int(message.count)
        self.sum = int(message.sum)
        self.log_prod = float(message.log_prod)

    def protobuf_dump(self, message):
        message.count = self.count
        message.sum = self.sum
        message.log_prod = self.log_prod


class Sampler(object):

# Path: distributions/dbg/special.py

# 
# Path: distributions/dbg/random.py
# LOG = logging.getLogger(__name__)
#     S = inner(inner(z, inv(sigma)), z)
#     X = multivariate_normal(mean=numpy.zeros(d), cov=Lambda, size=nu)
#     S = numpy.dot(X.T, X)
#     T = numpy.zeros((d, d))
#     D, = mu0.shape
#     Z = 0.
# def seed(x):
# def sample_discrete_log(scores):
# def sample_bernoulli(prob):
# def sample_discrete(probs, total=None):
# def sample_normal(mu, sigmasq):
# def sample_chi2(nu):
# def sample_student_t(dof, mu, Sigma):
# def score_student_t(x, nu, mu, sigma):
# def sample_wishart_naive(nu, Lambda):
# def sample_wishart(nu, Lambda):
# def sample_wishart_v2(nu, Lambda):
# def sample_inverse_wishart(nu, S):
# def sample_normal_inverse_wishart(mu0, lambda0, psi0, nu0):
# def sample_partition_from_counts(items, counts):
# def sample_stick(gamma, tol=1e-3):
# def sample_negative_binomial(p, r):


# Path: distributions/dbg/models/gp.py
from distributions.dbg.special import log, factorial, gammaln
from distributions.dbg.random import sample_gamma, sample_poisson
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin


    def sample_value(self, shared):
        sampler = Sampler()
        sampler.init(shared, self)
        return sampler.eval(shared)

    def load(self, raw):
        self.count = int(raw['count'])
        self.sum = int(raw['sum'])
        self.log_prod = float(raw['log_prod'])

    def dump(self):
        return {
            'count': self.count,
            'sum': self.sum,
            'log_prod': self.log_prod,
        }

    def protobuf_load(self, message):
        self.count = int(message.count)
        self.sum = int(message.sum)
        self.log_prod = float(message.log_prod)

    def protobuf_dump(self, message):
        message.count = self.count
        message.sum = self.sum
        message.log_prod = self.log_prod


class Sampler(object):

    def init(self, shared, group=None):