# 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):


import numpy
from distributions.dbg.special import log, gammaln
from distributions.dbg.random import sample_discrete, sample_dirichlet
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin
        score = sum(gammaln(a[k] + m[k]) - gammaln(a[k]) for k in xrange(dim))
        score += gammaln(a.sum())
        score -= gammaln(a.sum() + m.sum())
        return score

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

    def load(self, raw):
        self.counts = numpy.array(raw['counts'], dtype=numpy.int)

    def dump(self):
        return {'counts': self.counts.tolist()}

    def protobuf_load(self, message):
        self.counts = numpy.array(message.counts, dtype=numpy.int)

    def protobuf_dump(self, message):
        message.Clear()
        for count in self.counts:
            message.counts.append(count)


class Sampler(object):
    def init(self, shared, group=None):
        if group is None:
            self.ps = sample_dirichlet(shared.alphas)
        else:

# 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/dd.py
import numpy
from distributions.dbg.special import log, gammaln
from distributions.dbg.random import sample_discrete, sample_dirichlet
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin

        score = sum(gammaln(a[k] + m[k]) - gammaln(a[k]) for k in xrange(dim))
        score += gammaln(a.sum())
        score -= gammaln(a.sum() + m.sum())
        return score

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

    def load(self, raw):
        self.counts = numpy.array(raw['counts'], dtype=numpy.int)

    def dump(self):
        return {'counts': self.counts.tolist()}

    def protobuf_load(self, message):
        self.counts = numpy.array(message.counts, dtype=numpy.int)

    def protobuf_dump(self, message):
        message.Clear()
        for count in self.counts:
            message.counts.append(count)


class Sampler(object):
    def init(self, shared, group=None):
        if group is None:
            self.ps = sample_dirichlet(shared.alphas)
        else:

            self.ps = sample_dirichlet(group.counts + shared.alphas)