# 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 itertools import izip
from distributions.dbg.special import log, gammaln
from distributions.dbg.random import (
    sample_discrete,
    sample_dirichlet,
    sample_beta,
)
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin
        return {'counts': counts}

    def protobuf_load(self, message):
        self.counts = {}
        self.total = 0
        for i, count in izip(message.keys, message.values):
            if count:
                self.counts[int(i)] = int(count)
                self.total += count

    def protobuf_dump(self, message):
        message.Clear()
        for i, count in self.counts.iteritems():
            if count:
                message.keys.append(i)
                message.values.append(count)


class Sampler(object):
    def init(self, shared, group=None):
        self.values = []
        post = []
        alpha = shared.alpha
        counts = {} if group is None else group.counts
        for value, beta in shared.betas.iteritems():
            self.values.append(value)
            post.append(beta * alpha + counts.get(value, 0))
        if shared.beta0 > 0:
            self.values.append(OTHER)
            post.append(shared.beta0 * alpha)

# 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/dpd.py
from itertools import izip
from distributions.dbg.special import log, gammaln
from distributions.dbg.random import (
    sample_discrete,
    sample_dirichlet,
    sample_beta,
)
from distributions.mixins import SharedMixin, GroupIoMixin, SharedIoMixin

        return {'counts': counts}

    def protobuf_load(self, message):
        self.counts = {}
        self.total = 0
        for i, count in izip(message.keys, message.values):
            if count:
                self.counts[int(i)] = int(count)
                self.total += count

    def protobuf_dump(self, message):
        message.Clear()
        for i, count in self.counts.iteritems():
            if count:
                message.keys.append(i)
                message.values.append(count)


class Sampler(object):
    def init(self, shared, group=None):
        self.values = []
        post = []
        alpha = shared.alpha
        counts = {} if group is None else group.counts
        for value, beta in shared.betas.iteritems():
            self.values.append(value)
            post.append(beta * alpha + counts.get(value, 0))
        if shared.beta0 > 0:
            self.values.append(OTHER)
            post.append(shared.beta0 * alpha)

        self.probs = sample_dirichlet(post)