# 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

    def protobuf_load(self, message):
        assert len(message.betas) == len(message.values), "invalid message"
        assert len(message.counts) == len(message.values), "invalid message"
        self.gamma = float(message.gamma)
        self.alpha = float(message.alpha)
        self.betas = {
            int(value): float(beta)
            for value, beta in izip(message.values, message.betas)
        }
        self.counts = {
            int(value): int(count)
            for value, count in izip(message.values, message.counts)
        }
        self._load_beta0()

    def protobuf_dump(self, message):
        message.Clear()
        message.gamma = self.gamma
        message.alpha = self.alpha
        for value, beta in self.betas.iteritems():
            message.values.append(value)
            message.betas.append(beta)
            message.counts.append(self.counts[value])

    def add_value(self, value):
        assert value != OTHER, 'cannot add OTHER'
        count = self.counts.get(value, 0) + 1
        self.counts[value] = count
        if count == 1:

# 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


    def protobuf_load(self, message):
        assert len(message.betas) == len(message.values), "invalid message"
        assert len(message.counts) == len(message.values), "invalid message"
        self.gamma = float(message.gamma)
        self.alpha = float(message.alpha)
        self.betas = {
            int(value): float(beta)
            for value, beta in izip(message.values, message.betas)
        }
        self.counts = {
            int(value): int(count)
            for value, count in izip(message.values, message.counts)
        }
        self._load_beta0()

    def protobuf_dump(self, message):
        message.Clear()
        message.gamma = self.gamma
        message.alpha = self.alpha
        for value, beta in self.betas.iteritems():
            message.values.append(value)
            message.betas.append(beta)
            message.counts.append(self.counts[value])

    def add_value(self, value):
        assert value != OTHER, 'cannot add OTHER'
        count = self.counts.get(value, 0) + 1
        self.counts[value] = count
        if count == 1:

            beta = self.beta0 * sample_beta(1.0, self.gamma)