def update_dir_prior(prior, N, logphat, rho):
"""
Updates a given prior using Newton's method, described in
**Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters.**
http://jonathan-huang.org/research/dirichlet/dirichlet.pdf
"""
dprior = np.copy(prior)
gradf = N * (psi(np.sum(prior)) - psi(prior) + logphat)
c = N * polygamma(1, np.sum(prior))
q = -N * polygamma(1, prior)
b = np.sum(gradf / q) / (1 / c + np.sum(1 / q))
dprior = -(gradf - b) / q
if all(rho * dprior + prior > 0):
prior += rho * dprior
else:
logger.warning("updated prior not positive")
return prior
python类polygamma()的实例源码
def update_alpha(self, gammat, rho):
"""
Update parameters for the Dirichlet prior on the per-document
topic weights `alpha` given the last `gammat`.
Uses Newton's method: http://www.stanford.edu/~jhuang11/research/dirichlet/dirichlet.pdf
"""
N = float(len(gammat))
logphat = sum(dirichlet_expectation(gamma) for gamma in gammat) / N
dalpha = numpy.copy(self.alpha)
gradf = N * (psi(numpy.sum(self.alpha)) - psi(self.alpha) + logphat)
c = N * polygamma(1, numpy.sum(self.alpha))
q = -N * polygamma(1, self.alpha)
b = numpy.sum(gradf / q) / ( 1 / c + numpy.sum(1 / q))
dalpha = -(gradf - b) / q
if all(rho() * dalpha + self.alpha > 0):
self.alpha += rho() * dalpha
else:
logger.warning("updated alpha not positive")
logger.info("optimized alpha %s" % list(self.alpha))
return self.alpha
def update_alpha(self, gammat, rho):
"""
Update parameters for the Dirichlet prior on the per-document
topic weights `alpha` given the last `gammat`.
Uses Newton's method: http://www.stanford.edu/~jhuang11/research/dirichlet/dirichlet.pdf
"""
N = float(len(gammat))
logphat = sum(dirichlet_expectation(gamma) for gamma in gammat) / N
dalpha = numpy.copy(self.alpha)
gradf = N * (psi(numpy.sum(self.alpha)) - psi(self.alpha) + logphat)
c = N * polygamma(1, numpy.sum(self.alpha))
q = -N * polygamma(1, self.alpha)
b = numpy.sum(gradf / q) / ( 1 / c + numpy.sum(1 / q))
dalpha = -(gradf - b) / q
if all(rho() * dalpha + self.alpha > 0):
self.alpha += rho() * dalpha
else:
logger.warning("updated alpha not positive")
logger.info("optimized alpha %s" % list(self.alpha))
return self.alpha
def update_alpha(self, gammat, rho):
"""
Update parameters for the Dirichlet prior on the per-document
topic weights `alpha` given the last `gammat`.
Uses Newton's method, described in **Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters.** (http://www.stanford.edu/~jhuang11/research/dirichlet/dirichlet.pdf)
"""
N = float(len(gammat))
logphat = sum(dirichlet_expectation(gamma) for gamma in gammat) / N
dalpha = numpy.copy(self.alpha)
gradf = N * (psi(numpy.sum(self.alpha)) - psi(self.alpha) + logphat)
c = N * polygamma(1, numpy.sum(self.alpha))
q = -N * polygamma(1, self.alpha)
b = numpy.sum(gradf / q) / ( 1 / c + numpy.sum(1 / q))
dalpha = -(gradf - b) / q
if all(rho() * dalpha + self.alpha > 0):
self.alpha += rho() * dalpha
else:
logger.warning("updated alpha not positive")
logger.info("optimized alpha %s" % list(self.alpha))
return self.alpha
def update_alpha(self, gammat, rho):
"""
Update parameters for the Dirichlet prior on the per-document
topic weights `alpha` given the last `gammat`.
Uses Newton's method, described in **Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters.** (http://www.stanford.edu/~jhuang11/research/dirichlet/dirichlet.pdf)
"""
N = float(len(gammat))
logphat = sum(dirichlet_expectation(gamma) for gamma in gammat) / N
dalpha = numpy.copy(self.alpha)
gradf = N * (psi(numpy.sum(self.alpha)) - psi(self.alpha) + logphat)
c = N * polygamma(1, numpy.sum(self.alpha))
q = -N * polygamma(1, self.alpha)
b = numpy.sum(gradf / q) / ( 1 / c + numpy.sum(1 / q))
dalpha = -(gradf - b) / q
if all(rho() * dalpha + self.alpha > 0):
self.alpha += rho() * dalpha
else:
logger.warning("updated alpha not positive")
logger.info("optimized alpha %s" % list(self.alpha))
return self.alpha
def lnZ_Exponential_var(M):
return polygamma(1, M)
def perform(self, node, inputs, output_storage):
x = inputs[0]
z = output_storage[0]
z[0] = sp.polygamma(self.n, x)
