Ejemplos de dcm en Python

Ejemplo n.º 1

Archivo: incr_vs_dcm_pmf.py Proyecto: MobMonRob/myutil

def prob(z):
    return (counts[z] + alpha[z]) / (sum(counts) + sum(alpha))


print "alpha\t", alpha

lp = 0
for z in data:
    lp += np.log(prob(z))
    counts[z] += 1

print "counts\t", counts

print "urn process\t", lp, np.exp(lp)

x = mcmc.dcm(np.array(counts), np.array(alpha), single=True)
print "DCM-1   \t", x, np.exp(x)

x = mcmc.dcm(np.array(counts), np.array(alpha), single=False)
print "DCM full\t", x, np.exp(x)

lp = 0
for z in data:
    counts[z] -= 1
    lp += np.log(prob(z))
    counts[z] += 1

print "posthoc-1\t", lp, np.exp(lp)

lp = 0
for z in data:

Ejemplo n.º 2

def loglik(log_conc):
  alpha = np.tile(np.exp(log_conc)/K, K)
  loglik = sum(mcmc.dcm(c,alpha) for c in counts)
  #prior = r.dgamma(np.exp(log_conc), .5, scale=1000, log=True)
  #prior = 0
  return loglik

Ejemplo n.º 3

Archivo: incr_vs_dcm_pmf.py Proyecto: brendano/myutil

counts = np.tile(0, K)
def prob(z):
  return (counts[z]+alpha[z]) / (sum(counts) + sum(alpha))

print "alpha\t",alpha

lp = 0
for z in data:
  lp += np.log(prob(z))
  counts[z] += 1

print "counts\t",counts

print "urn process\t", lp, np.exp(lp)

x = mcmc.dcm(np.array(counts),np.array(alpha), single=True)
print "DCM-1   \t", x, np.exp(x)

x = mcmc.dcm(np.array(counts),np.array(alpha), single=False)
print "DCM full\t", x, np.exp(x)


lp = 0
for z in data:
  counts[z] -= 1
  lp += np.log(prob(z))
  counts[z] += 1

print "posthoc-1\t", lp, np.exp(lp)

lp = 0

Ejemplo n.º 4

Archivo: faster_dcm_test.py Proyecto: brendano/myutil

import scipy.sparse
S = scipy.sparse.csr_matrix(C)

print sp.gammaln(C+A).sum()
print statutil.faster_lgamma_sum(C,A[0])
print statutil.sparse_lgamma_sum(S,A[0])

#In [1]: timeit util.sparse_lgamma_sum(S,A[0])
#10000 loops, best of 3: 23.1 us per loop
#
#In [2]: timeit util.faster_lgamma_sum(C,A[0])
#10000 loops, best of 3: 23.7 us per loop
#
#In [3]: timeit sp.gammaln(C+A).sum()
#1000 loops, best of 3: 569 us per loop

import mcmc
print mcmc.dcm_symm(C,A[0], single=True)
#Out[8]: -824.251394401741

print mcmc.dcm(C,A, single=True)
#Out[9]: -824.25139440437488

#In [10]: timeit mcmc.dcm(C,A, single=True)
#1000 loops, best of 3: 1.15 ms per loop
#
#In [11]: timeit mcmc.dcm_symm(C,A[0], single=True)
#10000 loops, best of 3: 40.1 us per loop

Ejemplo n.º 5

Archivo: faster_dcm_test.py Proyecto: MobMonRob/myutil

import scipy.special as sp

import scipy.sparse
S = scipy.sparse.csr_matrix(C)

print sp.gammaln(C + A).sum()
print statutil.faster_lgamma_sum(C, A[0])
print statutil.sparse_lgamma_sum(S, A[0])

#In [1]: timeit util.sparse_lgamma_sum(S,A[0])
#10000 loops, best of 3: 23.1 us per loop
#
#In [2]: timeit util.faster_lgamma_sum(C,A[0])
#10000 loops, best of 3: 23.7 us per loop
#
#In [3]: timeit sp.gammaln(C+A).sum()
#1000 loops, best of 3: 569 us per loop

import mcmc
print mcmc.dcm_symm(C, A[0], single=True)
#Out[8]: -824.251394401741

print mcmc.dcm(C, A, single=True)
#Out[9]: -824.25139440437488

#In [10]: timeit mcmc.dcm(C,A, single=True)
#1000 loops, best of 3: 1.15 ms per loop
#
#In [11]: timeit mcmc.dcm_symm(C,A[0], single=True)
#10000 loops, best of 3: 40.1 us per loop