def __init__(self,
cluster_density,
alpha=None,
alpha_shape=None,
alpha_rate=None):
self.base_measure = PyCloneBaseMeasure()
self.partition_sampler = AuxillaryParameterPartitionSampler(
self.base_measure, cluster_density)
self.atom_sampler = BaseMeasureAtomSampler(self.base_measure,
cluster_density)
if alpha is None:
self.alpha = 1
self.update_alpha = True
self.concentration_sampler =\
GammaPriorConcentrationSampler(alpha_shape, alpha_rate)
else:
self.alpha = alpha
self.update_alpha = False
self.num_iters = 0
def __init__(self,
atom_sampler,
partition_sampler,
alpha=1.0,
alpha_priors=None,
global_params_sampler=None):
self.atom_sampler = atom_sampler
self.partition_sampler = partition_sampler
self.alpha = alpha
if alpha_priors is None:
self.update_alpha = False
else:
self.update_alpha = True
self.concentration_sampler = GammaPriorConcentrationSampler(
alpha_priors['shape'], alpha_priors['rate'])
if global_params_sampler is None:
self.update_global_params = False
else:
self.update_global_params = True
self.global_params_sampler = global_params_sampler
self.num_iters = 0
def __init__(self, base_measure, cluster_density):
self.base_measure = base_measure
self.cluster_density = cluster_density
self.concentration_sampler = GammaPriorConcentrationSampler(1, 1)
posterior_density = BetaBinomialDensity()
# self.partition_sampler = MarginalGibbsPartitionSampler(base_measure, cluster_density, posterior_density)
self.partition_sampler = AuxillaryParameterPartitionSampler(
base_measure, cluster_density)
self.atom_sampler = BetaBinomialGibbsAtomSampler(
base_measure, cluster_density)
from math import log
from pydp.samplers.concentration import GammaPriorConcentrationSampler
sampler = GammaPriorConcentrationSampler(0.01, 0.01)
x = 0
for i in range(1000000):
x = sampler.sample(x, 1, 100)
x /= 2
print x
log(x)
x = poisson_rvs(25)
data.append(PoissonData(x))
alpha = 1
base_measure = GammaBaseMeasure(1, 1)
cluster_density = PoissonDensity()
partition = Partition()
for item, data_point in enumerate(data):
partition.add_cell(base_measure.random())
partition.add_item(item, item)
concentration_sampler = GammaPriorConcentrationSampler(1, 1)
posterior_density = NegativeBinomialDensity()
partition_sampler = MarginalGibbsPartitionSampler(base_measure,
cluster_density,
posterior_density)
#partition_sampler = AuxillaryParameterPartitionSampler(base_measure, cluster_density)
atom_sampler = GammaPoissonGibbsAtomSampler(base_measure, cluster_density)
for i in range(num_iters):
alpha = concentration_sampler.sample(alpha, partition.number_of_cells,
partition.number_of_items)
partition_sampler.sample(data, partition, alpha)
