def __init__(
self,
expe,
frontend,
assortativity=False,
alpha_hyper_parameter=None,
sigma_w_hyper_parameter=None,
metropolis_hastings_k_new=True,
):
self._sigma_w_hyper_parameter = sigma_w_hyper_parameter
self.bilinear_matrix = None
self.log_likelihood = None
self.assortativity = assortativity
self._overflow = 1.0
self.ratio_MH_F = 0.0
self.ratio_MH_W = 0.0
self.snapshot_freq = 20
self.burnin = expe.get('burnin',
5) # (inverse burnin, last sample to keep
self.thinning = expe.get('thinning', 1)
self._csv_typo = '_iteration time_it _entropy _entropy_t _K _alpha _sigma_w Z_sum ratio_MH_F ratio_MH_W'
#self._fmt = '%d %.4f %.8f %.8f %d %.8f %.8f %d %.4f %.4f'
IBP.__init__(self, alpha_hyper_parameter, metropolis_hastings_k_new)
GibbsSampler.__init__(self, expe, frontend)
def __init__(self, expe, frontend):
self.comm = dict(
) # Empty dict to store communities and blockmodel structure
self._measures = [
'_iteration', 'time_it', '_entropy', '_entropy_t', '_K', '_alpha',
'_gmma', 'alpha_mean', 'delta_mean', 'alpha_var', 'delta_var'
]
#self._fmt = '%d %.4f %.8f %.8f %d %.8f %.8f %.4f %.4f %.4f %.4f'
GibbsSampler.__init__(self, expe, frontend)
def __init__(self, sampler, data_t=None, **kwargs):
self.burnin = kwargs.get('burnin', 0.05) # Ratio of iteration
self.thinning = kwargs.get('thinning', 1)
self.comm = dict() # Empty dict to store communities and blockmodel structure
self.data_t = data_t
self._csv_typo = 'it it_time entropy_train entropy_test K alpha gamma alpha_mean delta_mean alpha_var delta_var'
self.fmt = '%d %.4f %.8f %.8f %d %.8f %.8f %.4f %.4f %.4f %.4f'
#self.fmt = '%s %s %s %s %s %s %s %s %s %s %s'
GibbsSampler.__init__(self, sampler, **kwargs)
def __init__(self, sampler, data_t=None, **kwargs):
self.burnin = kwargs.get('burnin', 0.05) # Ratio of iteration
self.thinning = kwargs.get('thinning', 1)
self.comm = dict(
) # Empty dict to store communities and blockmodel structure
self.data_t = data_t
self._measures = [
'it', 'it_time', 'entropy_train', 'entropy_test', 'K', 'alpha',
'gamma', 'alpha_mean', 'delta_mean', 'alpha_var', 'delta_var'
]
self.fmt = '%d %.4f %.8f %.8f %d %.8f %.8f %.4f %.4f %.4f %.4f'
#self.fmt = '%s %s %s %s %s %s %s %s %s %s %s'
GibbsSampler.__init__(self, sampler, **kwargs)
def __init__(self, expe, frontend):
self.comm = dict(
) # Empty dict to store communities and blockmodel structure
self._csv_typo = '_iteration time_it _entropy _entropy_t _K _alpha _gmma alpha_mean delta_mean alpha_var delta_var'
#self._fmt = '%d %.4f %.8f %.8f %d %.8f %.8f %.4f %.4f %.4f %.4f'
GibbsSampler.__init__(self, expe, frontend)