def __init__(self, hash_oov_words=False, number_of_samples=10, burn_in_sweeps=5): Inferencer.__init__(self, hash_oov_words) self._number_of_samples = number_of_samples self._burn_in_sweeps = burn_in_sweeps
def __init__(self, hash_oov_words=False, maximum_gamma_update_iteration=50, minimum_mean_change_threshold=1e-3): Inferencer.__init__(self, hash_oov_words) self._maximum_gamma_update_iteration = maximum_gamma_update_iteration self._minimum_mean_change_threshold = minimum_mean_change_threshold
def __init__(self, hyper_parameter_optimize_interval=1, symmetric_alpha_alpha=True, symmetric_alpha_beta=True, ): Inferencer.__init__(self, hyper_parameter_optimize_interval); self._symmetric_alpha_alpha = symmetric_alpha_alpha self._symmetric_alpha_beta = symmetric_alpha_beta
def __init__(self, hash_oov_words=False, maximum_gamma_update_iteration=50, minimum_mean_change_threshold=1e-3 ): Inferencer.__init__(self, hash_oov_words); self._maximum_gamma_update_iteration = maximum_gamma_update_iteration; self._minimum_mean_change_threshold = minimum_mean_change_threshold;
def __init__(self, hyper_parameter_optimize_interval=1, #hyper_parameter_iteration=100, #hyper_parameter_decay_factor=0.9, #hyper_parameter_maximum_decay=10, #hyper_parameter_converge_threshold=1e-6, ): Inferencer.__init__(self, hyper_parameter_optimize_interval);
def __init__(self, hash_oov_words=False, number_of_samples=10, burn_in_sweeps=5 ): Inferencer.__init__(self, hash_oov_words); self._number_of_samples = number_of_samples; self._burn_in_sweeps = burn_in_sweeps;
def __init__( self, hyper_parameter_optimize_interval=1, #hyper_parameter_iteration=100, #hyper_parameter_decay_factor=0.9, #hyper_parameter_maximum_decay=10, #hyper_parameter_converge_threshold=1e-6, ): Inferencer.__init__(self, hyper_parameter_optimize_interval)
def __init__( self, hyper_parameter_optimize_interval=10, symmetric_alpha_alpha=True, symmetric_alpha_beta=True, ): Inferencer.__init__(self, hyper_parameter_optimize_interval) self._symmetric_alpha_alpha = symmetric_alpha_alpha self._symmetric_alpha_beta = symmetric_alpha_beta
def __init__(self, hyper_parameter_optimize_interval=10, symmetric_alpha_alpha=True, symmetric_alpha_beta=True, #local_parameter_iteration=1, ): Inferencer.__init__(self, hyper_parameter_optimize_interval); self._symmetric_alpha_alpha=symmetric_alpha_alpha self._symmetric_alpha_beta=symmetric_alpha_beta
def __init__(self, hyper_parameter_optimize_interval=1, symmetric_alpha_alpha=True, symmetric_alpha_beta=True, #scipy_optimization_method="BFGS", scipy_optimization_method="L-BFGS-B", #scipy_optimization_method = "CG" ): Inferencer.__init__(self, hyper_parameter_optimize_interval); self._symmetric_alpha_alpha = symmetric_alpha_alpha self._symmetric_alpha_beta = symmetric_alpha_beta self._scipy_optimization_method = scipy_optimization_method
def __init__(self, hyper_parameter_optimize_interval=1, ): ''' update_hyper_parameter=True, alpha_update_decay_factor=0.9, alpha_maximum_decay=10, alpha_converge_threshold=0.000001, alpha_maximum_iteration=100, model_likelihood_threshold=0.00001, gamma_converge_threshold=0.000001, gamma_maximum_iteration=20 ''' Inferencer.__init__(self, hyper_parameter_optimize_interval);
def __init__(self, update_hyper_parameter=True, alpha_update_decay_factor=0.9, alpha_maximum_decay=10, alpha_converge_threshold=0.000001, alpha_maximum_iteration=100, model_likelihood_threshold=0.00001, number_of_samples=10, burn_in_samples=5 ): Inferencer.__init__(self, update_hyper_parameter, alpha_update_decay_factor, alpha_maximum_decay, alpha_converge_threshold, alpha_maximum_iteration, model_likelihood_threshold); #self._alpha_update_decay_factor = alpha_update_decay_factor; #self._alpha_maximum_decay = alpha_maximum_decay; #self._alpha_converge_threshold = alpha_converge_threshold; #self._alpha_maximum_iteration = alpha_maximum_iteration; self._number_of_samples = number_of_samples; self._burn_in_samples = burn_in_samples;
def __init__(self, update_hyper_parameter=True, alpha_update_decay_factor=0.9, alpha_maximum_decay=10, alpha_converge_threshold=0.000001, alpha_maximum_iteration=100, model_likelihood_threshold=0.00001, gamma_converge_threshold=0.000001, gamma_maximum_iteration=20 ): Inferencer.__init__(self, update_hyper_parameter, alpha_update_decay_factor, alpha_maximum_decay, alpha_converge_threshold, alpha_maximum_iteration, model_likelihood_threshold); #self._alpha_update_decay_factor = alpha_update_decay_factor; #self._alpha_maximum_decay = alpha_maximum_decay; #self._alpha_converge_threshold = alpha_converge_threshold; #self._alpha_maximum_iteration = alpha_maximum_iteration; self._gamma_maximum_iteration = gamma_maximum_iteration; self._gamma_converge_threshold = gamma_converge_threshold;