# Path: tribeflow/dataio.py
# def save_model(out_fpath, model):
# def initialize_trace(trace_fpath, num_topics, num_iter, \
#         from_=0, to=np.inf, initial_assign=None):


from _learn import em
from tribeflow import dataio
from collections import OrderedDict
import numpy as np
import os

    num_topics : int
        The number of latent spaces to learn

    alpha_zh : float
        The value of the alpha_zh hyperparameter

    beta_zs : float
        The value of the beta_zs (beta) hyperaparameter

    kernel : Kernel object
        The kernel to use

    residency_priors : array of float
        The kernel hyper parameters

    num_iter : int
        The number of iterations to learn the model from

    burn_in : int
        The burn_in of the chain
    
    Returns
    -------
    
    A dictionary with the results.
    '''
    Dts, Trace, previous_stamps, Count_zh, Count_sz, \
            count_h, count_z, prob_topics_aux, Theta_zh, Psi_sz, \
            hyper2id, source2id = \

# Path: tribeflow/dataio.py
# def save_model(out_fpath, model):
# def initialize_trace(trace_fpath, num_topics, num_iter, \
#         from_=0, to=np.inf, initial_assign=None):


# Path: tribeflow/learn.py
from _learn import em
from tribeflow import dataio
from collections import OrderedDict
import numpy as np
import os


    num_topics : int
        The number of latent spaces to learn

    alpha_zh : float
        The value of the alpha_zh hyperparameter

    beta_zs : float
        The value of the beta_zs (beta) hyperaparameter

    kernel : Kernel object
        The kernel to use

    residency_priors : array of float
        The kernel hyper parameters

    num_iter : int
        The number of iterations to learn the model from

    burn_in : int
        The burn_in of the chain
    
    Returns
    -------
    
    A dictionary with the results.
    '''
    Dts, Trace, previous_stamps, Count_zh, Count_sz, \
            count_h, count_z, prob_topics_aux, Theta_zh, Psi_sz, \
            hyper2id, source2id = \

            dataio.initialize_trace(trace_fpath, num_topics, num_iter, \