# 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
# def prepare_results(trace_fpath, num_topics, alpha_zh, beta_zs, \
#         kernel, residency_priors, num_iter, burn_in, Dts, Trace, \
#         Count_zh, Count_sz, count_h, count_z, \
#         prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id, \
#         from_, to):
# def fit(trace_fpath, num_topics, alpha_zh, beta_zs, kernel, \
#         residency_priors, num_iter, burn_in, from_=0, to=np.inf):


from tribeflow import dataio
from tribeflow import learn
from tribeflow.tests import files
from tribeflow.kernels import NoopKernel
from numpy.testing import assert_equal
from numpy.testing import assert_almost_equal
import numpy as np
#-*- coding: utf8
'''
Unit tests for the main sherlock model.
'''
from __future__ import division, print_function





def test_full_learn_null():
    kernel = NoopKernel()
    kernel.build(1, 20, np.zeros(0, dtype='d'))

# 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
# def prepare_results(trace_fpath, num_topics, alpha_zh, beta_zs, \
#         kernel, residency_priors, num_iter, burn_in, Dts, Trace, \
#         Count_zh, Count_sz, count_h, count_z, \
#         prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id, \
#         from_, to):
# def fit(trace_fpath, num_topics, alpha_zh, beta_zs, kernel, \
#         residency_priors, num_iter, burn_in, from_=0, to=np.inf):


# Path: tribeflow/tests/test_node_sherlock.py
from tribeflow import dataio
from tribeflow import learn
from tribeflow.tests import files
from tribeflow.kernels import NoopKernel
from numpy.testing import assert_equal
from numpy.testing import assert_almost_equal
import numpy as np

#-*- coding: utf8
'''
Unit tests for the main sherlock model.
'''
from __future__ import division, print_function





def test_full_learn_null():
    kernel = NoopKernel()
    kernel.build(1, 20, np.zeros(0, dtype='d'))

    rv = learn.fit(files.SIZE1K, 20, .1, .1, kernel, \