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parallel.py
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parallel.py
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from __future__ import division
from IPython.parallel import Client
from IPython.parallel.util import interactive
profile = None
# NOTE: the ipcluster should be set up before this file is imported
from IPython.parallel import Client
c = Client()
dv = c.direct_view()
dv.execute('import pyhsmm')
lbv = c.load_balanced_view()
# this dict needs to be populated by hand before calling build_states*, both
# locally (in this module) and in the ipython top-level module on every engine
# NOTE: the data should probably be arrays with dtype=np.float64
alldata = {}
# this function is run on the engines, and expects the alldata global as well as
# the current model global_model to be present in the ipython global frame
@lbv.parallel(block=True)
@interactive
def build_states(data_id):
global global_model
global alldata
# adding the data to the pushed global model will build a substates object
# and resample the states given the parameters in the model
global_model.add_data(alldata[data_id],initialize_from_prior=False)
stateseq = global_model.states_list[-1].stateseq
global_model.states_list = []
return (data_id, stateseq)
# this stuff is for the 'changepoints' models
allchangepoints = {}
@lbv.parallel(block=True)
@interactive
def build_states_changepoints(data_id):
global global_model
global alldata, allchangepoints
global_model.add_data(alldata[data_id],allchangepoints[data_id],initialize_from_prior=False)
stateseq = global_model.states_list[-1].stateseq
global_model.states_list = []
return (data_id, stateseq)
@lbv.parallel(block=True)
@interactive
def resample_obs_distns(state):
global global_model
global_model.obs_distns[state].resample( ([s.data[s.stateseq == state] for s in global_model.states_list]) )
return global_model.obs_distns[ state ]
@lbv.parallel(block=True)
@interactive
def resample_states(s):
global global_model
global_model.states_list[0].resample()
return global_model.states_list[0]