def parse_dkf_args(runme_path, conf_path, weight_path):
# DKF uses arguments like "-vm LR -infm structured", etc. This script
# loads those arguments.
new_argv = get_args(runme_path)
new_argv.extend(['-reload', weight_path, '-params', conf_path])
from parse_args_dkf import parse
params = parse(new_argv)
return params
import os, time, sys
""" Add the higher level directory to PYTHONPATH to be able to access the models """
sys.path.append('../')
""" Change this to modify the loadDataset function """
from load import loadDataset
"""
This will contain a hashmap where the
parameters correspond to the default ones modified
by any command line options given to this script
"""
from parse_args_dkf import parse
params = parse()
""" Some utility functions from theanomodels """
from utils.misc import removeIfExists, createIfAbsent, mapPrint, saveHDF5, displayTime
""" Load the dataset into a hashmap. See load.py for details """
dataset = loadDataset()
params['savedir'] += '-template'
createIfAbsent(params['savedir'])
""" Add dataset and NADE parameters to "params"
which will become part of the model
"""
for k in ['dim_observations', 'data_type']:
params[k] = dataset[k]
mapPrint('Options: ', params)
if params['use_nade']:
params['data_type'] = 'binary_nade'
"""
import DKF + learn/evaluate functions
"""
start_time = time.time()
from stinfmodel.dkf import DKF
import os,time,sys
sys.path.append('../')
import numpy as np
from datasets.load import loadDataset
from parse_args_dkf import parse; params = parse()
from utils.misc import removeIfExists,createIfAbsent,mapPrint,saveHDF5,displayTime
params['dim_stochastic'] = 1
if params['dataset']=='':
params['dataset']='synthetic9'
dataset = loadDataset(params['dataset'])
params['savedir']+='-'+params['dataset']
createIfAbsent(params['savedir'])
#Saving/loading
for k in ['dim_observations','dim_actions','data_type']:
params[k] = dataset[k]
mapPrint('Options: ',params)
#Setup VAE Model (or reload from existing savefile)
start_time = time.time()
from stinfmodel.dkf import DKF
import stinfmodel.evaluate as DKF_evaluate
import stinfmodel.learning as DKF_learn
displayTime('import DKF',start_time, time.time())
dkf = None
#Remove from params
start_time = time.time()
