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%load_ext autoreload
%autoreload 2
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import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import hashlib
import time
import shutil
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MNM_nb_folder = os.path.join('..', '..', '..', 'side_project', 'network_builder')
sys.path.append(MNM_nb_folder)
python_lib_folder = os.path.join('..', '..', 'pylib')
sys.path.append(python_lib_folder)
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from MNM_nb import *
import MNMAPI
from DODE import *
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data_folder = os.path.join('..', '..', '..', 'data', 'input_files_2link_fix')
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nb = MNM_network_builder()
nb.load_from_folder(data_folder)
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config = dict()
config['use_link_flow'] = True
config['use_link_tt'] = False
config['link_flow_weight'] = 1
config['link_tt_weight'] = 1
config['num_data'] = 10
config['observed_links'] = [3]
config['paths_list'] = [0, 1]
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x_real = np.random.rand(10) * 10
data_dict = dict()
data_dict['link_flow'] = list()
for i in range(config['num_data']):
link_df = pd.DataFrame(index = range(10), columns = [3], data = x_real + np.random.randn(10) / 10)
data_dict['link_flow'].append(link_df)
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pdode = PDODE(nb, config)
dode = DODE(nb, config)
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pdode.add_data(data_dict)
dode.add_data(data_dict)
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pdode.estimate_path_flow(init_scale = 0.1, step_size = 0.1, max_epoch = 100, adagrad = False)
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dode.estimate_path_flow(init_scale = 0.1, step_size = 0.1, max_epoch = 100, adagrad = False)
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