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"""
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(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
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version 1.0.0
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This package serves as a proxy to the overall usage of the framework.
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This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques
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@TODO:
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- Make configurable GPU, EPOCHS
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"""
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import pandas as pd
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import numpy as np
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ROUND_UP = 2
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# _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
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info = {"rows":args['real'].shape[0],"cols":args['real'].shape[1],"name":col,"partition":args['partition']}
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# @TODO: perhaps get the space of values here ... (not sure it's a good idea)
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BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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# Let's cast the type to the original type (it makes the data more usable)
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