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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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}
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else:
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self.network_args = _args['network_args']
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self._encoder = None
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self._map = None
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self._df = _args['data'] if 'data' in _args else None
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pass
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no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]
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"""
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if self.gpu :
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_args['gpu'] = self.gpu
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g = Generator(**_args)
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# g.run()
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month = np.random.randint(1,13)
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_dates = [self.make_date(year=_year,field=name) for _year in years]
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super().__init__(**_args)
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def instance(**_args):
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