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"""
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(c) 2018 - 2021, Vanderbilt University Medical Center
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Steve L. Nyemba, steve.l.nyemba@vumc.org
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This file is designed to handle preconditions for a generative adversarial network:
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- The file will read/get data from a source specified by transport (or data-frame)
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- The class will convert the data to a binary vector
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- The class will also help rebuild the data from a binary matrix.
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Usage :
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"""
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import transport
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import json
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import pandas as pd
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import numpy as np
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def __init__(self,**_args):
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This function will initialize the class on the basis of a data-store and optionally pre-defined columns to be used to be synthesized
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:param store data-store configuration
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:param columns list of columns to be
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"""
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if 'columns' not in _args :
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self._initcols(data=self.df)
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else:
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self._initcols(data=self.df,columns=_args['columns'])
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pass
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# MIN_SPACE_SIZE = 2
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# if not _column :
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# _matrix = matrix[:,_beg:_end] #-- The understanding is that _end is not included
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# else:
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# _matrix = matrix
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_matrix = matrix[:,_beg:_end]
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#
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# vectorize the matrix to replace the bits by their actual values (accounting for the data-types)
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# @TODO: Find ways to do this on a GPU (for big data) or across threads
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#
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row_count = _matrix.shape[0]
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# r[key] = [columns[np.where(row == 1) [0][0] ] for row in _matrix[:,_beg:_end]]
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r[key] = [columns[np.where(row==1)[0][0]] if np.where(row==1)[0].size > 0 else '' for row in _matrix]
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