From ffc4a8a191cdcf88c0ac25a64dd509fa5797f203 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 31 Dec 2019 23:38:52 -0600 Subject: [PATCH] layout issue --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index f5c5e5d..46d2425 100644 --- a/README.md +++ b/README.md @@ -31,14 +31,14 @@ The trainer will store the data on disk (for now) in a structured folder that wi **Generate a candidate dataset from the learned features** - import pandas as pd - import data.maker + import pandas as pd + import data.maker - df = pd.read_csv('sample.csv') - id = 'id' - column = 'gender' - context = 'demo' - data.maker.generate(data=df,id=id,column=column,logs='logs') + df = pd.read_csv('sample.csv') + id = 'id' + column = 'gender' + context = 'demo' + data.maker.generate(data=df,id=id,column=column,logs='logs') ## Limitations @@ -49,7 +49,7 @@ GANS will generate data assuming the original data has all the value space neede Assuming we have a dataset with an gender attribute with values [M,F]. The synthetic data will not be able to generate genders outside [M,F] - + - Not advised on continuous values GANS work well on discrete values and thus are not advised to be used.