Comments (1)
Thank you for your interest in this repository.
As can be seen in the data_loader file (https://github.com/jsyoon0823/GAIN/blob/master/data_loader.py),
- data_x: original data without missing values.
- miss_data_x: data with missing values (which are represented as np.nan)
- data_m: a binary matrix that represents either missing (0) or observed (1).
During GAIN training,
- we first replace nan values in miss_data_x to 0 (https://github.com/jsyoon0823/GAIN/blob/master/gain.py#L68)
- then, we combine miss_data_x with random matrix here (https://github.com/jsyoon0823/GAIN/blob/master/gain.py#L169)
from gain.
Related Issues (20)
- How to decide Missingness Mechanism HOT 1
- Differences with the paper HOT 1
- Using GAIN in inductive mode HOT 1
- Changing only missing values? and scoring? HOT 1
- Why not both L_G and L_D relevant to V(D,G)? HOT 1
- Could you please provide Requirements.txt file HOT 1
- My dataset is 203454KB, I can't get the dataset after filling, because my dataset is too big? It gives some mistakes. HOT 1
- mixed (categorical and numerical) data HOT 3
- Model for the MNIST dataset HOT 1
- alpha HOT 1
- Hyperparameters training HOT 3
- hyperparameters HOT 3
- RMSE is not stable HOT 1
- RMSE HOT 1
- Hint matrix HOT 1
- Why isn't the loss calculated only with b_i=0 values of the Hints. HOT 2
- No split training and testing sets? HOT 2
- Training Query HOT 1
- about minibatch HOT 1
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from gain.