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Implementation and usage of marginalized stacked denoising autoencoders (mSDA)
Line 11 in msda.py states:
#data format: features are rows, data points are columns
Which is consistent with the call to mSDA() in the example in text_analysis.py.
However line 29 in msda.py uses:
num_features = data.shape[1]
And line 58:
num_data, num_features = data.shape
Both of which are in contradiction with line 11
Could you please check if that's correct?
Thank you.
I may have missed something but it seems in the process_data.py
file you have only created input features as unigrams but the original paper uses both unigram and bigrams. Also I wonder would you be so kind to explain which part of your code reflects the idea which is stated in the paper as combine pivot features and most frequent features since I neither seem to find where the concept of SCL was implemented.
Thank you.
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