Open FarshidShekari opened 5 years ago
K is the number of features you want to select, and so after you transform, what you get is the input with only the selected features.
this is my code:
selector.fit(X_tr, Y_tr, X_va, Y_va)
X_tr = selector.transform(X_tr)
print(X_tr.shape)
My trainning data has 20000 sample and 250 features, but after transform I get X_tr
with shape (k,250)
and not (20000,k)
, what's the problem?
Can you confirm that X_tr has the shape (20000, 250)?
Actually, I looked at the code and there is a bug, you can use this instead: X_tr[:, selector.get_support(indices=true)]
Can you confirm that X_tr has the shape (20000, 250)?
Yes
Actually, I looked at the code and there is a bug, you can use this instead: X_tr[:, selector.get_support(indices=true)]
I want to transform my data to new feature space because I want to feed it to my DNN.
@FarshidShekari could you find a solution to transform your data into a new feature space?
Hello I have a question about figure 1 from your paper. How is reconstruction supposed to be done? I only understand up to part c where k=20 can be used to mask the original image. How is it you can reconstruct image as in part d.
I applied on my dataset and that's shape changed to (K, N), that
k
is input to your model andn
is my X_train data features number. I supposedtransform
method is like dimensions reduction transform methods.