Numpy unfilter the data by original indexes
0
I have multi-dimentional data X and their corresonding labels y. I filter X by classes in the following way;
import numpy as np
np.random.seed(0)
X = np.random.rand(220,22,1125)
y = np.random.randint(4, size=(220))
# Class indexes
index_class0 = np.where(y==0)[0]
index_class1 = np.where(y==1)[0]
index_class2 = np.where(y==2)[0]
index_class3 = np.where(y==3)[0]
# Filtering X by classes
X0 = X[index_class0,:,:]
X1 = X[index_class1,:,:]
X2 = X[index_class2,:,:]
X3 = X[index_class3,:,:]
# Assume some operations are performed on X0-X3
# TODO: reconstruct X using X0-X3, having same class indexes.
Now given X0,X1,X2 & X3 and corresponding class indexes, how can I reconstruct X keeping in view that the class order remains same?
python numpy scipy
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
asked Nov 24 '18 at 7:24
ShahShah
134
add a comment |
0
I have multi-dimentional data X and their corresonding labels y. I filter X by classes in the following way;
import numpy as np
np.random.seed(0)
X = np.random.rand(220,22,1125)
y = np.random.randint(4, size=(220))
# Class indexes
index_class0 = np.where(y==0)[0]
index_class1 = np.where(y==1)[0]
index_class2 = np.where(y==2)[0]
index_class3 = np.where(y==3)[0]
# Filtering X by classes
X0 = X[index_class0,:,:]
X1 = X[index_class1,:,:]
X2 = X[index_class2,:,:]
X3 = X[index_class3,:,:]
# Assume some operations are performed on X0-X3
# TODO: reconstruct X using X0-X3, having same class indexes.
Now given X0,X1,X2 & X3 and corresponding class indexes, how can I reconstruct X keeping in view that the class order remains same?
python numpy scipy
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
asked Nov 24 '18 at 7:24
ShahShah
134
usenp.argwhereto get indices of the objects, then extract the elements you want to modify, modify them, them put them back using your stored indices forX0, X1, ...
– kevinkayaks
Nov 24 '18 at 7:36
Question has nothing to do withmachine-leraningorscikit-learn- kindly do not spam the tags (removed).
– desertnaut
Nov 24 '18 at 8:10
add a comment |
0
0
0
I have multi-dimentional data X and their corresonding labels y. I filter X by classes in the following way;
import numpy as np
np.random.seed(0)
X = np.random.rand(220,22,1125)
y = np.random.randint(4, size=(220))
# Class indexes
index_class0 = np.where(y==0)[0]
index_class1 = np.where(y==1)[0]
index_class2 = np.where(y==2)[0]
index_class3 = np.where(y==3)[0]
# Filtering X by classes
X0 = X[index_class0,:,:]
X1 = X[index_class1,:,:]
X2 = X[index_class2,:,:]
X3 = X[index_class3,:,:]
# Assume some operations are performed on X0-X3
# TODO: reconstruct X using X0-X3, having same class indexes.
Now given X0,X1,X2 & X3 and corresponding class indexes, how can I reconstruct X keeping in view that the class order remains same?
python numpy scipy
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
asked Nov 24 '18 at 7:24
ShahShah
134
I have multi-dimentional data X and their corresonding labels y. I filter X by classes in the following way;
import numpy as np
np.random.seed(0)
X = np.random.rand(220,22,1125)
y = np.random.randint(4, size=(220))
# Class indexes
index_class0 = np.where(y==0)[0]
index_class1 = np.where(y==1)[0]
index_class2 = np.where(y==2)[0]
index_class3 = np.where(y==3)[0]
# Filtering X by classes
X0 = X[index_class0,:,:]
X1 = X[index_class1,:,:]
X2 = X[index_class2,:,:]
X3 = X[index_class3,:,:]
# Assume some operations are performed on X0-X3
# TODO: reconstruct X using X0-X3, having same class indexes.
Now given X0,X1,X2 & X3 and corresponding class indexes, how can I reconstruct X keeping in view that the class order remains same?
python numpy scipy
python numpy scipy
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
asked Nov 24 '18 at 7:24
ShahShah
134
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
asked Nov 24 '18 at 7:24
ShahShah
134
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
edited Nov 24 '18 at 8:09
desertnaut
16.8k63566
16.8k63566
asked Nov 24 '18 at 7:24
ShahShah
134
asked Nov 24 '18 at 7:24
ShahShah
134
asked Nov 24 '18 at 7:24
ShahShah
134
134
usenp.argwhereto get indices of the objects, then extract the elements you want to modify, modify them, them put them back using your stored indices forX0, X1, ...
– kevinkayaks
Nov 24 '18 at 7:36
Question has nothing to do withmachine-leraningorscikit-learn- kindly do not spam the tags (removed).
– desertnaut
Nov 24 '18 at 8:10
add a comment |
usenp.argwhereto get indices of the objects, then extract the elements you want to modify, modify them, them put them back using your stored indices forX0, X1, ...
– kevinkayaks
Nov 24 '18 at 7:36
Question has nothing to do withmachine-leraningorscikit-learn- kindly do not spam the tags (removed).
– desertnaut
Nov 24 '18 at 8:10
use
np.argwhere to get indices of the objects, then extract the elements you want to modify, modify them, them put them back using your stored indices for X0, X1, ...– kevinkayaks
Nov 24 '18 at 7:36
use
np.argwhere to get indices of the objects, then extract the elements you want to modify, modify them, them put them back using your stored indices for X0, X1, ...– kevinkayaks
Nov 24 '18 at 7:36
Question has nothing to do with
machine-leraningor scikit-learn - kindly do not spam the tags (removed).– desertnaut
Nov 24 '18 at 8:10
Question has nothing to do with
machine-leraningor scikit-learn - kindly do not spam the tags (removed).– desertnaut
Nov 24 '18 at 8:10
add a comment |
1 Answer
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We already know the indices of different classes in the original array. So just make an empty array and put the X's for classes in their correct place.
reconstructed_X = np.zeros(X.shape)
reconstructed_X[index_class0] = X0
reconstructed_X[index_class1] = X1
reconstructed_X[index_class2] = X2
reconstructed_X[index_class3] = X3
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
We already know the indices of different classes in the original array. So just make an empty array and put the X's for classes in their correct place.
reconstructed_X = np.zeros(X.shape)
reconstructed_X[index_class0] = X0
reconstructed_X[index_class1] = X1
reconstructed_X[index_class2] = X2
reconstructed_X[index_class3] = X3
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
add a comment |
0
We already know the indices of different classes in the original array. So just make an empty array and put the X's for classes in their correct place.
reconstructed_X = np.zeros(X.shape)
reconstructed_X[index_class0] = X0
reconstructed_X[index_class1] = X1
reconstructed_X[index_class2] = X2
reconstructed_X[index_class3] = X3
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
add a comment |
0
0
0
We already know the indices of different classes in the original array. So just make an empty array and put the X's for classes in their correct place.
reconstructed_X = np.zeros(X.shape)
reconstructed_X[index_class0] = X0
reconstructed_X[index_class1] = X1
reconstructed_X[index_class2] = X2
reconstructed_X[index_class3] = X3
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
We already know the indices of different classes in the original array. So just make an empty array and put the X's for classes in their correct place.
reconstructed_X = np.zeros(X.shape)
reconstructed_X[index_class0] = X0
reconstructed_X[index_class1] = X1
reconstructed_X[index_class2] = X2
reconstructed_X[index_class3] = X3
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
edited Nov 24 '18 at 8:03
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
answered Nov 24 '18 at 7:52
Deepak SainiDeepak Saini
1,582814
1,582814
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
add a comment |
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
we have to reconstruct X from X0,X1,X2,X3 and not using X
– Shah
Nov 24 '18 at 7:54
add a comment |
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use
np.argwhereto get indices of the objects, then extract the elements you want to modify, modify them, them put them back using your stored indices forX0, X1, ...– kevinkayaks
Nov 24 '18 at 7:36
Question has nothing to do with
machine-leraningorscikit-learn- kindly do not spam the tags (removed).– desertnaut
Nov 24 '18 at 8:10