I have two lists of indices (idx[0] and idx[1]), and I should delete the corresponding rows from numpy array y_test.
y_test
12 11 10
1 2 2
3 2 3
4 1 2
13 1 10
idx[0] = [0,2]
idx[1] = [1,3]
I tried to delete the rows as follows (using ~). But it didn't work:
result = y_test[(~idx[0]+~idx[1]+~idx[2])]
Expected result:
result =
13 1 10
Instead of removing elements, just make a new array with the desired ones. This will keep any future indexing from getting jumbled up and maintain the old array.
import numpy as np
y_test = np.asarray([[12, 11, 10], [1, 2, 2], [3, 2, 3], [4, 1, 2], [13, 1, 10]])
idx = [[0, 2], [1, 3]]
# flatten list of lists
idx_flat = [i for j in idx for i in j]
# assign values that are NOT in your idx list to a new array
result = [row for num, row in enumerate(y_test) if num not in idx_flat]
# cast this however you want it, right now 'result' is a list of np.arrays
print result
[array([13, 1, 10])]
For an understanding of the flatten step using list comprehensions check this out
You can use numpy.delete which deletes the subarrays along the axis.
np.delete(y_test, idx, axis=0)
Make sure that idx.dtype is an integer type and use numpy.astype if not.
Your approach did not work because idx is not a boolean index array but holds the indices. So ~ which is binary negation will produce ~[0, 2] = [-1, -3] (where both should be numpy arrays).
I would definitely recommend reading up on the difference between index arrays and boolean index arrays. For boolean index arrays I would suggest using numpy.logical_not and numpy.logical_or.
+ concatenates Python lists but is the standard plus for numpy arrays.
y_test is (60000,1). Then the length of idx is 6000. Thus I expect the result of np.delete(y_test, idx, axis=0) to have the shape (54000,1), but it is 56846 
Since you are using NumPy I'd suggest masking in this way.
import numpy as np
y_test = np.array([[12,11,10],
[1,2,2],
[3,2,3],
[4,1,2],
[13,1,10]])
idx = np.array([[0,2], [1,3]])
Generate a mask of ones then set to zero elements at index in idx:
mask = np.ones(len(y_test), dtype = int).reshape(5,1)
mask[idx.flatten()] = 0
y_test[~np.all(y_test * mask == 0, axis=1)]
#=> [[13 1 10]]
y_test has not been modified.
+, but&for boolean AND operation~[0,2]supposed to do?[0,2]+[1,3]works to join 2 lists producing[0,2,1,3].idxis a numpy array, using the bitwise negation operator on it is not doing what you think it is.