I have two arrays A1(5,3) and A2(5,2) and want to create a new array A(5,5). I've try to use A=np.concatenate((A1,A2)) but it gives me the error
all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3 and the array at index 1 has size 2
How can I solve this?
To use np.concatenate you should specify on which axis the concatenation is happening. In this case, you should use:
A = np.concatenate((A1, A2), axis=1)
Other way to solve this issue is to use horizontal stack:
A = np.hstack((A1, A2))
Related
This question has been already asked and i have followed using the given solution.
Please refer to my code from github. Link given below.
I have already tried to use
np.c_[X, family_mems, marital_status, age_group]
np.concatenate((X, family_mems, marital_status, age_group), axis=1)
np.column_stack((X, family_mems, marital_status, age_group))
The X has shape of (891,8)
So, I also tried to reshape 1-d arrays to (891,1), but even then it doesn't seem to concatenate.
https://github.com/dvinci35/titanic_ml_kaggle.git
I am getting the error ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 3 and the array at index 1 has size 1 while running the below code.
for i in range(6):
print('current b', current_batch)
current_pred = model.predict(current_batch)[0]
print('current pred', current_pred)
test_predictions.append(current_pred)
print('current batch', current_batch)
print('current batch => ', current_batch[:,1:,:])
current_batch = np.append(current_batch[:,1:,:], [[current_pred]], axis=1)
getting this error
Can anyone please explain me why this is happening.
Thanks,
Basically, Numpy is telling you that the shapes of the concatenated matrices should align. For example, it is possible to concatenate a 3x4 matrix with 3x5 matrix so that we get 3x9 matrix (we added dimension 1).
The problem here is that Numpy is telling you that the axis don't align. In my example, that would be trying to concatenate 3x4 matrix with 10x10 matrix. This is not possible as the shapes are not aligned.
This usually means that the you are trying to concatenate wrong things. If you are sure though, try using np.reshape function, which will change the shape of one of the matrices so that they can be concatenated.
As the traceback shows, np.append is actually using np.concatenate. Did you read (study) the docs for either function? Understand what they say about dimensions?
From the display [[current_pred]], converted to array will be (1,1,1) shape. Do you understand that?
current_batch[:,1:,:] is, as best I can tell from the small image (1,5,3)
You are asking to join on axis 1, which is 1 and 5, ok. But it's saying that the last dimension, axis 2, doesn't match. That 1 does not equal 3. Do you understand that?
List append as you do with test_predictions.append(current_pred) works well in an iteration.
np.append does not work well. Even when it works, it is slow. And here it doesn't work, because you aren't taking sufficient care to match dimensions.
I have an issue with numpy arrays and I can't understand what I am doing wrong. I need to create a 100x100 matrix with random int (non zero) and the last row should be the combination of all previous rows. Here is my code:
non_zero_m = np.random.randint(0,10,(99,100))
arr = non_zero_m.sum(axis=0)
singular_m = np.concatenate((non_zero_m, arr))
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)
I can't understand why python shows that arrays has different dimensions
The problem is that arr is a 1-dimensional array, and you are trying to concatenate it to a matrix (2-dimensional).
Just replace the second line with:
arr = non_zero_m.sum(axis=0).reshape(1, -1)
This reshapes arr to a 2-dimensonal array, such that the first axis has dimension 1 (thus making arr effectively a row vector), and the second axis has the required dimension to keep all of arr's elements (this is the meaning of -1 in this context).
I have four Numpy arrays of shapes:
(2577, 42)
(2580, 100)
(2580, 236)
(2580, 8)
(2580, 37)
When I try to concatenate all of them do except (2577, 42). I get an error:
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 2580 and the array at index 4 has size 2577
The code I am using:
dataset = np.concatenate((onehot_b, num_v, onehot_s, onehot_c, onehot_s), axis=1)
Is there a way to fix this?
The error is prety clear. You Cannot concatenate arrays of different sizes. One possible way out is convert the numpy arrays to lists and append all list lines to you dataset.
Numpy does not allow non-rectangular arrays, meaning that all sub-arrays should have the same dimension along the same axis. In your case, 2577 and 2580, are dimensions along same axis=0 that you are not stacking over (hence not adding them along that axis and they should have same length). If you can change all of them to have same first dimension shape, you can use concatenate. If you insist on stacking them, another way is just stacking arrays rather than their content:
dataset = np.asarray([onehot_b, num_v, onehot_s, onehot_c, onehot_s])
This will create an array of arrays for you.
This is an example of my error. Say i created a numpy array
X = np.zeros((1000, 50))
Where 1000 is the features (rows) and 50 is the examples (columns)
Since i am adding examples one by one i will have to replace columns in the array 1 by 1 to get the final feature array. I tried this:
X[:,i] = example
where example is of size (1000, 1), and i is iterated for every example. This does not work because X[:,i] is of shape (1000,), a rank 1 array. How do i code it so that each example replaces a row of the X array without throwing the broadcast error. Thank you.
Reshape your vector before assigning it.
X[:,i] = example.reshape(-1,)
This will suppress the second dimension and turn example into shape (1000,)
Or, avoiding assigning one by one in the loop you can put all of your arrays in a list and then call np.array on your list and transpose it to have them as columns. This will probably work better if you can construct your list of arrays in a list comprehension.
Example:
arrs = [np.random.randint(10, size=5) for _ in range(5)]
X = np.array(arrs).T