Develop Reference

Determinant over a specific axis using numpy

Suppose I have a numpy array A with shape (j,d,d) and I want to obtain an array with shape j, in which each entry corresponds to the determinant of each (d,d) array.
I tried using np.apply_along_axis(np.linalg.det(A), axis=0), but np.apply_along_axis only seems to work for 1D slices.
Is there an efficient way of doing that using only numpy?

np.linalg.det can already do this for an array of arbitrary shape as long as the last two dimensions are square. You can see the documentation here.

How can I combine my three 2D tensors into a single 3D tensor in tensor flow?

Hello I am a newbie with the tensorflow and currently, I am working with colour Images and it's PCAS.
I have extracted PCAS in a form of "Red","Green" and "Blue" and also computed the weights which are associated with "Red","Green" and "Blue" components.
After doing the all the above stuff I want to combine all three 2D matrices into the single 3D matrix.
For a tensorflow it would be a 3D tensor.
def multi(h0,ppca,mu,i,scope=None):
with tf.variable_scope(scope or"multi"):
return tf.matmul(ppca[:,:,0],h0[i,:,:,0]) + tf.reshape(mu[:,0],[4096,1]) , tf.matmul(ppca[:,:,1],h0[i,:,:,1]) + tf.reshape(mu[:,1],[4096,1]) ,tf.matmul(ppca[:,:,2],h0[i,:,:,2]) + tf.reshape(mu[:,2],[4096,1])
So from the above function, I will get all three different 2D tensors and want to combine those 2D tensors to single 3D tensor which has dimensions [4096,1,3]
How can I do that?
any help is highly appreciated.

You need to concat them like this:
three_d_image = tf.concat(0, [[r], [g], [b]])
This tells tensorflow to concat them along the x dimension and treat each tensor as a matrix.
Doing the same without the additional brackets around the r,g,b tensors will try to concat them to one large 2D matrix

A clean, easy way to do it is using the tf.stack operation (tf.pack in older versions of tensorflow), it concatenats all tensors along a new dimension. If you want your new dimension to be after all previous, you need to set the axis argument to the number of dimensions of your tensors.
three_d_image = tf.stack([r,g,b], axis=2)

one of the solutions is that you can add one more empty dimension to your 2Ds so you will have 3 matrices of 3D dimension [4096,1,1] then you can concat these 3 matrices by axis 2 tf.concat(2,matrices) gives you [4096,1,3]
the second solution can be concat of axis 1, tf.concat(1,matrices) then reshape it to 3D

store images of different dimension in numpy array

I have two images , image 1 of dimension (32,43,3) and image2 of dimension (67,86,3) . How can i store this in a numpy array , Whenever i try to append the array
image=cv2.imread(image1,0)
image=cv2.resize(image,(32,43))
x_train=np.array(image.flatten())
x_train=x_train.reshape(-1,3,32,43)
X_train =np.append(X_train,x_train) #X_train is my array
image=cv2.imread(image2,0)
image=cv2.resize(image,(67,86))
x_train=np.array(image.flatten())
x_train=x_train.reshape(-1,3,67,86)
X_train =np.append(X_train,x_train)
Value Error: total size of new array must be unchanged.
i want the X_train in shape (-1,depth,height,width).So that i can feed it into my neural network. Is there any way to store images of different dimension in array and feed into neural network ?

Don't use np.append. If you must join arrays, start with np.concatenate. It'll force you to pay more attention to the compatibility of dimensions.
You can't join 2 arrays with shapes (32,43,3) (67,86,3) to make a larger array of some compatible shape. The only dimension they share is the last.
These reshapes don't make sense either: (-1,3,32,43), (-1,3,67,86).
It works, but it also messes up the 'image'. You aren't just adding a 4th dimension. It looks like you want to do some axis swapping or transpose as well. Practice with some small arrays so you can see what's happening, e.g. (2,4,3).
What final shape do you expect for Xtrain?
You can put these two images in a object dtype array, which is basically the same as the list [image1, image2]. But I doubt if your neuralnet can do anything practical with that.
If you reshaped the (32,43,3) array to (16,86,3) you could concatenate that with (67,86,3) on axis=0 to produce a (83,86,3) array. If you needed the 3 to be first, I'd use np.transpose(..., (2,0,1)).
Conversely reshape (67,86,3) to (2*67,43,3).
Passing the (32,43,3) to (32,86,3) is another option.
Joining them on a new 4th dimension, requires that the number of 'rows' match as well as the number of 'columns'.

numpy transformation 2d to 3d

I m working on audio stuff with python and I have the data into a 2d array with left and right channels in two different dimensions
so my array shape looks like this (exemple) :
(360448, 2)
I'm trying to find a way to reshape the array so the data fits into a 3d array where each cell represent each sample in this way : [left_sample,right_sample,0] so the final array will have shape (X,X,3)
is there a way to reshape the array( without iterating and assigning the values) ?

Computing average across a list of MxN arrays

I'm still getting the hang of working with numpy and array-wise operations.
I'm looking for the way of getting the row-wise average of a list of 2D arrays.
E.g I have a 4x3x25 array and I'm looking to get a 3x25 array of the row-wise averages.

If everything’s in one 3D array already, you can just do:
A.mean(axis=0)
…which will operate along the first dimension.
If it’s actually just a list of 2D arrays, you’ll have to convert it to a 3D array first. I would do:
A = np.dstack(list_of_arrays) # Combine the 2D arrays along a new 3rd dimension
A.mean(axis=2) # Calculate the means along that new dimension