How can we create an array with n elements. zeros function can create only arrays of dimensions greater than or equal to 2? zeros(4), zeros([4]) and zeros([4 4]) all create 2D zero matrix of dimensions 4x4.
I have a code in Python where I have used numpy.zeros(n). I wish to do something similar in Octave.
zeros(n,1) works well for me in Octave.
A vector is a row vector in Octave. So if you want to create a vector of zeros, you need to use the command:
b = zeros(1,n)
The command
c = zeros(n,1)
will create a column vector of zeros.
Related
I have a (square) 2 dimensional numpy array where I would like to compare (subtract) all of the values within each row to each other but not to other rows so the output should be a 3D array.
matrix = np.array([[10,1,32],[32,4,15],[6,3,1]])
Output should be a 3x3x3 array which looks like:
output = [[[0,-9,22],[0,-28,-17],[0,-3,-5]], [[9,0,31],[28,0,11],[3,0,-2]], [[-22,-31,0],[17,-11,0],[5,2,0]]]
I.e. for output[0], for each of the 3 rows of matrix, subtract that row's zeroth element from every other, for output[1] subtract each row's first element etc.
This seems to me like a reduced version of numpy's ufunc.outer functionality which should be possible with
tryouter = np.subtract(matrix, matrix)
and then taking some clever slice and/or transposition.
Indeed, if you do this, one finds that: output[i,j] = tryouter[i,j,i]
This looks like it should be solvable by using np.transpose to switch the 1 and 2 axes and then taking the arrays on the new 0,1 diagonal but I can't work out how to do this with numpy diagonal or any slicing method.
Is there a way to do this or is there a simpler approach to this whole problem built into numpy?
Thanks :)
You're close, you can do it with broadcasting:
out = matrix[None, :, :] - matrix.T[:, :, None]
Here .T is the same as np.transpose, and using None as an index introduces a new dummy dimension of size 1.
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.
My goal is to to turn a row vector into a column vector and vice versa. The documentation for numpy.ndarray.transpose says:
For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.)
However, when I try this:
my_array = np.array([1,2,3])
my_array_T = np.transpose(np.matrix(myArray))
I do get the wanted result, albeit in matrix form (matrix([[66],[640],[44]])), but I also get this warning:
PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
my_array_T = np.transpose(np.matrix(my_array))
How can I properly transpose an ndarray then?
A 1D array is itself once transposed, contrary to Matlab where a 1D array doesn't exist and is at least 2D.
What you want is to reshape it:
my_array.reshape(-1, 1)
Or:
my_array.reshape(1, -1)
Depending on what kind of vector you want (column or row vector).
The -1 is a broadcast-like, using all possible elements, and the 1 creates the second required dimension.
If your array is my_array and you want to convert it to a column vector you can do:
my_array.reshape(-1, 1)
For a row vector you can use
my_array.reshape(1, -1)
Both of these can also be transposed and that would work as expected.
IIUC, use reshape
my_array.reshape(my_array.size, -1)
I have 2 multidimensional arrays , both of size 128X640X5. 5 is the number of channels for the matrices. I wish to add the respective channel values of both the matrices for every point in the matrices. For eg if we have A and B as 2 matrices, I wish to do an operation something like this:
A(x,y,0)+B(x,y,0) =A(x,y,0). This should add the 0th channel values of points x and y in both A and B and then store it back in A. Similiary I wish to do it for other 4 channels also. Any idea on how to do this in python? I am using numpy array of python and basically working on image manipulation problem.
In order to add each corresponding point of a ndarray in numpy you can use numpy's add function (numpy.add).
It will add up each corresponding point of two ndarrays which have the same shape. If the arrays do not have the same shape, it will broadcast (change their shape) so that they can be added together.
In your case, you would simply write:
import numpy as np
C = np.add(A, B)
Source:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.add.html
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