How to create 3 dimensions matrix in numpy , like matlab a(:,:,:) . I try to convert matlab code that create 3d matrix to python by use numpy.array and i don't know how to create 3d matrix/array in numpy
a=np.empty((2,3,5))
creates a 2x3x5 array. (There is also np.zeros if you want the values initialized.)
You can also reshape existing arrays:
a=np.arange(30).reshape(2,3,5)
np.arange(30) creates a 1-d array with values from 0..29. The reshape() method returns an array containing the same data with a new shape.
Related
I create a masked array A with shape (8,1,51,120) and a data masked array B with shape (11232,51,120).
Now I make a loop to select bar of B[step,:,:] array and append it at the end of A[foo,0,:,:]. Here is some pseudo-code to describe what I want:
for step in range(B.shape[0]):
foo = select_func(step)
A[foo,A.shape[1]-1,:,:].append(B[step,:,:])
print(A[foo,:,:,:].shape)
Finally I want A[foo,:,:,:].shape = (bar,51,120).
It turns out very hard to append B to A using np.ma.dstack( or np.ma.append( not only because A not change as I do but also it made new masked array.
I try this: Append 2D array to 3D array, extending third dimension, but I want my original A array grow up not to make new array.
Suppose you have a higher dimensional array (3 or greater) which is composed of a series of 2d images. If this array is called x, then a 2d image will be represented as x[0,0,:,:]. Now what I want to do is apply a function that takes in a 2d image and outputs a scalar, on this higher dimensional array so that I would convert the dimension of the original array to one that is 2 dimensions lower. How would I do such a thing?
In other words, what is the faster numpy way of doing this: np.array([[f(x[i,j,:,:]) for i in range(x.shape[0])] for j in range(x.shape[1])]) for a list of axes and some function f that takes in an array.
I've looked at numpy.apply_along_axis but that only acts on a 1d array and the shape must be identical. numpy.apply_on_axes also doesn't work since it doesn't reduce the amount of dimensions which are given to the function (it gives my function a 4d array, not a 2d array which I can work with). numpy.vectorize doesn't work because it doesn't ever apply on more than one element at once.
I have a 2d array of shape (512,512). I need to convert this to shape (512,512,3). All values of 2d dimension will be repeated on other two dims. How can I do this in python?
you can try using np dstack
it would work for your case
np.dstack([a,a,a])
I would use array[..., None].repeat(3, -1)
I have a pandas dataframe where one column is labeled "feature_vector" and contains in it a 1d numpy array with a bunch of numbers. Now, I am needing to use this data in an scikit learn model, so I need it as a single numpy array. So naturally I call DataFrame["feature_vector"].as_matrix() to get the numpy array from the correct series. The only problem is, the as_matrix() function will return an 1d numpy array where each element is an 1d numpy array containing each vector. When this is passed to an sklearn model's .fit() function, it throws an error. What I instead need is a 2d numpy array rather than the 1d array of 1d arrays. I wrote this work around, which uses presumably unnecessary memory and computation time:
x = dataframe["feature_vector"].as_matrix()
#x is a 1d array of 1d arrays.
l = []
for e in x:
l.append(e)
x = np.array(l)
#x is now a single 2d array.
Is this a bug in pandas .as_matrix()? Is there a better work around that doesn't require me to change the structure of the original dataframe?
I have been struggling with changing a 2D numpy array to a 2D numpy matrix. I know that I can use numpy.asmatrix(x) to change array x into a matrix, however, the size for the matrix is not the size I wish to have. For example, I want to have a numpy.matrix((2,10)). It is easier for me to use two separate numpy.arrays to form each rows of the matrix. then I used numpy.append to put these two arrays into a matrix. However, when I use numpy.asmatrix to make this 2d array into a 2d matrix, the size is not the same size as my matrix (my desired matrix should have a size of 2*10 but when I change arrays to matrix, the size is 1*2). Does anybody know how I can change size of this asmatrix to my desired size?
code (a and b are two numpy.matrix with size of (1*10)):
m=10
c=sorted(random.sample(range(m),2))
n1=numpy.array([a[0:c[0]],b[c[0]:c[1]],a[c[1]:]])
n2=numpy.array([b[0:c[0]],a[c[0]:c[1]],b[c[1]:]])
n3=numpy.append(n1,n2)
n3=numpy.asmatrix(n3)
n1 and n2 are each arrays with shape 3 and n3 is matrix with shape 6. I want n3 to be a matrix with size 2*10
Thanks