How to flipped 3D data with 50% chance along x axis?

Question

I read one paper and they mentioned as

flipped data with 50% chance along x-axis.

Given an input data is 40x40x24. How can I perform the above requirement? I am trying the bellow code using python 2.7 but I am not sure about "50% chance" meaning

data_flip = np.flipud(data)
data_flip = data[:, ::-1, :]

Answer 1

First, in order to choose out of n elements with probability p you can simply use: np.random.rand(n) < p . r = np.random.rand() generates a number from a uniform distribution over [0, 1) , so the probability that r is smaller than some constant p (where p is in [0,1]) is exactly p . This probability is actually the CDF of the distribution , which in this case where a=0 and b=1 is:

F(p) = 0, p<0
       p, 0<=p<=1
       1, p>1

Second, to flip the data along the x axis use np.fliplr rather than np.flipud (which flips along the y axis):

# generate a 3D array size 3x3x5
A = np.array([[1,2,3],[4,5,6],[7,8,9]])
A = np.tile( np.expand_dims(A, axis=2), (1,1,5) )
# index the 3rd axis with probability 0.5
p = 0.5
idxs = np.random.rand(A.shape[2]) < p
# flip left-right the chosen arrays in the 3rd dimension
A[:,:,idxs] = np.fliplr(A[:,:,idxs])