Why does this code use only x instead of both x and y in for loop?

Why have they only used X in for loop and not both X and Y? And why we are using reshape with 1, -1?

# implement a loop which computes Euclidean distances between each element in X and Y
# store results in euclidean_distances_vector_l list
X = np.random.uniform( low=lower_boundary, high=upper_boundary, size=(sample_size, n) )
Y = np.random.uniform( low=lower_boundary, high=upper_boundary, size=(sample_size, n) )

for index, x in enumerate(X):
    euclidean_distances_vector_l.append(euclidean_distances(x.reshape(1, -1), Y[index].reshape(1, -1)))

Solution

I haven't used numpy much, but here's my best guess at your questions.

The reason the code only iterates through X instead of X and Y both is because the code isn't pairing each value of X to each value of Y. Instead, it wants each value in X along with its corresponding value in Y. Consider the following example:

X = [0, 1, 2, 3, 4]
Y = [5, 6, 7, 8, 9]

for index, x in enumerate(X):
    print(x, Y[index])

# Prints:
# 0 5
# 1 6
# 2 7
# 3 8
# 4 9

As far as your question regarding reshape, the documentation states that a value of -1 in any parameter indicates that the length of that dimension should be inferred from the length of the original array. My guess then is that x.reshape(1, -1) will restructure x into a 2D array where the length of the first dimension is 1 and the length of the second is as long as it needs to be to hold all the values in x.

X = [1, 2, 3, 4, 5]
X2 = X.reshape(1, -1)

# The value of X2 will be:
# [[1, 2, 3, 4, 5]]