Asking Numpy. Why i can get result this?

user_16369275 0
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I'm reading the book Introduction Machine Learning to Python and this code is on page 69. I can't understand that why I can get a result of this.

X = np.array([[0, 1, 0, 1],
              [1, 0, 1, 1],
              [0, 0, 0, 1],
              [1, 0, 1, 0]])
y = np.array([0, 1, 0, 1])

for label in np.unique(y):
    print(X[y == label])

result:

[[0 1 0 1]
 [0 0 0 1]]
[[1 0 1 1]
 [1 0 1 0]]
numpy python
1 Answers

Let's break this down piece by piece.

  1. np.unique(y)

    np.unique returns all of the unique elements of the given array. In this case, that's [0, 1] (since there are only two unique elements in y, 0 and 1).

    So running

    for label in np.unique(y):

    will iterate twice. The first time label will equal 0, and the second time it will equal 1. You can inspect this for yourself by running

    for label in np.unique(y):
    print(label)

  2. y == label

    If you run a comparison against an array, numpy will return a boolean array of the same size. So on the first iteration of the for loop, it'll be running y == 0, which gives us [ True False True False], since the first and third element in y are 1. Then on the second iteration, it'll be running y == 1, giving us the inverse, [False True False True].

    You can inspect this for yourself by running:

    for label in np.unique(y):
    print(y == label)

  3. X[y == label]

    Now that we know what the rest of the logic is doing, the last piece is determining what happens when passing the y == label as a "selector" for elements in X. When you pass a boolean array as a selector, you're telling numpy which elements of the original array you want to return. In this case, X is a 2-dimensional array (size 4x4), and if we pass an array of length 4, we're telling numpy which rows we want to select.

    As mentioned above, on the first iteration y == label is [ True False True False], so we're saying we want the first and third rows of X. So print(X[y == label]) gives

    [[0 1 0 1]
 [0 0 0 1]]

    And then on the second iteration, per the above, y == label will be selecting the second and fourth rows, giving:

    [[1 0 1 1]
 [1 0 1 0]]

Hope that clarifies!

user_45439770
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