虹膜随机均匀分布

Question

This is the code I have to randomly select 30 data points uniformly. The part that confuses me is why we are checking if random.random() <= p ? Can anyone explain?

from sklearn import datasets
import random
iris = datasets.load_iris()
d = iris.data

# sample 30 points uniform randomly from 150 points dataset
n = 150
m = 30
p = m/n

lst = []
for i in range(0, n):
    if random.random() <= p:
        lst.append(d[i,:])

Answer 1

So p represents the probability of an element being selected.

As there are 150 total elements, and 30 elements need selecting, the probability of selecting one element is 30/150 . This is set to p .

Each element is then iterated over and if the result of random.random() (between 0 and 1 ) is greater than p , than that element is selected (I assume this; I do not fully know your dataset).

On average, this should give about 30 elements.