替换 Boolean Numpy 数组中 True 项的百分比

Question

I have a numpy array that maybe looks like:

matches = np.array([True, True, False, False, False])

I need to replace the True values with True or False depending on a probability. For example if the probability is 0.5 one or the other will get replaced with False . Actually each element will have the probability applied to it.

So I have numpy where. But I cant quite figure out how to do it:

Where value == True replace with random value.

Answer 1

Assuming you want a uniform probability distribution

import numpy as np
matches = np.array([True, True, False, False, False])
# Here you create an array with the same length as the number of True values in matches
random_values = np.random.uniform(low=0, high=100, size=(sum(matches)))

# Setting the threshold and checking which random values are lower. 
# If they are higher or equal it returns False, if they are lower it returns True
threshold = 75
random_values_outcome = random_values < threshold 

# Substituting the True entries in matches with corresponding entries from
# random_values_outcome
matches[matches == True] = random_values_outcome

Answer 2

This worked for me:

import numpy as np
import random

matches = np.array([True, True, False, False, False])
for position, value in np.ndenumerate(matches):
    if value == True:
        matches[position] = random.choice([True, False])

print(matches)