python numpy: evaluate list of probabilities to randomly generated binary values

Question

I have an array of probabilities, and I want to use those probabilities to generate an array of picked values, each value picked with the corresponding probability.

Example:

in:  [ 0.5,  0.5,  0.5,  0.5, 0.01, 0.01, 0.99, 0.99]
out: [   0,    1,    1,    0,    0,    0,    1,    1]

I'd like to use numpy native functions for this, rather than the following loop:

array_of_probs = [0.5, 0.5, 0.5, 0.5, 0.01, 0.01, 0.99, 0.99]
results = np.zeros(len(array_of_probs))
for i, probs in enumerate(array_of_probs):
  results[i] = np.random.choice([1, 0], 1, p=[probs, 1-probs])

A note about terminology: "odds" conventionally means a ratio of probabilities. Odds of A against B = p(A)/p(B). Odds can be any nonnegative number while probabilities are between 0 and 1 inclusive. Obviously if you have odds you can calculate probabilities and vice versa. Looks like you have probabilities and not odds in this case, maybe you want to adjust the title and text. — Robert Dodier
– Robert Dodier, Commented Nov 6, 2017 at 18:03

Erwin Haas · Accepted Answer · 2018-01-26 12:58:08Z

2

You can easily calculate this by comparing the array with a random generated array, as the probability that a random number between 0 and 1 is smaller than 0.3 is 0.3.

E.g.

np.random.rand(len(odds)) < odds

edited Jan 26, 2018 at 12:58

answered Nov 6, 2017 at 18:02

Erwin Haas

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1 Comment

Mark Dickinson Over a year ago

I think you want < rather than > here.

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