👀 Views: 30 💬 Answers: 1 📅 Created: 2025-07-11

numpy random sampling probabilities Python

I'm dealing with I just started working with I'm running into an scenario with `np.random.choice` in NumPy version 1.24.3 where it seems to be sampling from the provided probabilities incorrectly. I expected that when I set the probabilities for sampling, the output would reflect those weights accurately. For example, I'm trying to sample from an array of integers `[0, 1, 2, 3]` with specified probabilities `[0.1, 0.1, 0.2, 0.6]`. Here’s the code snippet I'm using: ```python import numpy as np values = [0, 1, 2, 3] probabilities = [0.1, 0.1, 0.2, 0.6] n_samples = 1000 samples = np.random.choice(values, size=n_samples, p=probabilities) # Count occurrences unique, counts = np.unique(samples, return_counts=True) print(dict(zip(unique, counts))) ``` When I run this code, the output distribution is not aligning with the expected probabilities. For instance, I'm consistently getting significantly more `3`s than I expected, but fewer `1`s and `2`s. I was expecting roughly 100 samples of `0`, 100 of `1`, 200 of `2`, and 600 of `3`, but the counts are skewed. I've tried normalizing the probabilities, checking for any issues with the input arrays, and even running multiple trials, but the results seem inconsistent. The randomness of the output is affecting my analysis, and I need the sampling to be representative based on the set weights. Is there something I'm overlooking, or is there a known scenario with `np.random.choice` in this version? I'm working in a Debian environment. Am I approaching this the right way? For reference, this is a production service. What's the correct way to implement this? I'm working with Python in a Docker container on CentOS. Any examples would be super helpful.