3 ms·

I perused the codebase but I'm unfamiliar with dart: https://github.com/filiph/unsure/blob/master/lib/src/calculation.dart https://github.com/filiph/unsure/blo

I perused the codebase but I'm unfamiliar with dart: https://github.com/filiph/unsure/blob/master/lib/src/calculation.dart https://github.com/filiph/unsure/blob/master/lib/src/calcula... I assume this is a montecarlo approach? (Not to start a flamewar, at least for us data scientists :) ).

kccqzy 2y ago
Yes it is.
- porridgeraisin 2y ago
  Can you explain how? I'm an (aspiring)
  - kccqzy 2y ago
    I didn't peruse the source code. I just read the linked article in its entirety and it says > The computation is quite slow. In order to stay as flexible as possible, I'm using the Monte Carlo method. Which means the calculator is running about 250K AST-based computations for every calculation you put forth. So therefore I conclude Monte Carlo is being used.
  - constantcrying 2y ago
    Line 19 to 21 should be the Monte-Carlo sampling algorithm. The implementation is maybe a bit unintuitive but apparently he creates a function from the expression in the calculator, calling that function gives a random value from that function.
  - hawthorns 2y ago
    It's dead simple. Here is the simplified version that returns the quantiles for '100 / 2 ~ 4'. import numpy as np def monte_carlo(formula, iterations=100000): res = [formula() for _ in range(iterations)] return np.percentile(res, [0, 2.5, \*range(10, 100, 10), 97.5, 100]) def uncertain_division(): return 100 / np.random.uniform(2, 4) monte_carlo(uncertain_division, iterations=100000)