dists = [0, 1, 1 to 3, 2 to 10] # each dist represented as 1M samples
weights = [(1 - p_c), p_c/2, p_c/4, p_c/4 ]
result = mixture(dists, weights)
mean(result)
```
As of now, it may be useful for checking the validity of simple estimations. The title of this repository is a pun on two meanings of "time to": "how much time does it take to do x", and "let's do x".
I was very surprised that Node/Squiggle code was almost as fast as the raw C code. For the Python code, it's possible that the lack of speed is more a function of me not being as familiar with Python. It's also very possible that the code would run faster with [PyPy](https://doc.pypy.org).
I was also really happy with trying [Nim](https://nim-lang.org/). The version which beats all others is just the fastest "danger" compilation of Nim (the "release" compilation is 0m0.183s instead). The Nim version has the particularity that I define the normal function from scratch, using the [Box–Muller transform](https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform#Basic_form). For Nim I also have a version of the code which takes around 4 seconds, where I define some very inefficient sine & logarithm functions to feed into the Box-Muller method, because it felt like fun to really write a botec tool really from scratch.
- [ ] Check whether the Squiggle code is producing 1M samples. Still not too sure.
- Differentiate between initial startup time (e.g., compiling, loading environment) and runtime. This matters because startup time could be ~constant, so for larger projects only the runtime matters.
