[routine][random] Update the random module + unify behaviors + add randint
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…umerics-and-Algorithms-group#197) Merge pre-0.5 branch to main branch for release of V0.5. See `docs/changelog.md` for differences.
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commit 539af95 Author: ZHU Yuhao 朱宇浩 <[email protected]> Date: Tue Jan 28 22:14:28 2025 +0100 [routine][random] Update the random module + unify behaviors + add `randint` (Mojo-Numerics-and-Algorithms-group#199) The `random` module was created at quite an early stage. A lot of things should be re-considered. This PR aims to refactor the random module by: 1. Aligning the functional behaviors as much as possible with `numpy` while keeping internal consistency of style: The `shape` always comes as the first argument. 2. For all functions, accept `shape` as an `NDArrayShape`. Meanwhile, we still provide overloads for `*shape: Int`. (`shape: List[Int]` is also possible but not recommened). 3. `rand` now generates uniformed distributed values and does not accept integral types. 4. `randint` is added to generate random integral values based on `low` and `high`. add tests for it. 5. `random_exponential` is renamed as `exponential` (same as numpy)[https://numpy.org/doc/stable/reference/random/generated/numpy.random.exponential.html#numpy.random.exponential].
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…andint` (Mojo-Numerics-and-Algorithms-group#199) The `random` module was created at quite an early stage. A lot of things should be re-considered. This PR aims to refactor the random module by: 1. Aligning the functional behaviors as much as possible with `numpy` while keeping internal consistency of style: The `shape` always comes as the first argument. 2. For all functions, accept `shape` as an `NDArrayShape`. Meanwhile, we still provide overloads for `*shape: Int`. (`shape: List[Int]` is also possible but not recommened). 3. `rand` now generates uniformed distributed values and does not accept integral types. 4. `randint` is added to generate random integral values based on `low` and `high`. add tests for it. 5. `random_exponential` is renamed as `exponential` (same as numpy)[https://numpy.org/doc/stable/reference/random/generated/numpy.random.exponential.html#numpy.random.exponential].
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The
randommodule was created at quite an early stage. A lot of things should be re-considered. This PR aims to refactor the random module by:numpywhile keeping internal consistency of style: Theshapealways comes as the first argument.shapeas anNDArrayShape. Meanwhile, we still provide overloads for*shape: Int. (shape: List[Int]is also possible but not recommened).randnow generates uniformed distributed values and does not accept integral types.randintis added to generate random integral values based onlowandhigh. add tests for it.random_exponentialis renamed asexponential(same as numpy)[https://numpy.org/doc/stable/reference/random/generated/numpy.random.exponential.html#numpy.random.exponential].