random

问题 I want to use the <random> library in my program and I will have classes with different distributions and I want to generate a number at different times in my program. Currently I have the following in my header file #include <random> #include <time.h> class enemy { private: int max_roll; typedef std::mt19937 MyRng; MyRng rng; public: enemy(int MR){ max_roll = MR; rng.seed(time(NULL)); std::uniform_int_distribution<int> dice(1, max_roll); } int roll() { return dice(rng); } }; I'm getting the

问题 I have a large list of items, each item has a weight. I'd like to select N items randomly without replacement, while the items with more weight are more probable to be selected. I'm looking for the most performing idea. Performance is paramount. Any ideas? 回答1: If you want to sample items without replacement, you have lots of options. Use a weighted-choice-with-replacement algorithm to choose random indices. There are many algorithms like this. One of them is WeightedChoice , described later

问题 I have a large list of items, each item has a weight. I'd like to select N items randomly without replacement, while the items with more weight are more probable to be selected. I'm looking for the most performing idea. Performance is paramount. Any ideas? 回答1: If you want to sample items without replacement, you have lots of options. Use a weighted-choice-with-replacement algorithm to choose random indices. There are many algorithms like this. One of them is WeightedChoice , described later

问题 I have been using numpy's random functionality for a while, by calling methods such as np.random.choice() or np.random.randint() etc. I just now found about the ability to create a default_rng object, or other Generator objects: from numpy.random import default_rng gen = default_rng() random_number = gen.integers(10) So far I would have always used np.random.randint(10) instead, and I am wondering what the difference between both ways is. The only benefit I can think of would be keeping track

问题 I have been using numpy's random functionality for a while, by calling methods such as np.random.choice() or np.random.randint() etc. I just now found about the ability to create a default_rng object, or other Generator objects: from numpy.random import default_rng gen = default_rng() random_number = gen.integers(10) So far I would have always used np.random.randint(10) instead, and I am wondering what the difference between both ways is. The only benefit I can think of would be keeping track

问题 I have been using numpy's random functionality for a while, by calling methods such as np.random.choice() or np.random.randint() etc. I just now found about the ability to create a default_rng object, or other Generator objects: from numpy.random import default_rng gen = default_rng() random_number = gen.integers(10) So far I would have always used np.random.randint(10) instead, and I am wondering what the difference between both ways is. The only benefit I can think of would be keeping track

问题 I've heard of a new balanced BST data structure called a zip tree. What is the zip tree? How does it work? 回答1: At a high level, a zip tree is a randomized balanced binary search tree, that is a way of encoding a skiplist as a BST, and that uses a pair of operations called zipping and unzipping rather than tree rotations. The first bullet point - that zip trees are randomized, balanced BSTs - gives a feel for what a zip tree achieves at a high level. It's a type of balanced binary search tree

问题 I am writing a word-learning application for android. To get random word I use: Random rnd = new Random(); final int rnd.nextInt(WordsNumber); To get random direction (show word or show translation) I use: Random rnd = new Random(); final boolean dir.nextBoolean(); But I see, that a words are not uniformly distributed. I'm testing the application with 17 words. Some words are shown 10 times, some are shown only once. The same problem is with direction. It often happens, for the fifth

问题 Setting a seed ensures reproducibility and is important in simulation modelling. Consider a simple model f() with two variables y1 and y2 of interest. The outputs of these variables are determined by a random process ( rbinom() ) and the parameters x1 and x2 . The outputs of the two variables of interest are independent of each other. Now say we want to compare the change in the output of a variable after a change in the respective parameter has occurred with a scenario before the change was

问题 Setting a seed ensures reproducibility and is important in simulation modelling. Consider a simple model f() with two variables y1 and y2 of interest. The outputs of these variables are determined by a random process ( rbinom() ) and the parameters x1 and x2 . The outputs of the two variables of interest are independent of each other. Now say we want to compare the change in the output of a variable after a change in the respective parameter has occurred with a scenario before the change was