random

问题 I was implementing a hashmap in C as part of a project I'm working on and using random inserts to test it. I noticed that rand() on Linux seems to repeat numbers far more often than on Mac. RAND_MAX is 2147483647/0x7FFFFFFF on both platforms. I've reduced it to this test program that makes a byte array RAND_MAX+1 -long, generates RAND_MAX random numbers, notes if each is a duplicate, and checks it off the list as seen. #include <stdio.h> #include <stdlib.h> #include <string.h> #include <time

问题 I want to fit a random-intercept complementary log-log regression in R, in order to check for unobserved user heterogeneity. I have searched through the internet and books and have only found one solution in Stata, maybe someone can adapt that to R. In Stata there are 2 commands available: xtcloglog for two-level random intercept gllamm for random-coefficient and and higher-levels models My data relates if activities from people are completed or not and affected by sunshine - completion is

问题 I want to fit a random-intercept complementary log-log regression in R, in order to check for unobserved user heterogeneity. I have searched through the internet and books and have only found one solution in Stata, maybe someone can adapt that to R. In Stata there are 2 commands available: xtcloglog for two-level random intercept gllamm for random-coefficient and and higher-levels models My data relates if activities from people are completed or not and affected by sunshine - completion is

问题 I have a given price range and i had used random uniform to get random generated random results from it. How can i introduce np.random.zipf to do the same ? i have tried the following : a = np.random.zipf((randint(1, 6000000)), size=None) print(a) But it seems to be providing no return values, and it keeps running the code without any termination order_total_price_range1 = round(random.uniform(850, 560000), 5) order_total_price_range2 = round(random.uniform(850, 560000), 5) I expected to get

问题 I have been looking at similar questions regarding how to generate random numbers in python. Example: Similar Question - but i do not have the problem that the randomfunction returns same values every time. My random generator works fine, the problem is that it returns the same value when calling the function at, what I think, the same second which is undesireable. My code looks like this def getRandomID(): token = '' letters = "abcdefghiklmnopqrstuvwwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890"

问题 I have 4 parts, every part 10000 times, which should fit into case, and the dimensions of the parts are given by uniform, normal and triangular distribution by randomly generating numbers in added dimensions of each distribution. For each 4 parts there is decision if they fit or not. But that shouldn't be a problem. I've managed somehow to do uniform and normal distribution: public double uniformDistrubution(double min, double max) { Random rand = new Random(); return Math.random() * max +

问题 I have 4 parts, every part 10000 times, which should fit into case, and the dimensions of the parts are given by uniform, normal and triangular distribution by randomly generating numbers in added dimensions of each distribution. For each 4 parts there is decision if they fit or not. But that shouldn't be a problem. I've managed somehow to do uniform and normal distribution: public double uniformDistrubution(double min, double max) { Random rand = new Random(); return Math.random() * max +

问题 This question already has an answer here : What does “possible lossy conversion” mean and how do I fix it? (1 answer) Closed 2 years ago . I have this program that is pretty much a calculator but with a moving JLabel that is supposed to change colors every time you click the label, but i have 3 errors at the very bottom of the code that i have marked with a comment. all three are: error: incompatible types: possible lossy conversion from long to int public class TestCalculator { private

问题 I have a list and I want to shuffle a portion of it in-place . I am aware of random.shuffle() and that it works in-place, but if I slice the list, it shuffles the sliced copy of the original input, leaving the original list untouched: import random l = list(range(20)) print(l) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] random.shuffle(l[:10]) # I wish it was shuffling the first half print(l) # but does nothing to `l` # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13

问题 In order to draw random number from a Poisson distribution in C++, it is generally advised to use RNG_type rng; std::poisson_distribution<size_t> d(1e-6); auto r = d(rng); At each call of the std::poisson_distribution object, an entire sequence of random bits is consumed (e.g. 32 bits with std::mt19937, 64 bits for std::mt19937_64). It strikes me that with such low mean ( mean = 1e-6 ), the vast majority of times, only a few bits are enough to determine that the value to return is 0. The