random-seed

问题 Using an arima.sim() function to simulate time series data that follows a particular ARIMA model requires a lot of trials of this nature: library(forecast) set.seed(1) ar1 <- arima.sim(n = 10, model=list(ar=0.2, order = c(1, 0, 0)), sd = 1) ar2 <- auto.arima(ar1, ic ="aicc") ar2 One needs to be changing the seed integer until the desired result is archived. I now think of instead of changing the seed integer manually and checking with auto.arima() function I should automate the seeds with a

问题 I'm trying to set seeds and configure keras settings to ensure my experiments are reproducible. When I run the following (based on code in an answer to this question): # Import libraries import numpy as np import pandas as pd import tensorflow as tf from tensorflow.keras.models import load_model from tensorflow.keras.regularizers import l2 # for setting seeds and configuring keras so that experiments are reproducible from numpy.random import seed import random as rn import os from tensorflow

问题 I'm trying to set seeds and configure keras settings to ensure my experiments are reproducible. When I run the following (based on code in an answer to this question): # Import libraries import numpy as np import pandas as pd import tensorflow as tf from tensorflow.keras.models import load_model from tensorflow.keras.regularizers import l2 # for setting seeds and configuring keras so that experiments are reproducible from numpy.random import seed import random as rn import os from tensorflow

问题 Here's a simple fortran program I was using to understand the behavior of the fortran intrinsic uniform random number generator. program test_prog implicit none integer, allocatable :: seed(:) real(8), dimension(2) :: unif_rand integer :: nseed ! minimum number of random seed value integer :: i,n call random_seed( size=nseed ) nseed = 100 allocate( seed(nseed) ) write(*,*) "nseed: ",nseed do n = 1,5 seed(:) = n**10 call random_seed( put=seed ) call random_number(harvest=unif_rand) write(*

问题 Like in R, I would like to set a random seed globally for the entire script/session, instead of having to call the random seed function every time I execute a function or run a model. I am aware that sci-kit learn uses the numpy RNG, but also could not find a way to set it globally. I have read several posts here on this topic, such as this one: Differences between numpy.random and random.random in Python It explains the difference between the two RNG classes, but not how to set it globally.

问题 Using MATLAB and trying to use a computer cluster to perform 100 repetitions of certain calculation with inherent stochastic nature. Each of those repetitions should include the same code, but with different random seed. It seems that rng('shuffle') recommended by documentation may not achieve this if all jobs start running at the same time (on different machines) as the seed used is an integer which seems to be initialized from time (it is monotonously increasing, seems like precision of

问题 When I running this code while ... srand (1); printf ("Again the first number: %d\n", rand()%100); on 2 PCs I get different results. but in each PC I get always the same random list. For ex. in PC 1 I always get 83,86,77,15,93,35 and in PC2 I always get F, G ,H , I ,J . I want to know what is the algorithim? If I know the first random , is there any way to know the second random in same PC? . Second random is depending on seed or only on the last random? I not to put srand(time(0)); to get a

问题 I want to use the sample function after setting set.seed with a fixed number. Unfortunately I obtain different results from a edx website. I'm wondering why I get different results. In the website they obtain 0.2706222. On my computer I obtain 0.3293778 I realized that the problem came from my sample function which do not returns me the same sample. Setting set.seed(1) and using sample(seq(1:5000), 3). My computer returns 1017 4775 2177 whereas it "should be" 1328 1861 2864 library(downloader

问题 I am doing a scheduling simulation in python which is full determinstic. So, when I have the same input and parameters I always get the same output. Now I want to randomize the initial starting state of the simulation and compare the output of two (or more) different simulation parameters. To compare the "same randomized initial starting state" I want to set the random.seed() with an initial value, which should stay the same for all comparisions of different schedulers. Furthermore I want to

问题 This solution Automate Seed as a Vector Instead of an Integer in R library(forecast) SEED_vector <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19) arima_order_results = data.frame() for (my_seed in SEED_vector){ set.seed(my_seed) ar1 <- arima.sim(n = 10, model=list(ar=0.2, order = c(1, 0, 0)), sd = 1) ar2 <- auto.arima(ar1, ic ="aicc") arima_order = arimaorder(ar2) arima_order = t(as.data.frame(arima_order)) # Print the arima order. print(arima_order) # This line of code is just if you