random-seed

问题 I want to add a column to my table with a random number using seed. If I use RAND: select *, RAND(5) as random_id from myTable I get an equal value(0.943597390424144 for example) for all the rows, in the random_id column. I want this value to be different for every row - and that for every time I will pass it 0.5 value(for example), it would be the same values again(as seed should work...). How can I do this? ( For example, in PostrgreSql I can write SELECT setseed(0.5); SELECT t.* , random()

问题 I have searched for pseudo-RNG algorithms but all I can find seem to generate the next number by using the previous result as seed. Is there a way to generate them non-recursively? The scenario where I need this is during OpenCL concurrent programming, each thread/pixel needs an independent RNG. I tried to seed them using BIG_NUMBER + work_id , but the result has a strong visual pattern in it. I tried several different RNG algorithms and all have this problem. Apparently they only guarantee

问题 I'm trying to extend the code in this post (accepted answer) to allow me to be able to call randomGen2 to get a random number, based on the function randomGen which takes a seed as an argument. But everytime I call randomGen2, despite returning an Int, I get an error about not been able to print Random (when infact I am only trying to print Int). <interactive>:3:1: No instance for (Show (Random Int)) arising from a use of `print' Possible fix: add an instance declaration for (Show (Random Int

问题 Every time one opens a R console, the random seed is set to the same value. On my computer (it might be the same on your machine), if I run rnorm(1) , I always get 0.1777571 at the first call. I tried to automatically set the random seed using the computer current time by adding something like set.seed( as.integer( as.numeric( gsub("[^0-9]","",paste(format(Sys.time(), "%Y %X %x"))) )%%.Machine$integer.max ) ) in the file .Rprofile but it does not change anything. The first call to rnorm(1)

问题 I have n similar analyses each using m_i pseudo-random number streams ( m_i may vary between analyses). Each analysis has its own random number seed so that the random numbers are uncorrelated between analyses. My problem is that I need to create the m_i streams from the single seed. The analysis is currently written in Numpy, so solutions its Mersenne Twister are ideal, but I am open to solutions in other mature libraries. I considered these possibilities: Use the seed to create a random

问题 I'm trying to get repeatable results when running a session, but want to change the seed freely between sessions. Something like this: a = tf.random_uniform([1]) #Set seed here to e.g. 123 with tf.Session() as sess: print(sess.run(a)) #Output: A1 print(sess.run(a)) #Output: A2 #Set seed here to e.g. 42 with tf.Session() as sess: print(sess.run(a)) #Output: A3 print(sess.run(a)) #Output: A4 #Set seed here to e.g. 123 with tf.Session() as sess: print(sess.run(a)) #Output: A1 print(sess.run(a))

问题 I have a project in which I cannot reproduce random numbers when I use numpy in combination with tensorflow. In the beginning of all my tests, I set tf.set_random_seed(seed) np.random.seed(seed) I have been debugging, and when I use numpy and no TF, all results are reproducible. When I add the TF code, the random numbers stop being reproducible. When I use both TF and numpy, I get the following results: TF variables are initialized to the same value every time (OK) When I use np.random

问题 I have a very basic Lua script that asks a math question math.randomseed(os.time()) print ("Let's play Math") a = math.random(1,10) b = math.random(1,10) io.write("What is " .. a .. " + " .. b .. "?") answer = io.read() correct = (a + b) if (answer == correct) then print ("Correct") else print ("Wrong") print (correct) --For debug end For some reason, I am always getting "incorrect" even when answered correctly. I also print out the correct answer, just to make sure the program is handling

问题 Recently, I was reading the documentation for the caret package when I noticed this: Also, please note that some packages load random numbers when loaded (directly or via namespace) and this may effect [ sic ] reproducibility. What are possible use cases for packages loading random numbers? This seems to be counter to the idea of reproducible research and might interfere with my own attempts to set.seed . (I've started setting seeds closer to code that requires random number generation

问题 Is there a way to store the current state of the built in pseudo-random number generator in Perl so that when my program is run again, it can pick up the sequence from where it left off rather than starting with a new sequence? Right now, I am storing where I am as well as the initial seed and then throwing away the initial segment which I have already seen using something similar to: sub consume_upto_n { my ($seed, $n) = @_; $n = 1 unless defined $n and $n >= 1; srand $seed; rand for 1 .. $n