random

问题 Can anyone please tell me why the Go example here: https://tour.golang.org/basics/1 always returns the same value for rand.Intn(10)? 回答1: 2 reasons: You have to initalize the global Source used by rand.Intn() and other functions of the rand package using rand.Seed(). For example: rand.Seed(time.Now().UnixNano()) See possible duplicate of Difficulty with Go Rand package. Quoting from package doc of rand: Top-level functions, such as Float64 and Int, use a default shared Source that produces a

问题 The bounty expires in 14 hours . Answers to this question are eligible for a +200 reputation bounty. Peter O. is looking for a canonical answer : To summarize: Either (1) find a more efficient algorithm than the one implemented in the code, or (2) state the formula for the number of valid. combinations and how to "unrank" a combination number. Is there an efficient way to generate a random combination of N integers such that— each integer is in the interval [ min , max ], the integers have a

问题 The bounty expires in 14 hours . Answers to this question are eligible for a +200 reputation bounty. Peter O. is looking for a canonical answer : To summarize: Either (1) find a more efficient algorithm than the one implemented in the code, or (2) state the formula for the number of valid. combinations and how to "unrank" a combination number. Is there an efficient way to generate a random combination of N integers such that— each integer is in the interval [ min , max ], the integers have a

问题 I am running n instances of the same code in parallel and want each instance to use independent random numbers. For this purpose, before I start the parallel computations I create a list of random states, like this: import numpy.random as rand rand_states = [(rand.seed(rand.randint(2**32-1)),rand.get_state())[1] for j in range(n)] I then pass one element of rand_states to each parallel process, in which I basically do rand.set_state(rand_state) data = rand.rand(10,10) To make things

问题 I am running n instances of the same code in parallel and want each instance to use independent random numbers. For this purpose, before I start the parallel computations I create a list of random states, like this: import numpy.random as rand rand_states = [(rand.seed(rand.randint(2**32-1)),rand.get_state())[1] for j in range(n)] I then pass one element of rand_states to each parallel process, in which I basically do rand.set_state(rand_state) data = rand.rand(10,10) To make things

问题 I asked a similar question regarding this same piece of code earlier but once again I have found myself stuck. Particularly on the generation of a license plate containing two letters, two numbers, and then two letters. I hope that this question isn't a duplicate but in this circumstance I am very stuck with what to do, this is the code so far and I hope you can identify where I am going wrong: from datetime import date, datetime, time, timedelta import time, string from random import uniform

问题 So I need to write a program which generates random numbers from 100 to 999, and the tricky part is that the digits in the number can't be the same. For example: 222 , 212 and so on are not allowed. So far, I have this: import random a = int (random.randint(1,9)) <-- first digit b = int (random.randint(0,9)) <-- second digit c = int (random.randint(0,9)) <-- third digit if (a != b and a != b and b != c): print a,b,c As you can see, I generate all three digits separately. I think it's easier

问题 So I need to write a program which generates random numbers from 100 to 999, and the tricky part is that the digits in the number can't be the same. For example: 222 , 212 and so on are not allowed. So far, I have this: import random a = int (random.randint(1,9)) <-- first digit b = int (random.randint(0,9)) <-- second digit c = int (random.randint(0,9)) <-- third digit if (a != b and a != b and b != c): print a,b,c As you can see, I generate all three digits separately. I think it's easier

问题 I have problem with Within and random effect method (it doesn't work). And I have no problem with pooling, between or first diffeences estimator -> it works. I have the same problem like R - Error in class(x) - plm - only within and random effects models. Here is the link to my data: https://www.dropbox.com/s/8tgeyhxeb0wrdri/my_data.xlsx?raw=1 (there are some financial measures and GDP growth for some countries) My code: proba<-read_excel("my_data.xlsx") attach(proba) Y<-cbind(GDP_growth) X<

问题 running gradle build on an android project or module yields the following lint report entry: Potentially insecure random numbers on Android 4.3 and older. Read https://android-developers.blogspot.com/2013/08/some-securerandom-thoughts.html for more info. This provides an easy to implement patch for said issue. But I'm wondering if it needs to be applied, given that it's 5 years old, or can this be ignored? Thanks 回答1: That depends on whether your app must support Android versions 4.3 and