random

问题 I'm having trouble to create an array of random choices, where a choice is a tuple. I get the error: a must be 1-dimensional Here is an example: choices = ((0,0,0),(255,255,255)) numpy.random.choice(choices,4) Is there any other way to do this? Expected result: a numpy array consiting of 4 elements randomly picked from the choices tuple. ((0,0,0),(0,0,0),(255,255,255),(255,255,255)) 回答1: Use choice to choose the 1dim indices into the array, then index it. In the example you provided, only the

问题 I just started learning React in my work and I need to build a blog using React. I need to show random posts as "recommended Posts", after doing some research I found a possible solution, using Math.random() but I could not figure it out how to implement it in my component. This is my code: RecommendedPost/index.js import React from 'react'; import { Link } from 'react-router'; class RecommendedPosts extends React.Component { render() { return ( <ul> {this.props.posts.map((post, idx) => {

问题 I just started learning React in my work and I need to build a blog using React. I need to show random posts as "recommended Posts", after doing some research I found a possible solution, using Math.random() but I could not figure it out how to implement it in my component. This is my code: RecommendedPost/index.js import React from 'react'; import { Link } from 'react-router'; class RecommendedPosts extends React.Component { render() { return ( <ul> {this.props.posts.map((post, idx) => {

问题 I'm sampling a Poisson process at a millisecond time scale where the rate is not fixed. I discretise the sampling process by checking in each interval of size delta whether there is an event there or not based on the average rate in that interval. Since I'm using Python it's running a bit slower than I would hope it to be. The code I'm currently using is the following: import numpy def generate_times(rate_function,max_t,delta): times = [] for t in numpy.arange(delta,max_t,delta): avg_rate =

问题 I want to know how many numbers can you store in array? srand (time(NULL)); int array[10000000]; for(int i = 0; i < 10000000; i++){ array[i] = (rand() % 10000000) + 1; } Every time I want to store 10.000.000 numbers in array my program crashed (Eclipse). I even tryed Visual Studio and it crashed to. So i want to know how many numbers can I store in array or is something wrong with my code? 回答1: You can store as many numbers as you have memory for, but you cannot do it like that. The reason

问题 I want to know how many numbers can you store in array? srand (time(NULL)); int array[10000000]; for(int i = 0; i < 10000000; i++){ array[i] = (rand() % 10000000) + 1; } Every time I want to store 10.000.000 numbers in array my program crashed (Eclipse). I even tryed Visual Studio and it crashed to. So i want to know how many numbers can I store in array or is something wrong with my code? 回答1: You can store as many numbers as you have memory for, but you cannot do it like that. The reason

问题 I want to know how many numbers can you store in array? srand (time(NULL)); int array[10000000]; for(int i = 0; i < 10000000; i++){ array[i] = (rand() % 10000000) + 1; } Every time I want to store 10.000.000 numbers in array my program crashed (Eclipse). I even tryed Visual Studio and it crashed to. So i want to know how many numbers can I store in array or is something wrong with my code? 回答1: You can store as many numbers as you have memory for, but you cannot do it like that. The reason

问题 The problem I need to produce the same (pseudo) random number sequence on different machines and compilers. If I use the same kernel, it seems that the implementetion of mersenne twister (MT) in g++ works well: regardless if I compile my program on a newer machine, with g++ 4.9 or 4.7, I get the same random numbers. But I get different ones if I use older kernel or if I change to Visual Studio's compiler. That's ok, because there's no gurantee that mersenne_twister_engine::seed sets the

问题 Like the asker of this question, I was wondering why Math.ceil(Math.random() * 10) was not preferred over Math.floor(Math.random() * 10) + 1 , and found that it was because Math.random has a tiny (but relevant) chance of returning 0 exactly. But how tiny? Further research told me that this random number is accurate to 16 decimal places... well, sort of. And it's the "sort of" that I'm curious about. I understand that floating point numbers work differently to decimals. I struggle with the

问题 Like the asker of this question, I was wondering why Math.ceil(Math.random() * 10) was not preferred over Math.floor(Math.random() * 10) + 1 , and found that it was because Math.random has a tiny (but relevant) chance of returning 0 exactly. But how tiny? Further research told me that this random number is accurate to 16 decimal places... well, sort of. And it's the "sort of" that I'm curious about. I understand that floating point numbers work differently to decimals. I struggle with the