Random number generator from 1 to 8. Excel random number generator in functions and data analysis
The presented online random number generator operates on the basis of a pseudo-random number generator with a uniform distribution built into JavaScript. Integers are generated. By default, 10 random numbers are output in the range 100...999, the numbers separated by spaces.
Basic settings of the random number generator:
- Amount of numbers
- Number range
- Separator type
- Turn on/off the function of removing repetitions (duplicates of numbers)
The total number is formally limited to 1000, with a maximum of 1 billion. Delimiter options: space, comma, semicolon.
Now you know exactly where and how to get a free sequence of random numbers in a given range on the Internet.
Application options for a random number generator
A random number generator (RNG in JS with uniform distribution) will be useful for SMM specialists and owners of groups and communities on the social networks Instagram, Facebook, VKontakte, Odnoklassniki to determine the winners of lotteries, competitions and prize draws.
A random number generator allows you to draw prizes among an arbitrary number of participants with a specified number of winners. Contests can be held without reposts and comments - you yourself set the number of participants and the interval for generating random numbers. You can get a set of random numbers online and for free on this site, and you do not need to install any application on your smartphone or program on your computer.
Also, an online random number generator can be used to simulate tossing a coin or dice. However, we have separate specialized services for these cases.
Description of the generator
Our free online generator is designed to generate random integers. It can easily be used, for example, to determine the winning number of your lottery, draw or competition.
Number of simultaneously generated numbers: from 1 to 999. By default, one number is generated.
Available number range: from 1 to 99,999,999 inclusive. In this case, the final value of the range must be greater than the initial value. By default, the range from 1 to 100 is used to generate a random number.
The generated numbers can be sorted: randomly (by default), descending, and ascending.
When displaying numbers in the result block, you can use the following delimiters: space (default), comma, and the combination "comma+space".
When generating several numbers, the same ones may appear. By default, duplicates are removed. If you assume that the results will have the same numbers, then simply remove the bird from the field "Repetition of numbers".
The generator also allows you to copy the result to the clipboard. To do this, you need to use the "Copy" button, the result will be automatically placed on the clipboard.
If you need to return the values of all fields to their original state, click the "Reset" button.
A few words about accidents
As surprising as it may be for the average person, random numbers play a very important role in a variety of areas of human activity that require sequences of random numbers that no one can predict. The most familiar examples to us are lotteries or online casinos. After all, if such sequences turn out to be not completely random and someone can trace the order in them, he can easily use this knowledge to his advantage. So in 1873, British engineer Joseph Jagger, along with six of his assistants, went to the casino and began recording the numbers on the roulette wheels. Imagine, but he discovered that on one of the roulettes some numbers appear noticeably more often than others, i.e. are not so random! That's when he started betting money on these numbers. And although the casino owners suspected something was wrong and tried to somehow correct the situation by moving the tables from place to place, this did not help them. Jagger won a total of about $5 million from them at today's rates.
Random numbers are also needed in cryptography, for example, to encrypt network traffic or carry out banking transactions. If the generated sequences of random numbers have easily detectable patterns, attackers will be able to intercept outgoing traffic and harm the user's computer or steal his confidential data.
In addition, random numbers are used in various scientific and engineering fields for computer modeling of real natural processes, in statistics, which itself is based on chance, in various gambling games (not only roulette requires chance), etc.
As you can see, accidents are in demand. But is it easy to obtain truly high-quality random sequences of numbers or symbols? Let us say right away that it is theoretically possible to create such a generator, but it is very difficult to do in practice. That is why numbers obtained using various mathematical algorithms are called pseudorandom. There are many varieties of pseudorandom number generators that use the most complex algorithms, but they are still not perfectly random. However, the more different initial conditions that make it difficult to predict each subsequent number in the sequence that are used in the generator, the more reliable it is.
So where do truly random events happen? At the moment, science believes that the events of quantum mechanics should be considered random. According to the Heisenberg uncertainty principle, we cannot measure all the necessary parameters of a quantum object with any great accuracy. Moreover, in principle we cannot, and not because we lack the technical capabilities. And since it is impossible to measure all the initial parameters, it means that the exact outcome of the process cannot be predicted.
Thus, quantum generators should be considered the highest quality random number generators, i.e. those that use quantum processes in their work.
Numbers surround us from birth and play an important role in life. For many people, their work itself is connected with numbers; some rely on luck, filling out lottery tickets with numbers, while others attach even mystical meaning to them. One way or another, sometimes we cannot do without using a program such as random number generator.
For example, you need to organize a prize draw among your group’s subscribers. Our online random number generator will help you quickly and honestly select winners. You just need, for example, to set the required number of random numbers (based on the number of winners) and the maximum range (based on the number of participants, if numbers are assigned to them). Fraud in this case is completely excluded.
This program can also serve as a random number generator for lotto. For example, you bought a ticket and want to rely entirely on chance and luck in choosing the numbers. Then our number randomizer will help you fill out your lottery ticket.
How to generate a random number: instructions
Random number program It works very simply. You don't even need to download it to your computer - everything is done in the browser window where this page is open. Random numbers are generated in accordance with the specified number of numbers and their range - from 0 to 999999999.
To generate a number online, you need to:
- Select the range in which you want the result. Perhaps you want to cut off numbers up to 10 or, say, 10,000;
- Eliminate repetitions - by selecting this option, you will force the **number randomizer** to offer you only unique combinations within a certain range;
- Select the number of numbers – from 1 to 99999;
- Click the “Generate numbers” button.
No matter how many numbers you want to get as a result, the prime number generator will produce the entire result at once and you can see it on this page by scrolling through the field with numbers using the mouse or touchpad.
Now you can use the ready-made numbers the way you need. From the number field, you can copy the result to publish in a group or send by mail. And so that the result does not raise any doubts, take a screenshot of this page, on which the parameters of the number randomizer and the results of the program will be clearly visible. It is impossible to change the numbers in the field, so the possibility of manipulation is excluded. We hope our website and random number generator helped you.
Have you ever wondered how Math.random() works? What is a random number and how is it obtained? Imagine an interview question - write your random number generator in a couple of lines of code. So, what is it, an accident and is it possible to predict it?
I am very fascinated by various IT puzzles and tasks, and the random number generator is one of these tasks. Usually in my Telegram channel I analyze all sorts of puzzles and various tasks from interviews. The random number generator problem has gained great popularity and I wanted to perpetuate it in the depths of one of the authoritative sources of information - that is, here on Habré.
This material will be useful to all those front-end and Node.js developers who are on the cutting edge of technology and want to get into a blockchain project/startup, where even front-end developers are asked questions about security and cryptography, at least at a basic level.
Pseudo-random number generator and random number generator
In order to get something random, we need a source of entropy, a source of some chaos from which we will use to generate randomness.This source is used to accumulate entropy and then obtain from it an initial value (seed), which is necessary for random number generators (RNG) to generate random numbers.
The Pseudo-Random Number Generator uses a single seed, hence its pseudo-randomness, while the Random Number Generator always generates a random number by starting with a high-quality random variable that is drawn from various sources of entropy.
Entropy is a measure of disorder. Information entropy is a measure of the uncertainty or unpredictability of information.It turns out that in order to create a pseudo-random sequence we need an algorithm that will generate a certain sequence based on a certain formula. But such a sequence can be predicted. However, let's imagine how we could write our own random number generator if we didn't have Math.random()
PRNG has some algorithm that can be reproduced.
RNG is the process of obtaining numbers entirely from some kind of noise, the ability to calculate which tends to zero. At the same time, the RNG has certain algorithms for equalizing the distribution.
We come up with our own PRNG algorithm
Pseudorandom number generator (PRNG) is an algorithm that generates a sequence of numbers whose elements are almost independent of each other and obey a given distribution (usually uniform).We can take a sequence of some numbers and take the modulus of the number from them. The simplest example that comes to mind. We need to think about which sequence to take and the module from what. If you just directly from 0 to N and modulus 2, you get a generator of 1 and 0:
Function* rand() ( const n = 100; const mod = 2; let i = 0; while (true) ( yield i % mod; if (i++ > n) i = 0; ) ) let i = 0; for (let x of rand()) ( if (i++ > 100) break; console.log(x); )
This function generates the sequence 01010101010101... and it cannot even be called pseudo-random. For a generator to be random, it must pass the next bit test. But we don’t have such a task. Nevertheless, even without any tests we can predict the next sequence, which means that such an algorithm is not suitable, but we are in the right direction.
What if we take some well-known but non-linear sequence, for example the number PI. And as the value for the module we will take not 2, but something else. You can even think about the changing value of the module. The sequence of digits in Pi is considered random. The generator can operate using Pi numbers starting from some unknown point. An example of such an algorithm, with a PI-based sequence and a variable module:
Const vector = [...Math.PI.toFixed(48).replace(".","")]; function* rand() ( for (let i=3; i<1000; i++) {
if (i >99) i = 2; for (let n=0; n
We got a generator of numbers from 0 to 9, but the distribution is very uneven and it will generate the same sequence every time.
We can take not the number Pi, but time in numerical representation and consider this number as a sequence of numbers, and in order to ensure that the sequence does not repeat each time, we will read it from the end. In total, our algorithm for our PRNG will look like this:
Function* rand() ( let newNumVector = () => [...(+new Date)+""].reverse(); let vector = newNumVector(); let i=2; while (true) ( if ( i++ > 99) i = 2; let n=-1; while (++n< vector.length) yield (vector[n] % i);
vector = newNumVector();
}
}
// TEST:
let i = 0;
for (let x of rand()) {
if (i++ >100) break; console.log(x)
This already looks like a pseudo-random number generator. And the same Math.random() is a PRNG, we’ll talk about it a little later. Moreover, each time we get a different first number.
Actually, using these simple examples you can understand how more complex random number generators work. And there are even ready-made algorithms. As an example, let’s look at one of them — this is the Linear Congruent PRNG (LCPRNG).
Linear congruent PRNG
Linear congruent PRNG (LCPRNG) is a common method for generating pseudorandom numbers. It is not cryptographically strong. This method consists of calculating the terms of a linear recurrent sequence modulo some natural number m, given by the formula. The resulting sequence depends on the choice of starting number — i.e. seed. With different seed values, different sequences of random numbers are obtained. An example of implementing such an algorithm in JavaScript:Const a = 45; const c = 21; const m = 67; var seed = 2; const rand = () => seed = (a * seed + c) % m; for(let i=0; i<30; i++)
console.log(rand())
Many programming languages use LCPRNG (but not exactly this algorithm(!)).
As mentioned above, such a sequence can be predicted. So why do we need PRNG? If we talk about security, then PRNG is a problem. If we talk about other tasks, then these properties can be a plus. For example, for various special effects and graphics animations, you may need to frequently call random. And this is where the distribution of meanings and performance are important! Secure algorithms cannot boast of speed.
Another property is reproducibility. Some implementations allow you to specify a seed, and this is very useful if the sequence must be repeated. Reproduction is needed in tests, for example. And there are many other things that do not require a secure RNG.
How Math.random() works
The Math.random() method returns a pseudo-random floating point number from the range = crypto.getRandomValues(new Uint8Array(1)); console.log(rvalue)
But, unlike the Math.random() PRNG, this method is very resource-intensive. The fact is that this generator uses system calls in the OS to gain access to entropy sources (mac address, CPU, temperature, etc...).
