# Category: Randn seed

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You may receive emails, depending on your notification preferences. Ricky on 30 Nov Vote 0. Accepted Answer: Jan. Assuming I generate some random number inside the loop. Accepted Answer. Jan on 30 Nov Vote 2. Cancel Copy to Clipboard. Seeding the random number generator means initializing it to a certain status. Seeding inside the loop means, that all "random" numbers created inside the loop will be the same in each iteration:.

Result: 0. This is not very useful. Seeding RAND outside the loop allows you to reproduce the results:.However, the reason that we need to use it is a little complicated. To understand why we need to use NumPy random seed, you actually need to know a little bit about pseudo-random numbers. That being the case, this tutorial will first explain the basics of pseudo-random numbers, and will then move on to the syntax of numpy.

As I said earlier, numpy. Understanding why we use it requires some background. NumPy random seed is simply a function that sets the random seed of the NumPy pseudo-random number generator. It provides an essential input that enables NumPy to generate pseudo-random numbers for random processes.

Unless you have a background in computing and probability, what I just wrote is probably a little confusing. A pseudo-random number is a number. Pseudo -random. Pseudo-random numbers are numbers that appear to be random, but are not actually random. According to the encyclopedia at Wolfram Mathworld, a pseudo-random number is:.

A separate article at random. Got that? Pseudo-random numbers are computer generated numbers that appear random, but are actually predetermined. I think that these definitions help quite a bit, and they are a great starting point for understanding why we need them. Setting aside some rare exceptions, computers are deterministic by their very design.

Another way of saying this is that if you give a computer a certain input, it will precisely follow instructions to produce an output.

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This introduces a problem: how can you use a non-random machine to produce random numbers? As such, they are completely deterministic.

However, the numbers that they produce have properties that approximate the properties of random numbers. That is to say, the numbers generated by pseudo-random number generators appear to be random. Even though the numbers they are completely determined by the algorithm, when you examine them, there is typically no discernible pattern. I can assure you though, that these numbers are not random, and are in fact completely determined by the algorithm.

Importantly, because pseudo-random number generators are deterministic, they are also repeatable. What I mean is that if you run the algorithm with the same input, it will produce the same output. So you can use pseudo-random number generators to create and then re-create the exact same set of pseudo-random numbers.

Remember what I wrote earlier: computers and algorithms process inputs into outputs. The outputs of computers depend on the inputs.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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### NumPy random seed explained

Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am a bit confused on what random. For example, why does the below trials do what they do consistently? Pseudo-random number generators work by performing some operation on a value. Generally this value is the previous number generated by the generator. However, the first time you use the generator, there is no previous value.

Seeding a pseudo-random number generator gives it its first "previous" value. Each seed value will correspond to a sequence of generated values for a given random number generator. That is, if you provide the same seed twice, you get the same sequence of numbers twice. Generally, you want to seed your random number generator with some value that will change each execution of the program.

For instance, the current time is a frequently-used seed. The reason why this doesn't happen automatically is so that if you want, you can provide a specific seed to get a known sequence of numbers.

## random.seed( ) in Python

All the other answers don't seem to explain the use of random. Here is a simple example source :. You try this. Let's say 'random. One of the must properties of random numbers is that they should be reproducible. Once you put same seed you get the same pattern of random numbers. So you are generating them right from the start again. You give a different seed it starts with a different initial above 3.

You have given a seed now it will generate random numbers between 1 and 10 one after another. So you can assume one set of numbers for one seed value. If there is no previous value then the current time as previous value automatically.

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We can provide this previous value by own using random. Hence random. Hence, generating a random number is not actually random, because it runs on algorithms. Algorithms always give the same output based on the same input. This means, it depends on the value of the seed. So, in order to make it more random, time is automatically assigned to seed.

Explanation: Every time we are running the above program we are setting seed to 10, then random generator takes this as a reference variable. And then by doing some predefined formula, it generates a random number.Documentation Help Center. For example, randn 3,4 returns a 3-by-4 matrix. For example, randn [3 4] returns a 3-by-4 matrix. The typename input can be either 'single' or 'double'. You can use any of the input arguments in the previous syntaxes.

You can specify either typename or 'like'but not both. The 'seed''state'and 'twister' inputs to the randn function are not recommended. Use the rng function instead. For more information, see Replace Discouraged Syntaxes of rand and randn. Generate values from a bivariate normal distribution with specified mean vector and covariance matrix. Generate a single random complex number with normally distributed real and imaginary parts. Save the current state of the random number generator and create a 1-by-5 vector of random numbers.

Restore the state of the random number generator to sand then create a new 1-by-5 vector of random numbers. The values are the same as before. Always use the rng function rather than the rand or randn functions to specify the settings of the random number generator.

Create a matrix of normally distributed random numbers with the same size as an existing array. Create an array of random numbers that is the same size and data type as p. For the distributed data type, the 'like' syntax clones the underlying data type in addition to the primary data type.

Create an array of random numbers that is the same size, primary data type, and underlying data type as p. If n is 0then X is an empty matrix. If n is negative, then it is treated as 0. Data Types: single double int8 int16 int32 int64 uint8 uint16 uint32 uint If the size of any dimension is 0then X is an empty array.

Beyond the second dimension, randn ignores trailing dimensions with a size of 1. For example, randn 3,1,1,1 produces a 3-by-1 vector of random numbers. Size of each dimension, specified as a row vector of integer values.Sign in to comment. Sign in to answer this question. Unable to complete the action because of changes made to the page. Reload the page to see its updated state.

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What does randn 'seed', mean in the program? Littlem on 12 Feb Vote 0. Answered: Stephen Cobeldick on 12 Feb Accepted Answer: Stephen Cobeldick. As shown in the title. Accepted Answer. Stephen Cobeldick on 12 Feb Vote 1. Cancel Copy to Clipboard. This is explained in the documentation:. More Answers 0. See Also. Tags randn and seed. Opportunities for recent engineering grads.

Apply Today.Operators and Keywords. This returns a column vector v of length Later, you can restore the random number generator to the state v using the form. This new state will be a hash based on the value of vnot v itself. Matsumoto and T.

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Do not use for cryptography without securely hashing several returned values together, otherwise the generator state can be learned after reading consecutive values. Older versions of Octave used a different random number generator. The new generator is used by default as it is significantly faster than the old generator, and produces random numbers with a significantly longer cycle time.

However, in some circumstances it might be desirable to obtain the same random sequences as produced by the old generators. To do this the keyword "seed" is used to specify that the old generators should be used, as in.

The seed of the generator can be queried with. However, it should be noted that querying the seed will not cause rand to use the old generators, only setting the seed will. To cause rand to once again use the new generators, the keyword "state" should be used to reset the state of the rand. The state or seed of the generator can be reset to a new random value using the "reset" keyword.

The class of the value returned can be controlled by a trailing "double" or "single" argument. These are the only valid classes. The arguments are handled the same as the arguments for eye.Documentation Help Center. For example, rng 1 initializes the Mersenne Twister generator using a seed of 1.

The rng function controls the global streamwhich determines how the randrandirandnand randperm functions produce a sequence of random numbers. To create one or more independent streams separate from the global stream, see RandStream and RandStream. For example, rng 0,'philox' initializes the Philox 4x32 random generator with a seed of 0. Set the random number generator to the default seed 0 and algorithm Mersenne Twisterthen save the generator settings.

Now restore the original generator settings and create a random vector.

SPSS Set Seed of Random Number Generator

The result matches the original row vector x created with the default generator. Random number algorithm, specified as one of the options in the table.

When parallel processing, rng 'shuffle' should not be used to set the random number stream on different workers to ensure independent streams since it seeds the random number generator based on the current time.

This is especially true when the command is sent to multiple workers simultaneously, such as inside a parfor job.

For independent streams on the workers, use the default behavior or consider using a unique substream on each worker using RandStream. To use rng instead of the rand or randn functions with the 'seed''state'or 'twister' inputs, see Replace Discouraged Syntaxes of rand and randn. Only the 'twister''v5normal'and 'v4' generators are supported. If extrinsic calls are enabled and rng is not called from inside a parfor loop, only rng can access the data in the structure that rng returns. RandStream RandStream.

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