I want to use the skewnorm function to generate random numbers. I want the numbers to come close to zero, but never less than or equal to it. Is there a way to specify a minium value?
By definition, skew normal distribution allows any number to be generated just as the normal distribution does (its density is positive on the entire real line). So if your numbers are never less than or equal to 0, they don't come from skew normal distribution.
To ensure you never get a non-positive number you can e.g. throw out any such number you get and sample again in a loop; you just won't get a skew normal distribution this way.
Related
I need to calculate a pseudo random number in a given range (e.g. 0-150) based on another, strictly increasing number. Is there a mathematical way to solve this?
I am given one number x, which increases by 1 every day. Based on this number, I need to - somehow - calculate a number in a given range, which seems to be random.
I have a feeling that there is an easy mathematical solution for this, but sadly I am not able to find it. So any help would be appreciated. Thanks!
One sound way to do that is to hash the number x (either its binary representation or in text form) and then to use the hash to produce the 'random' number in the desired range (say by taking the first 32 bits of the hash and extracting by any known method the desired value). A cryptographic hash can be used like Sha256, but this is not necessary, MurmurHash is possibly a good one for your application.
Normally when you generate a random number, a seed value is used so that the same sequence of psuedorandom numbers isn't repeated. When a seed isn't explicitly given, many systems will use the time as a seed value.
Perhaps you could use x as a seed.
Here's an article explaining seeding: https://www.statisticshowto.com/random-seed-definition/
While reading about Transfer Learning with MATLab I came across a piece of code which says...
rng(2016) % For reproducibility
convnet = trainNetwork(trainDigitData,layers,options);
...before training the network so that the results can be reproduced exactly as given in the example by anyone who tries that code. I would like to know how generating a pseudo-random number using rng(seed_value) function can help with reproduciblity of the entire range of results?
Not random number generation, the random number generator seed.
There is no such things as random numbers, just pseudo-random numbers, numbers that behave almost as random, generally arising from some complex mathematical function, function that usually requires an initial value. Often, computers get this initial value from the time register in the microchip in your PC, thus "ensuring" randomness.
However, if you have an algorithm that is based in random numbers (e.g. a NN), reproducibility may be a problem when you want to share your results. Someone that re-runs your code will be ensured to get different results, as randomness is part of the algorithm. But, you can tell the random number generator to instead of starting from a seed taken randomly, to start from a fixed seed. That will ensure that while the numbers generated are random between themseves, they are the same each time (e.g. [3 84 12 21 43 6] could be the random output, but ti will always be the same).
By setting a seed for your NN, you ensure that for the same data, it will output the same result, thus you can make your code "reproducible", i.e. someone else can run your code and get EXACTLY the same results.
As a test I suggest you try the following:
rand(1,10)
rand(1,10)
and then try
rng(42)
rand(1,10)
rng(42)
rand(1,10)
Wikipedia for Pseudo-random number generator
Because some times is good to use the same random numbers, this is what matlab says about that
Set the seed and generator type together when you want to:
Ensure that the behavior of code you write today returns the same results when you run that code in a future MATLAB® release.
Ensure that the behavior of code you wrote in a previous MATLAB release returns the same results using the current release.
Repeat random numbers in your code after running someone else's random number code
this is te point of repating the seed, and generate the same random numbers. matlab points it out in two good articles one for repeating numbers and one for different numbers
You dont want to start with weights all equal zeros, so in the initializing stage you give the weights some random value. There maybe other random values involved in searching for minimum later in the learning process, or in the way you feed your data.
So the real input to all neural network learning process is your data and the random number generator.
If they are the same, than all going to be the same.
And 'rng' command put the random number generator in predefined state so it will generate same sequence of number.
anquegi's answer, pretty much answers your question, so this post is just to elaborate a bit more.
Whenever you ask for a random number, what MATLAB really does, is that it generates a pseudo random number, which has distribution U(0,1) (that is the uniform on [0,1]) This is done via some deterministic formula, typically something like, see Linear congruential generator:
X_{n+1} = (a X_{n} + b) mod M
then a uniform number is obtained by U = X_{n+1}/M.
There is, however, a problem, If you want X_{1}, then you need X_{0}. You need to initialise the generator, this is the seed. This also means that once X_{0} is specified you will draw the same random numbers, every time. Try open a new MATLAB instance, run randn, close MATLAB, open it again and run randn again. It will be the same number. That is because MATLAB always uses the same seed whenever it is opened.
So what you do with rng(2016) is that you "reset" the generator, and put X_{0} = 2016, such that you now know all numbers that you ask for, and thus reproduce the results.
I am trying to simulate an arrival process of vehicles to an intersection in Matlab. The vehicles are randomly generated with Poisson distribution.
Let´s say that in one diraction there is the intensity of the traffic flow 600 vehicles per hour. From what I understood from theory, the lambda of the Poisson distribution should be 600/3600 (3600 sec in 1 hour).
Then I run this cycle:
for i = 1:3600
vehicle(i) = poissrnd(600/3600);
end
There is one problem: when I count the "ones" in the array vehicle there are never 600 ones, it is always some number around, like 567, 595 and so on.
The question is, am I doing it wrong, i.e. should lambda be different? Or is it normal, that the numbers will never be equal?
If you generate a random number, you can have an expectation of the output.
If you actually knew the output it would not be random anymore.
As such you are not doing anything wrong.
You could make your code a bit more elegant though.
Consider this vectorized approach:
vehicle = poissrnd(600/3600,3600,1)
If you always want the numbers to be the same (for example to reproduce results) try setting the state of your random generator.
If you have a modern version (without old code) you could do it like so:
rng(983722)
I've used 2 Uniform Random Number blocks in my simulation model, but every time I run the program they generate last numbers (exactly the same). I need to test the model with new generated numbers. what should I do?
thanks for your helps in advance
The fact that random number generators generate the same random numbers "from the start" is a feature, not a bug. It allows for reproducible testing. You need to initialize your random number generator with a "random seed" in order to give a different result each time - you could use the current time, for example. When you do, it is recommended that you store the seed used - it means you can go back and run exactly the same code again.
For initializing a random seed, you can use the methods given in this earlier answer
In that answer, they are setting the seed to 0 - this is the opposite of what you are trying to do. You will want to generate a non-random number (like the date), and use that. A very useful article can be found here. To quote:
If you look at the output from rand, randi, or randn in a new MATLAB
session, you'll notice that they return the same sequences of numbers
each time you restart MATLAB. It's often useful to be able to reset
the random number generator to that startup state, without actually
restarting MATLAB. For example, you might want to repeat a calculation
that involves random numbers, and get the same result.
They recommend the command
rng shuffle
To generate a new random seed. You can access the seed that was used with
rng.seed
and store that for future use. So if you co
rng shuffle
seedStore = rng.seed;
Then next time you want to reproduce results, you set
rng(seedStore);
I am generating a random population of strings made of 0s and 1s. I am using randi(2)-1 to get a randomly generated single value 0 or 1. I expect to get 1s almost as frequently as 0s. Instead, when I view all the individuals in the population, they mostly consist of 1s. Below is the code - what is wrong?
for iInd=1:individualsCount
individual(attrCount) = 0;
for i=1:attrCount
individual(i) = randi(2)-1;
end
population{iInd} = individual;
end
Firstly, you don't need a loop to generate a random string of 0's and 1's. Try this instead:
individual = randi([0 1],[attrCount,1]);
Secondly, again, you don't need a loop to construct your population cell. Try this instead:
population=arrayfun(#(x)randi([0 1],[attrCount,1]),1:individualsCount,'UniformOutput',false)
You might have to change the order of rows and columns depending on how you want to set it up.
Now, coming to your question, you ought to understand that these distributions are stochastic and approach a truly uniform distribution of 50% 1s and 50% 0s only as your sample size approaches infinity. If your attrCount is small enough, do not be surprised if you don't find numbers close to 50% for each. That doesn't mean it is wrong. It is what it is.
Here's how the distribution of 1s looks like for a random binary vector of different sample sizes. You can see that for small sample sizes, there is high variability (and by no means is this exact... it will be different each time), whereas as you start approaching large sample sizes of 1000 and above, your distribution of 1s gets closer and closer to 50%, eventually being exactly 50% at infinity.