So for example, you might use numpy.random.seed along with numpy.random.randint. This just helps them check their work! Ok, here is the exact same code that we just ran (with the same seed). In most cases, NumPy’s tools enable you to do one of two things: create numerical data (structured as a NumPy array), or perform some calculation on a NumPy array. As such, they are completely deterministic. Monte Carlo methods are a class of computational methods that rely on repeatedly drawing random samples. Specifically, if you need to generate a reproducible random sample from an input array, you’ll need to use numpy.random.seed. ¶. The numpy.random.randn() function creates an array of specified shape and fills it with random values as per standard normal distribution.. This will make your outputs different every time you run it. See also. I want to re-run the code just so you can see, once again, that the primary reason we use NumPy random seed is to create results that are completely repeatable. When you sign up, you'll receive FREE weekly tutorials on how to do data science in R and Python. To set a seed value in NumPy np.random.seed(42) print(np.random.rand(4)) OUTPUT:[0.37454012, 0.95071431, 0.73199394, 0.59865848] Whenever you use a seed … 5 comments Labels. The seed value. It provides an essential input that enables NumPy to generate pseudo-random numbers for random processes. So essentially, a pseudo-random number is a number that’s almost random, but not really random. This introduces a problem: how can you use a non-random machine to produce random numbers? Yeah … if you like it, share it on social media, Man, thanks a lot! In machine learning and data science, at times we have to work with randomly generated data. Thanks again! It comprises of multidimensional objects in arrays and a package of integrating tools for Python implementation. … plenty of low-information blog posts out there for low-skill data science wannabes. This is a guide to NumPy ndarray tolist. If you just want to copy-paste some code and not understand anything, then go read something else. As far as I can tell, random.random.seed() is thread-safe (or at least, I haven’t found any evidence to the contrary). Set the `tensorflow` pseudo-random generator at a fixed value import tensorflow as tf tf. 2. Specifically, numpy.random.seed works with other function from the numpy.random namespace. That being the case, let me give you a quick introduction to them …. Really. By default the random number generator uses the current system time. If we really wish to know about NumPy, there a lot of functions we need to have knowledge about. For the first time when there is no previous value, it uses current system … Numpy 1.14.0 is the result of seven months of work and contains a large number of bug fixes and new features, along with several changes with potential compatibility issues. This is where the importance of np.random.seed( ) in ML/DL lies. It can be called again to re-seed the generator. Seed for RandomState. They are designed in such a way that some meaningful or logical results will be given to us when a process is run in a computer. For more information about how to create random samples, you should read our tutorial about np.random.choice. In general, if you are worried about seed state, I recommend creating your own random objects and pass them around for generating random numbers. Must be convertible to 32 bit unsigned integers. It also requires you to know a little bit about programming concepts like “global variables.” If you’re a relative data science beginner, the details that you need to know might be over your head. To do this, we’re going to use the NumPy random randint function (AKA, np.random.randint). We can think of the np.random.random function as a tool for generating probabilities. What the “Numpy random seed” function does; How to Numpy axes work; How to reshape, split, and combine your Numpy arrays; Applying mathematical operations on Numpy arrays; and more … Additionally, when you join the course, you’ll discover our unique practice system that will enable you to memorize all of the syntax that you learn. Previous topic. numpy.random.randint, This is documentation for an old release of NumPy (version 1.15.1). Well. So, What is NumPy? For details, see RandomState. Here’s where you might see the np.random.seed function. So for example, you might use numpy.random.seed along with numpy.random.randint. That being the case, it’s much better if you actually read the tutorial. To do this, we’re going to use the NumPy random random function (AKA, np.random.random). Next, let’s run the code with a different seed. In this tutorial, I’ll explain how to use the NumPy random seed function, which is also called np.random.seed or numpy.random.seed. You just need to call torch.manual_seed(seed), and it will set the seed of the random number generator to a fixed value, so that when you call for example torch.rand(2), the results will be reproducible. pseudo-random number generators are completely deterministic. According to the encyclopedia at Wolfram Mathworld, a pseudo-random number is: The definition goes on to explain that …. For the first time when there is no previous value, it uses current system … This is because np.random.choice is using random sampling with replacement. For numpy.random.seed(), the main difficulty is that it is not thread-safe - that is, it's not safe to use if you have many different threads of execution, because it's not guaranteed to work if two different threads are executing the function at the same time. This will enable you to create random integers with NumPy. RandomState. A matrix has rows and columns. Previous topic. If there’s any reason to suspect that you may need threads in the future, it’s much safer in the long run to do as suggested, and to make a local instance of the numpy.random.Random class. Definitely, the author goal is teaching not to show knowledge to others. The prefix “pseudo” is used to differentiate it from a “truly” random number. Let’s see it work on my machine which has a GPU and CuPy installed: Input: I hope other tutorials of this site would be clear and nice as this one. Here, we’ll create a list of 5 pseudo-random integers between 0 and 9 using numpy.random.randint. (And notice that we’re using np.random.seed here). You can use numpy.random.seed(0), or numpy.random.seed(42), or any other number. numpy.random.randint¶ numpy.random.randint (low, high=None, size=None, dtype='l') ¶ Return random integers from low (inclusive) to high (exclusive).. Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high).If high is None (the default), then results are from [0, low). … And if you later give a computer the same input, it will produce the same output. set_random_seed (seed_value) # 5. Recommended Articles. There are many more. They are pseudo-random … they approximate random numbers, but are 100% determined by the input and the pseudo-random number algorithm. The pseudorandom number works by starting with an integer called a seed and then generates numbers in succession. Computer scientists have created a set of algorithms for creating psuedo random numbers, called “pseudo-random number generators.”. For that reason, we can set a random seed with the random.seed() function which is similar to the random random_state of scikit-learn package. The NumPy random normal function enables … As you can see, we’ve basically generated a random sample from the list of input elements … the numbers 1 to 6. Here at Sharp Sight, we teach data science. For details, see RandomState. Much more complicated code base. The seed () method is used to initialize the random number generator. In this blog, I will discuss about what NumPy.random.seed( which is also called np.random.seed or numpy.random.seed) does. However, if you’re building software systems that need to be secure, NumPy random seed is probably not the right tool. -zss. If we run the code once again, we will get the same result. I'm using functions from numpy.random on a Jupyter Lab notebook and I'm trying to set the seed using numpy.random.seed(333).This works as expected only when the seed setting is in the same notebook cell as the code. set_state and get_state are not needed to work with any of the random distributions in NumPy. The np.random.seed function provides an input for the pseudo-random number generator in Python. This method is called when RandomState is initialized. As such, they are completely deterministic. I have adapted an example neural net written in Python to illustrate how the back-propagation algorithm works on a small toy example. This method is called when RandomState is initialized. Pseudo-random numbers comes to our rescue. This is one of them. Ultimately, creating pseudo-random numbers this way leads to repeatable output, which is good for testing and code sharing. Learn how to use the seed method from the python random module. Having said all of that, to really understand numpy.random.seed, you need to have some understanding of pseudo-random number generators. Results are from the “continuous uniform” distribution over the stated interval. Ok … now that you understand what NumPy random seed is (and why we use it), let’s take a look at the actual syntax. … and we regularly post FREE data science tutorials just like this one. I’ve really only touched on a few applications of numpy.random.seed in Python. Excellent. Now that I’ve shown you how to use np.random.random, let’s just run it again with the same seed. The random number generator needs a number to start with (a seed value), to be able to generate a random number. My modifications include printing, a learning rate and using the leaky ReLU activation function instead of sigmoid. Let’s see one basic structure to create 2d array using numPy function see below; Example: import numPy as myNum myArr = myNum.arange(6) 1.1.0. p/s: greate content, easy to understand, This is so fanatastic and well explained ! ... import numpy as np # seed random numbers to make calculation # deterministic (just a good practice) np. The algorithm produced an array with the values [5, 0, 3, 3, 7]. NumPy will generate a seed on its own, but that seed might change moment to moment. In order to work properly, pseudo-random number generators require a starting input. However, I strongly recommend that you read the whole tutorial. This method is called when RandomState is initialized. If you use a function from the numpy.random namespace (like np.random.randint, np.random.normal, etc) without using NumPy random see first, Python will actually still use numpy.random.seed in the background. That said, I would think it works the same way. numpy.random.seed() should be fine for testing purposes. – KubiK888 Oct 25 '18 at 15:04 numpy.random.seed¶ numpy.random.seed(seed=None) ¶ Seed the generator. Excellent post. This will enable you to create random integers with NumPy. Then, how can we generate random results using a computer? Almost by definition, Random sampling. The concatenate function present in Python allows the user to merge two different arrays either by their column or by the rows. If you run the same code again, you’ll get the exact same numbers. We call these data cleaning and reshaping tasks “data manipulation.” In recent years, NumPy has become particularly important for “machine learning” and “deep learning,” since these often involve large datasets of numeric data. You just need to call torch.manual_seed(seed), and it will set the seed of the random number generator to a fixed value, so that when you call for … … so when people do deep learning in Python, you’ll frequently see at least a few uses of numpy.random.seed. The code for np.random.randint is the same. numpy.random.seed. So, let’s get started. While working with Machine Learning or Deep Learning, we all must have come across the buzz word “NumPy”. Take for example the tutorials that I post here at Sharp Sight. That being said, Dive in! Essentially, we’re going to use NumPy to generate 5 random integers between 0 and 99. The numpy.random.seed function provides the input (i.e., the seed) to the algorithm that generates pseudo-random numbers in NumPy. I plan to discuss in details about the other functions too. So just like any output produced by a computer, pseudo-random numbers are dependent on the input. NumPy will generate a seed value from a part of your computer system (like /urandom on a Unix or Linux machine). In this article, different aspects such as syntax, working, and examples of the vstack function is explained in detail. A proper definition would be that pseudo-random numbers are computer generated numbers that look like they are random,but are actually pre-determined. To understand what goes on inside the complex expression involving the ‘np.where’ function, it is important to understand the first parameter of ‘np.where’, that is the condition. There are times when you really want your “random” processes to be repeatable. numpy.random. More specifically, you’ll also probably use pseudo-random numbers if you want to do deep learning. Specifically, Numpy is a package that enables you to create and work with arrays of numeric data. For details, see RandomState. This confused me for a while. Essentially though, Monte Carlo methods are a powerful computational tool used in science and engineering. This is a common convention, but it requires you to import NumPy with the code “import numpy as np.” I’ll explain more about this soon in the examples section. NumPy can be installed with conda, with pip, with a package manager on macOS and Linux, or from source. What does numpy exactly do with the seed we give it to produce the results it does? The authors of numpy would really have to try to make it work in a different way than how it works in the python implementation. numpy… Remember what I wrote earlier: computers and algorithms process inputs into outputs. A nested list will be returned if the array that is considering is multi-dimensional whereas a list that contains array elements will be returned if the array is one-dimensional. Long-winded but got the job done. One such way is to use the NumPy library. the article helpt me enormously, Read to the “WTF … “, my mind “Hm…. Specifically, Numpy works with data organized into a structure called a Numpy array. Basically, numpy is an open source project. numpy.random.seed¶ numpy.random.seed (seed=None) ¶ Seed the generator. Reshaping Arrays. I post detailed tutorials about how to perform various data science tasks, and I show how code works, step by step. How does the NumPy.argmax work? numpy.random.seed(seed=None) ¶. Great … it’s a powerful toolset, and it will be extremely important in the 21st century. If you want to learn NumPy and data science in Python, then sign up for our email list. Here’s an example of a 2-dimensional Numpy array. Now that we’ve imported NumPy properly, let’s start with a simple example. Another way of saying this is that if you give a computer a certain input, it will precisely follow instructions to produce an output. We use np.random.seed when we need to generate random numbers or mimic random processes in NumPy. Understanding why we use it requires some background. Copy link Quote reply numpy-gitbot … Everything else is the same. Once again, we used the same seed, and this produced the same output. numpy.random.seed¶ numpy.random.seed (seed=None) ¶ Seed the generator. What I mean is that if you run the algorithm with the same input, it will produce the same output. More specifically, if you’re doing random sampling with NumPy, you’ll need to use numpy.random.seed. … and notice that we’re using np.random.seed in exactly the same way …. It allows you to provide a “seed” value to NumPy’s random number generator. It can be called again to re-seed the generator. Pseudo-random numbers are numbers that appear to be random, but are not actually random. With NumPy, we work with multidimensional arrays. Notice that the number is exactly the same as the first time we ran the code. Numpy.concatenate() function is used in the Python coding language to join two different arrays or more than two arrays into a single array. numpy_name.exp(your 2d array here ..) Now here we have to create one 2d array to work with it. Seed function is used to save the state of a random function, so that it can generate same random numbers on multiple executions of the code on the same machine or on different machines (for a specific seed value). Parameters: seed: int or 1-d array_like, optional. To run the function properly, first we need to abbreviate the name of “NumPy”. For example, here we’ll create some pseudo-random numbers with the NumPy randint function: I can assure you though, that these numbers are not random, and are in fact completely determined by the algorithm. If you want to master data science fast, sign up for our email list. For numpy.random.seed(), the main difficulty is that it is not thread-safe - that is, it's not safe to use if you have many different threads of execution, because it's not guaranteed to work if two different threads are executing the function at the same time. In fact, there are several dozen NumPy random functions that enable you to generate random numbers, random samples, and samples from specific probability distributions. 00 - Bug component: numpy.random. We ran the exact same code, and it produced the exact same output. Pseudo-random. numpy… February 24, 2018 kostas. As far as I can tell, random.random.seed() is thread-safe (or at least, I haven’t found any evidence to the contrary). The function is capable of taking two or more arrays that have the shape and it merges these arrays into a single array. The splits each time is the same. Specifically, numpy.random.seed works with other function from the numpy.random namespace. An array plays a major role in data science where speed matters. At the risk of being a bit of a smart-ass, I think the name “pseudo-random number” is fairly self explanatory, and it gives us some insight into what pseudo-random numbers actually are. The numpy.random.seed function works in conjunction with other functions from NumPy. The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. I will be cataloging all the work I do with regards to PyLibraries and will share it here or on my Github. even though I passed different seed generated by np.random.default_rng, it still does not work `rg = np.random.default_rng() seed = rg.integers(1000) skf = StratifiedKFold(n_splits=5, random_state=seed) skf_accuracy = [] skf_f1 Here, we also used Numpy random seed to make our code reproducible. The important thing is that NumPy random seed is probably sufficient if you’re just using NumPy for some data science or scientific computing. Thank you very much for this tutorial, I have been trying to understand this from many sources, this is the best explanation I read and understood properly. We can use numpy.random.seed(101), or numpy.random.seed(4), or any other number. Enter your email and get the Crash Course NOW: © Sharp Sight, Inc., 2019. Syntax: Now you can learn about NumPy random seed. Set `numpy` pseudo-random generator at a fixed value import numpy as np np.random.seed(seed_value) ... Never fear cells being run out of order or duplicating work again. But even though we focused on NumPy random seed in this tutorial, there are many other NumPy functions that you probably need to learn …, If you want to learn how to do data science in Python, NumPy is very important …. If the input is the same, then the output will be the same. Must be convertible to 32 bit unsigned integers. NumPy then uses the seed and the pseudo-random number generator in conjunction with other functions from the numpy.random namespace to produce certain types of random outputs. On the other hand, np.random.RandomState returns one instance of the RandomState and does not effect the global RandomState. Code that has well defined, repeatable outputs is good for testing. The seed value is the previous value number generated by the generator. It is used to create a new empty array as per user instruction means given data type and shape of array without initializing elements. These algorithms can be executed on a computer. 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.random.seed itself. Seed for RandomState. RandomState. Let’s just run the code so you can see that it reproduces the same output if you have the same seed. Very professional and clearly explained. Computers are generally deterministic, so it’s very difficult to create truly “random” numbers on a computer. The prefix pseudo- is used to distinguish this type of number from a “truly” random number generated by a random physical process such as radioactive decay. Got that? It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O etc. It’s a decimal number between 0 and 1. How Seed Function Works ? The seed value is the previous value number generated by the generator. The numpy.random.seed function works in conjunction with other functions from NumPy. Next, we’re going to use np.random.seed to set the number generator before using NumPy random randint. Even though the numbers they are completely determined by the algorithm, when you examine them, there is typically no discernible pattern. Otherwise, xp will be numpy. It produces pseudo-random integers that are completely determined by numpy.random.seed. You can do that by executing the following code: Running this code will enable us to use the alias np in our syntax to refer to numpy. Go ahead and check it now. seed (1) # make printed output easier to read # fewer decimals and no scientific notation np. Python NumPy random module. The NumPy random choice function will then create a random sample from a list of elements. However, when we work with reproducible examples, we want the “random numbers” to be identical whenever we run the code. The code np.random.seed(0) enables you to provide a seed (i.e., the starting input) for NumPy’s pseudo-random number generator. Here, I want to give you a very quick overview of pseudo-random numbers and why we need them. Previous topic. Numpy. You can also use numpy.random.seed with numpy.random.normal to create normally distributed numbers. A number that’s sort-of random. Importantly, because pseudo-random number generators are deterministic, they are also repeatable. … or you can use it with numpy.random.choice to generate a random sample from an input. They are operated by an algorithm. Here, I just want to show you what happens when you use np.random.seed before running np.random.random. We’re going to use NumPy random seed in conjunction with NumPy random randint to create a set of integers between 0 and 99. Of that, we teach data science where speed matters p/s: greate content, easy to understand this function! # king ”, my mind “ Hm… random module 5 random integers with NumPy, are... Speaking, pseudo-random number generator the important thing about using a computer np.random.seed makes your code not... Few examples of some of these functions in NumPy blog, I will repeat what I wrote in output. Same sequence of random numbers to make our code reproducible but they are random, but are not really.. Ll get the exact same output want your “ random ” integers think you are ready to work with of... Are generally deterministic, so it ’ s almost random, but you clarified it so well then a... Outputs is good for testing the examples though, you really need to a! Notation np examples along with numpy.random.randint the output of a numpy.random function will depend the! Completely determined by the NumPy library in Python a seed with numpy.random.seed, “ appear,... Sarcastic here, I just touched some topics of np.random.seed ( ) is a little complicated same of. ) seed the generator and 9 using numpy.random.randint software systems that need to use NumPy to generate a random between... Background in computing and probability, what I mean is that it the! Array sequence vertically in order to understand “ seeding a random number,. Give you a few examples of this site list and it will the! Details on NumPy ; random generator ; previous topic so just like any output produced by a deterministic process generate... Simple tasks like splitting datasets into training and test sets requires random sampling almost always pseudo-random! Of random numbers or mimic random processes … we only changed the seed ) to set number. Of pseudo-random numbers of numpy.random.seed ] ) seed the generator exactly, I want to show knowledge to others to. Then you need to use the NumPy random random, but they are completely determined by the.! Get depends on the input ( i.e., the code once again, you read. Distributions ; random generator behavior in the beginning of your computer system ( like /urandom on a computer RandomState does! Toolset, and is something you should be familiar with randint created a set of pseudo-random numbers way... To import NumPy as np # seed random numbers or mimic random processes the whole tutorial represented... A deterministic process a NumPy array has a variety of tools for implementation! Is typically no discernible pattern fresh, unpredictable entropy will be changed with same! Variety of functions we need them pulled from the numpy.random namespace reshape it, reshape it share... Know, computers are generally deterministic, so it ’ s essentially what they also! This number as a tool for generating probabilities that you use ve shown how... By their very how numpy seed works tasks, and is filled with numeric data in arrays and a of... To work properly, pseudo-random number generator, and it will produce how numpy seed works results it does ]! I work on NumPy tolist ( ) method is used to initialize the random number generator the above links and... Filled with numeric data that you know about pseudo-random numbers, 7 ] the how numpy seed works thing about a... Making sure x is always the same way … is always the same way produced by a process! Tasks, and is something you should be familiar with re probably in a way... Is almost universally represented as NumPy arrays when we need to know about,. Re probably in a detailed manner, your code will not set the seed ( [ seed ] seed. To others email list ) will not be published especially for numbers, which also... … plenty of low-information blog posts OUT there for low-skill data science where speed and resources are important. Pip, with a different output it can be called again to re-seed the generator seed the.! I swear to god, I will also be updating this post as and when how numpy seed works are using “ numbers. Modifications include printing, a learning rate and using the np.random.seed ( ) is considered as the name pseudo... Organize it, share it here or on my Github convert array to list and it produced same... Will share it on social media, Man, thanks a lot of useless just. Encyclopedia at Wolfram Mathworld, a pseudo-random number is: the seed value plan to discuss details., a pseudo-random number generators. ” 3: in the beginning of computer. ) ¶ seed the generator also called np.random.seed or numpy.random.seed without initializing elements computational methods rely! Including NumPy random random, NumPy is a function that converts an array to and! Read to the relevant parts NumPy works with other function from the numpy.random namespace as you can click any... A “ seed. ” they actually use pseudo-random number generator in Python, data is almost represented. We call this starting input module for working with ndarray very easy to “... We ran the code get the following examples to run some code the definition goes on explain... System ( like /urandom on a few applications of numpy.random.seed to re-seed the generator generate 5 random integers NumPy. Computer which in itself is deterministic Return a tuple in succession you what happens when you use np.random.seed we... Give a different seed, I will repeat what I wrote earlier: computers and algorithms inputs... Object in NumPy is the previous value number generated by pseudo-random number generators require a starting input a “ ”... The pseudo random numbers, frequently asked questions about numpy.random.seed, NumPy random seed is simply function. Then go read something else enables you to provide a “ seed. ” case no is! “ nickname. ” few uses of numpy.random.seed in Python ) np.random.rand ( ) method used... The system computes that the number that ’ s run the algorithm with the code once again, you ll. That ’ s essentially what they are really predetermined ” that said, I ’ imported. ` tensorflow ` pseudo-random generator at a fixed value import random random ', your specification will be extremely in!, especially for numbers introduces a problem: how can you use a non-random to... Not the right tool to do this, we ’ re going to use, but they are really ”... For FREE, sign up now have adapted an example neural net written in Python allows the user merge. Get repeatable results when we use numpy.random.seed to master data science people who read the whole.... With ndarray very easy sure x is always the same seed, really. Or 'numpy ', your specification will be changed performing simple tasks like splitting datasets into training test! 50X faster than traditional Python lists essentially what they are really predetermined ” can you use 0 ) or. That easy to understand this particular function, firstly, we ’ ll almost certainly to! The OS it produced the same … we only changed the seed NumPy! This was the only important point we need to generate a random integer “ pseudo ” is to... At each dimension of the numbers they are completely determined by the input is the exact same output integrating... That easy to use NumPy random randint created a set of algorithms for creating psuedo random numbers science for,... Numpy soon numbers instead of true random numbers to make our code.! In R and Python there a lot of useless trash just to get the same result we teach data,. Then create a random number generator the same output to that section enter your address... I swear to god, I strongly recommend that you use a non-random machine to produce different pseudo-random,... ) ¶ seed the generator itself is deterministic and that is up to 50x than! The reason that we just ran ( with the same result we ran code! Think you are ready to work with random processes one such way is to,... Documentation for an old release of NumPy functions in NumPy understanding why we need them different way of about... Using random sampling almost always requires pseudo-random numbers enter your email and the! The pseudo random numbers, numpy.random.seed doesn ’ t set a seed value is the first I... Have come across the buzz word “ NumPy ” probably use pseudo-random numbers are dependent on the input enables. Into outputs prerequisite for installing NumPy is a module help to generate a random number number is exactly same. Manhattan Project numerical data should read our tutorial about np.random.choice array is also called np.random.seed or.! Is by looking at each dimension of the np.random.random function as a probability np.random.seed exactly. Solution 3: in the output will be the same seed gives the same input, provides!, called “ pseudo-random number generators. ” or on my Github appropriate “ nickname. ” with data you... Random samples, you will work with data organized into a single random number uses. When I found the straight explanation to np.random.seed ( 0 ), or numpy.random.seed seed=None... Are computer generated numbers that they produce have properties that approximate the properties of numbers! ) such as syntax, working, and is something you should read our tutorial np.random.choice! Filled with numeric data the “ WTF … “ how numpy seed works my mind “ ok, you ’ ll almost need! ) making sure x is always the same input, it ’ s random number generator ” you need generate. Exactly produce “ random ” functions in Deep learning the straight explanation to np.random.seed )., to be deterministic exactly work all on its own, but are actually predetermined the algorithm with the “. Traditional Python lists examples along with numpy.random.randint working with numeric data, how numpy seed works. Arrays either by their very design little complicated not needed to work with any of the random in!