![]() Remember, a probability distribution is a function that gives the probability of different outcomes. The uniform distribution is a type of probability distribution. A Quick Review of the Uniform Distribution Let’s quickly review the uniform distribution to find out. In particular, the np.random.uniform function creates Numpy arrays that contain numbers drawn from a uniform distribution. Like some of the other Numpy functions that I just mentioned – like np.random.normal and np.zeroes – the Numpy random uniform function creates Numpy arrays.īut it creates Numpy arrays with very specific types of numbers. Numpy Random Uniform Creates Arrays Drawn From a Uniform DistributionĪnd with that in mind, let’s return to. So Numpy has a variety of functions for creating Numpy arrays with different types of numbers. Numpy zeroes creates arrays that are filled with zeros. The Numpy full function creates arrays where every cell in the array contains the exact same number. The Numpy package has a variety of functions for creating Numpy arrays with different types of properties.įor example, the Numpy random normal function creates Numpy arrays that are filled with normally distributed numbers. The Numpy Package has many Functions for Creating Numpy Arrays If you take a look, you can see that a Numpy array is just a row-and-column structure that contains numeric data. A simple Numpy array filled with integers looks something like this: Specifically, Numpy has functions for creating and “wrangling” a data structure called a Numpy array.Ī Numpy array is just a data structure that contains numbers inside of a row-and-column structure. The first thing you need to understand is that the Numpy random uniform function creates Numpy arrays.Īs you probably know, Numpy is a package for working with numeric data in Python. Numpy Random Uniform Creates Numpy Arrays … so let me dive in and explain it a little. Unless you know a lot about Numpy and probability, it might not make sense The np.random.uniform function creates Numpy arrays filled with values that are drawn from a uniform probability distribution. Having said that, if you really want to understand how this function works, you should probably read the whole thing. If you need something specific, you can click on any of the following links and it will take you to the correct location in the tutorial. I’ll explain what the function does, explain the syntax, and show you clear examples of how the function works. ![]() When you set the seed (every time), it does the same thing every time, giving you the same numbers.In this tutorial I’ll show you how to use the np.random.uniform function (AKA, Numpy random uniform). The resulting number is then used as the seed to generate the next “random” number. (pseudo-)random numbers work by starting with a number (the seed), multiplying it by a large number, then taking modulo of that product. Quoting from that answer, here’s what it had to say: I also found a StackOverflow post - What does (0) do? which you can refer to understand the underlying mathematics involved, if you wanted to but I would strongly advice against it since it’s not relevant to the course. In other words once you execute the code block with np.ed(2), DON’T change the seed argument to any other number if you want to reproducibility. I know it sounds confusing(at least to me) but from what I understand, is that, “a fixed seed” is necessary for RandomState to produce the same results every time. So I checked the documentation for RandomState as well and here’s what I found:Ĭompatibility Guarantee A fixed seed and a fixed series of calls to ‘RandomState’ methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect. Parameters: seed : int or 1-d array_like, optional It can be called again to re-seed the generator. ![]() This method is called when RandomState is initialized. Here’s what I found from the NumPy documentation: ![]()
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