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1. Random number generation how to#
2. Random number generation generator#
3. Random number generation update#
4. Random number generation code#

Linear congruential generators are known to be statistically weak, but PCG's state transition function only does half the work, so it doesn't need to be perfect.

Random number generation generator#

PCG's State-Transition Function The PCG family uses a linear congruential generator as the state-transition function-the “CG” of PCG stands for “congruential generator”. The PCG family takes a more balanced approach. The observation that underlies the PCG family is that these approaches are unbalanced, they put too much weight on one side or the other. For example, the Fortuna RNG has a trivial state transition function (it just increments a counter), but uses a cryptographic block cypher as the output function. Again, this is a very simple output function.Ī few RNGs adopt the opposite approach. Some RNGs combine multiple simple RNGs and thus have an output function that just merges them together (e.g., with addition or xor). Many RNGs just use the identity function! They just return the state as is (making them easily predicted). Most RNGs use a very simple output function. We can see them as two functions: The State-Transition Function Governs how the RNG's internal state changes every time you ask for a random number The Output Function Turns the RNG's internal state into the actual random number There are two parts to a random number generator.

Random number generation code#

And then from our application, we just have to say something like Random.generate NewSpin spin to get the next random value.Įxercises: Here are a few ideas to make the example code on this page a bit more interesting! The point here is that from small building blocks, we can create a Generator that describes pretty complex behavior. It says to generate three symbols and then put them together into a Spin. We then create a random generator that generates each symbol with equal probability.įrom there we use map3 to combine them into a new spin generator. We first create Symbol to describe the pictures that can appear on the slot machine. Type Symbol = Cherry | Seven | Bar | Grapes symbol : Random. We could create a generator like this: import Random Imagine we want to make a simple slot machine. Once we have some simple generators like probability and usuallyTrue, we can start snapping them together with functions like map3. That is all we need to know to get our random dice rolls, but generators can do quite a bit more! Combining Generators So in our example, the Generator produces a value between 1 and 6, and then it gets turned into a message like NewFace 1 or NewFace 4.

Random number generation update#

When the command is performed, the Generator produces some value, and then that gets turned into a message for your update function. From there you use the Random.generate to turn it into a command: generate : (a -> msg) -> Generator a -> Cmd msg

Random number generation how to#

We are just describing how to generate them. The point is that we are not actually generating the values yet.

Likewise, the usuallyTrue generator is saying it will produce a Bool, and more specifically, it will be true 80% of the time. The roll generator is saying it will produce an Int, and more specifically, it will produce an integer between 1 and 6 inclusive.

The core idea is that we have random Generator that describes how to generate a random value. We are using the elm/random package for this. So let’s see how it works in Elm! Random Generators Generating random values works a bit different than in languages like JavaScript, Python, Java, etc. The new thing here is command issued in the update function: Random.generate NewFace ( Random.int 1 6) VIEW view : Model -> Html Msg view model = SUBSCRIPTIONS subscriptions : Model -> Sub Msg subscriptions model = , Random.generate NewFace ( Random.int 1 6) | NewFace Int update : Msg -> Model -> ( Model, Cmd Msg)