I want to swap consecutive elements in list with a probability. For example, I have this list: I wrote the following code thats swap the consecutive elements The above code results in the following : However, what I want to do is to use a probability for the swap. Let’s assume the probability of a swap is 50%. So only
Tag: probability
Rolling N Non-regular Dice in Constant Time
Say I have a non-regular dice defined by probabilities in a list that add up to one, e.g [0.1, 0.3, 0.4, 0.2]. I can use the following code to simulate rolling that dice n times: Counter({3: 4000343, 2: 2998523, 4: 2000309, 1: 1000825}) However, for large n, the code gets quite slow, as choices iterates n times. Is there an
Binomial distributions (Bernoulli trials) with different probabilities
I want to speed up the code below – namely the for loop. Is there a way to do it in numpy? More explanations – I want to generate N sequences of Bernoulli trials (ie. 0s and 1s, called throws) where each sequence has a mean (probability p) given by values in another array (mu_throws). This can be sampled from
Rolling of Dice probability of getting exactly two 3s in ‘k’ rolls [closed]
Closed. This question is not reproducible or was caused by typos. It is not currently accepting answers. This question was caused by a typo or a problem that can no longer be reproduced. While similar questions may be on-topic here, this one was resolved in a way less likely to help future readers. Closed 10 months ago. Improve this question
How do I distribute a value between numbers in a list
I am creating a bias dice rolling simulator I want the user to: 1.Input the number they would like to change the prob of 2.Input the prob (in decimal form) Then I would like my program to distribute the remainder between the other values, this is my first post – let me know if any other info is needed My
Trying to run a test to see how many children it takes to have one of each sex but the array is not working properly?
I am running some code for a class and I have no clue what to do, I submitted the assignment but it’s not helpful if I don’t know what to do in the end. Here is the code that I am running to try and track these tests. The idea is to see how many children it takes before you
Write a random number generator that, based on uniformly distributed numbers between 0 and 1, samples from a Lévy-distribution?
I’m completely new to Python. Could someone show me how can I write a random number generator which samples from the Levy Distribution? I’ve written the function for the distribution, but I’m confused about how to proceed further! The random numbers generated by this distribution I want to use them to simulate a 2D random walk. I’m aware that from
How to generate random numbers with predefined probability distribution?
I would like to implement a function in python (using numpy) that takes a mathematical function (for ex. p(x) = e^(-x) like below) as input and generates random numbers, that are distributed according to that mathematical-function’s probability distribution. And I need to plot them, so we can see the distribution. I need actually exactly a random number generator function for
convert an unfair coin into a fair coin in Python 2.7
Using Python 2.7. Suppose I have an unfair coin and I want to turn it into a fair coin using the following way, Probability of generating head is equal for unfair coin; Flip unfair coin and only accept head; When a head is appearing, treat it as 1 (head for virtual fair coin), when another head is appearing, treat it
How do I simulate flip of biased coin?
In unbiased coin flip H or T occurs 50% of times. But I want to simulate coin which gives H with probability ‘p’ and T with probability ‘(1-p)’. something like this: Answer random.random() returns a uniformly distributed pseudo-random floating point number in the range [0, 1). This number is less than a given number p in the range [0,1) with