probability

问题 I have an array of structs and one of the fields in the struct is a float. I want to pick one of the structs where the probability of picking it is relative to the value of the float. ie struct s{ float probability; ... } s sArray[50]; What is the fastest way to decide which s to pick? Is there a function for this? If I knew the sum of all the probability fields (Note it will not be 1), then could I iterate through each s and compare probability/total_probability with a random number,

问题 I have a bunch of keys that each have an unlikeliness variable. I want to randomly choose one of these keys, yet I want it to be more unlikely for unlikely (key, values) to be chosen than a less unlikely (a more likely) object. I am wondering if you would have any suggestions, preferably an existing python module that I could use, else I will need to make it myself. I have checked out the random module; it does not seem to provide this. I have to make such choices many millions of times for

问题 According to this documentation, arc4random_uniform() is recommended over constructions like arc4random() % upper_bound as it avoids "modulo bias" when the upper bound is not a power of two. How bad is the bias? For example if I generate random numbers with an upper bound of 6, what's the difference between using arc4random with % and arc4random_uniform() ? 回答1: arc4random() returns an unsigned 32-bit integer, meaning the values are between 0 and 2^32-1 = 4 294 967 295. Now, the bias results

问题 Consider that following program. import math import random def inside_unit_circle(point): """ Compute distance of point from origin """ distance = math.sqrt(point[0] ** 2 + point[1] ** 2) return distance < 1 def estimate_mystery(num_trials): """ Main function """ num_inside = 0 for dumm_idx in range(num_trials): new_point = [2 * random.random() - 1, 2 * random.random() - 1] if inside_unit_circle(new_point): num_inside += 1 return float(num_inside) / num_trials print estimate_mystery(10000)

问题 I wonder how it's possible to perform distributional sampling in Node.js. In particular, I have the number of elements, where the i'th value of the arrays is the probability of the element i'th, like following [0.2 0.3 0.5] Now I need to perform sampling, and the result of the sampling in half of the samples should be 2 in 0.2 of the samples is 0 and in 0.3 of the samples is 1. 回答1: Trivial method: function distribute(probs) { return function() { var r = Math.random(); var i = 0, acc = 0;

问题 I am having trouble understanding the likelihood function for GDA given in Andrew Ng's CS229 notes. l(φ,µ0,µ1,Σ) = log (product from i to m) {p(x(i)|y(i);µ0,µ1,Σ)p(y(i);φ)} The link is http://cs229.stanford.edu/notes/cs229-notes2.pdf Page 5. For Linear regression the function was product from i to m p(y(i)|x(i);theta) which made sense to me. Why is there a change here saying it is given by p(x(i)|y(i) and that is multiplied by p(y(i);phi)? Thanks in advance 回答1: The starting formula on page 5

问题 I work on calibration of probabilities. I'm using a probability mapping approach called generalized additive models. The algorithm I wrote is: probMapping = function(x, y, datax, datay) { if(length(x) < length(y))stop("train smaller than test") if(length(datax) < length(datay))stop("train smaller than test") datax$prob = x # trainset: data and raw probabilities datay$prob = y # testset: data and raw probabilities prob_map = gam(Target ~ prob, data = datax, familiy = binomial, trace = TRUE)

问题 I want to generate biased random numbers in matlab. Let me explain a bit more, by what I mean by biased. Lets say I have a defined upper bound and lower bound of 30 and 10 respectively. I want to generate N random numbers biased towards the bounds, such that the probability of the numbers lying close to 10 and 30 (the extremes) is more as compared to them lying some where in the middle. How can I do this? Any help is much appreciated :) 回答1: % Upper bound UB = 30 % Lower bound LB = 0; % Range

问题 In a poker where each player gets 5 cards from a 32 card deck, I am trying to calculate how many subsets contain exactly one pair using Mathematica. I created a deck with four suits and created subsets of all possible combinations of the cards and now I am trying to filter out all the wrong combinations using different methods to exclude the four of a kind and three of a kind and the full house. but the filter is still showing higher values than it actually is. The out put of my program is

问题 I am an experienced Python programmer trying to transition some Python code to Spark for a classification task. This is my first time working in Spark/Scala. In Python, both Keras/tensorflow and sci-kit Learn neural networks do a great job on the multi-class classification and I'm able to easily return the top 3 most probable classes along with probabilities which are key to this project. I have been generally successful in moving the code to Spark (Scala) and I'm able to generate the correct