weighted

Lets assume: List<element> which element is: public class Element(){ int Weight {get;set;} } What I want to achieve is, select an element randomly by the weight. For example: Element_1.Weight = 100; Element_2.Weight = 50; Element_3.Weight = 200; So the chance Element_1 got selected is 100/(100+50+200)=28.57% the chance Element_2 got selected is 50/(100+50+200)=14.29% the chance Element_3 got selected is 200/(100+50+200)=57.14% I know I can create a loop, calculate total, etc... What I want to learn is, whats the best way to do this by Linq in ONE line (or as short as possible), thanks. UPDATE

This question is related to two different questions I have asked previously: 1) Reproduce frequency matrix plot 2) Add 95% confidence limits to cumulative plot I wish to reproduce this plot in R: I have got this far, using the code beneath the graphic: #Set the number of bets and number of trials and % lines numbet <- 36 numtri <- 1000 #Fill a matrix where the rows are the cumulative bets and the columns are the trials xcum <- matrix(NA, nrow=numbet, ncol=numtri) for (i in 1:numtri) { x <- sample(c(0,1), numbet, prob=c(5/6,1/6), replace = TRUE) xcum[,i] <- cumsum(x)/(1:numbet) } #Plot the

I need to obtain a random sample from an ElasticSearch index, i.e. to issue a query that retrieves some documents from a given index with weighted probability Wj/ΣWi (where Wj is a weight of row j and Wj/ΣWi is a sum of weights of all documents in this query). Currently, I have the following query: GET products/_search?pretty=true {"size":5, "query": { "function_score": { "query": { "bool":{ "must": { "term": {"category_id": "5df3ab90-6e93-0133-7197-04383561729e"} } } }, "functions": [{"random_score":{}}] } }, "sort": [{"_score":{"order":"desc"}}] } It returns 5 items from selected category,

I have a 2396x34 double matrix named y wherein each row (2396) represents a separate situation consisting of 34 consecutive time segments. I also have a numeric[34] named x that represents a single situation of 34 consecutive time segments. Currently I am calculating the correlation between each row in y and x like this: crs[,2] <- cor(t(y),x) What I need now is to replace the cor function in the above statement with a weighted correlation. The weight vector xy.wt is 34 elements long so that a different weight can be assigned to each of the 34 consecutive time segments. I found the Weighted

I have a time series x_0 ... x_t . I would like to compute the exponentially weighted variance of the data. That is: V = SUM{w_i*(x_i - x_bar)^2, i=1 to T} where SUM{w_i} = 1 and x_bar=SUM{w_i*x_i} ref: http://en.wikipedia.org/wiki/Weighted_mean#Weighted_sample_variance The goal is to basically weight observations that are further back in time less. This is very simple to implement but I would like to use as much built in funcitonality as possible. Does anyone know what this corresponds to in R? Thanks R provides weighted mean. In fact, ?weighted.mean shows this example: ## GPA from Siegel

Is there a way to use the numpy.percentile function to compute weighted percentile? Or is anyone aware of an alternative python function to compute weighted percentile? thanks! Unfortunately, numpy doesn't have built-in weighted functions for everything, but, you can always put something together. def weight_array(ar, weights): zipped = zip(ar, weights) weighted = [] for i in zipped: for j in range(i[1]): weighted.append(i[0]) return weighted np.percentile(weight_array(ar, weights), 25) Alleo Completely vectorized numpy solution Here is the code I'm using. It's not an optimal one (which I'm