reduce

Inversion sum is not correct using Stream.reduce, what is going wrong here ? double[] array = {1.0, 2.0}; double iunversionSum = Arrays.stream(array).reduce(0.0, (a, b) -> Double.sum(1.0 / a, 1.0 / b)); output is .5 but expected is 1.5 (1/1 + 1/2) I think using map() it could be simplier. double inversionSum = Arrays.stream(arr).map(val -> 1 / val).sum(); The error in your reduce is: Double.sum(1.0 / a, 1.0 / b), starting the series with 0.0. Now check why your outcome is .5. Use Double.sum(a, 1.0 / b), if you want to use reduce. 来源： https://stackoverflow.com/questions/54144110/inversion-sum

I want to calculate a^b , e.g. 2^30, public long pow(final int a, final int b) first I used this manner return LongStream.range(0, b).reduce(1, (acc, x) -> a * acc); // 1073741824 Got right result. Then I want to calculate it parallelly, so naturally I changed it to return LongStream.range(0, b).parallel().reduce(1, (acc, x) -> a * acc); // 32 but in this case the result is just 32 . Why? So for supporting parallel I changed it again return Collections.nCopies(b,a).parallelStream().reduce(1, (acc, x) -> acc * x); // 1073741824 in this case it works. So what's wrong with parallel manner? reduce

I would like to compute the frequency of letters in a string using pythons map and reduce built-in functions. Could anyone offer some insight into how I might do this? What I've got so far: s = "the quick brown fox jumped over the lazy dog" # Map function m = lambda x: (x,1) # Reduce # Add the two frequencies if they are the same # else.... Not sure how to put both back in the list # in the case where they are not the same. r = lambda x,y: (x[0], x[1] + y[1]) if x[0] == y[0] else ???? freq = reduce(r, map(m, s)) This works great when all the letters are the same. >>> s 'aaaaaaa' >>> map(m, s)

i need help with this, i need group by id and sum, but i need a new object for each result let data =[ {"id":"2018", "name":"test", "total":1200}, {"id":"2019", "name":"wath", "total":1500}, {"id":"2019", "name":"wath", "total":1800}, {"id":"2020", "name":"zooi", "total":1000}, ] i have this code that return just one object with the result let result = data.reduce(function (r, o) { (r[o.id])? r[o.id] += o.total: r[o.id] = o.total; return r; }); but i need some like this [ {"id":"2018", "name":"test", "total":1200}, {"id":"2019", "name":"wath", "total":2300}, {"id":"2020", "name":"zooi", "total

as explained in the title, when i execute my Hadoop Program (and debug it in local mode) the following happens: 1. All 10 csv-lines in my test data are handled correctly in the Mapper, the Partitioner and the RawComperator(OutputKeyComparatorClass) that is called after the map-step. But the OutputValueGroupingComparatorClass's and the ReduceClass's functions do NOT get executed afterwards. 2. My application looks like the following. (due to space constraints i omit the implementation of the classes i used as configuration parameters, til somebody has an idea, that involves them): public class

Below is the code that I'm struggling to understand: let rectToDisplay = self.treasures.reduce(MKMapRectNull) { //1 (mapRect: MKMapRect, treasure: Treasure) -> MKMapRect in //2 let treasurePointRect = MKMapRect(origin: treasure.location.mapPoint, size: MKMapSize(width: 0, height: 0)) //3 return MKMapRectUnion(mapRect, treasurePointRect) } My understanding of reduce function is: var people [] // an array of objects var ageSum = 0 ageSum = people.reduce(0) { $0 + $1.age} //(0) = initial value //$0 = running total //$1 = an object in an array My understanding of closure is: { (params) ->

I am using Spark 1.5/1.6, where I want to do reduceByKey operation in DataFrame, I don't want to convert the df to rdd. Each row looks like and I have multiple rows for id1. id1, id2, score, time I want to have something like: id1, [ (id21, score21, time21) , ((id22, score22, time22)) , ((id23, score23, time23)) ] So, for each "id1", I want all records in a list By the way, the reason why don't want to convert df to rdd is because I have to join this (reduced) dataframe to another dataframe, and I am doing re-partitioning on the join key, which makes it faster, I guess the same cannot be done