accumulator

My Accumulator for my For… Next loop is skipping numbers in VB; Visual Studio 2010.

由 删除回忆录丶 提交于 2019-12-11 19:16:16
问题 I am trying to write a For... Next loop that will allow for 12 entries, or the cancel button. Somehow intEntries is only using 1, 3, 5, 7, 9, and 11. After that is completed the calculation is being divided by 13, and not 12. I'm not sure what it is I've got wrong, but it's apparently something. Any Assistance you can give me is greatly appreciated! Private Sub Button1_Click(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles Button1.Click 'initialize accumulator Dim

VHDL - Phase Accumulator with feedback

由 风流意气都作罢 提交于 2019-12-11 04:26:55
问题 I am trying to create a phase accumulator using VHDL that has the following characteristics. Inputs: D (Input signal) RESET CE CLK Outputs: Q (output signal - feedback) Source code: library IEEE; use IEEE.STD_LOGIC_1164.ALL; use IEEE.STD_LOGIC_ARITH.ALL; use IEEE.STD_LOGIC_UNSIGNED.ALL; entity Phase_accu is port ( D : in std_logic_vector(3 downto 0); CE : in std_logic; CLK : in std_logic; RESET : in std_logic; Q : out std_logic_vector(15 downto 0) ); end Phase_accu; architecture Behavioral of

How to create custom list accumulator, i.e. List[(Int, Int)]?

由 自作多情 提交于 2019-12-11 00:01:53
问题 I am trying to use custom accumulator in Apache Spark to accumulate pairs in a list. The result should have List[(Int, Int)] type. For this I creat custom accumulator: import org.apache.spark.AccumulatorParam class AccumPairs extends AccumulatorParam[List[(Int,Int)]] { def zero(initialValue: List[(Int,Int)]): List[(Int,Int)] = { List() } def addInPlace(l1: List[(Int,Int)], l2: List[(Int,Int)]): List[(Int,Int)] = { l1 ++ l2 } } Yet I can not instantiate variable of this type. val pairAccum =

Evaluation issue using accumulators in Prolog to evaluate a polynomial

由 青春壹個敷衍的年華 提交于 2019-12-10 16:48:24
问题 Background I need to write a predicate eval(P,A,R), where: P represents a list of polynomial coefficients, i.e. 1+2x+3x^2 is represented as [1,2,3]. A represents the value for X. R is the result of the polynomial at X=A. Example: eval([3,1,2],3,R) produces R = 24. *edit, previously incorrect example I am trying to use accumulators from this article and example on Learn Prolog Now. My Algorithm: 0. Initialize result and exponent variables to 0. 1. Take the head of the list. 2. Multiply the

Spark scala get an array of type string from multiple columns

由 三世轮回 提交于 2019-12-08 13:46:14
问题 I am using spark with scala. Imagine the input: I would like to know how to get the following output [see the column accumulator on the following image] which should be a Array of type String Array[String] In my real dataframe I have more than 3 columns. I have several thousand of column. How can I proceed in order to get my desired output? 回答1: You can use an array function and map a sequence of columns: import org.apache.spark.sql.functions.{array, col, udf} val tmp = array(df.columns.map(c

Three Dimensional Hough Space

由 爱⌒轻易说出口 提交于 2019-12-08 06:38:36
问题 Im searching for radius and the center coordinates of circle in a image. have already tried 2D Hough transform. but my circle radius is also a unknown. Im still a beginner to Computer vision so need guild lines and help for implementing three dimensional hough space. 回答1: You implement it just like 2D Hough space, but with an additional parameter. Pseudo code would look like this: for each (x,y) in image for each test_radius in [min_radius .. max_radius] for each point (tx,ty) in the circle

Irrefutable pattern does not leak memory in recursion, but why?

由 女生的网名这么多〃 提交于 2019-12-06 19:22:55
问题 The mapAndSum function in the code block all the way below combines map and sum (never mind that another sum is applied in the main function, it just serves to make the output compact). The map is computed lazily, while the sum is computed using an accumulating parameter. The idea is that the result of map can be consumed without ever having the complete list in memory, and (only) afterwards the sum is available "for free". The main function indicates that we had a problem with irrefutable

Spark accumulableCollection does not work with mutable.Map

由 邮差的信 提交于 2019-12-05 02:44:57
问题 I am using Spark to do employee record accumulation and for that I use Spark's accumulator. I am using Map[empId, emp] as accumulableCollection so that I can search employee by their ids. I have tried everything but it does not work. Can someone point if there is any logical issues with the way I am using accumulableCollection or Map is not supported. Following is my code package demo import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.SparkContext._ import

Statistical accumulator in Python

由 不问归期 提交于 2019-12-05 01:34:03
问题 An statistical accumulator allows one to perform incremental calculations. For instance, for computing the arithmetic mean of a stream of numbers given at arbitrary times one could make an object which keeps track of the current number of items given, n and their sum, sum . When one requests the mean, the object simply returns sum/n . An accumulator like this allows you to compute incrementally in the sense that, when given a new number, you don't need to recompute the entire sum and count.

Spark accumulableCollection does not work with mutable.Map

由 倖福魔咒の 提交于 2019-12-03 18:23:00
I am using Spark to do employee record accumulation and for that I use Spark's accumulator. I am using Map[empId, emp] as accumulableCollection so that I can search employee by their ids. I have tried everything but it does not work. Can someone point if there is any logical issues with the way I am using accumulableCollection or Map is not supported. Following is my code package demo import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.SparkContext._ import scala.collection.mutable object MapAccuApp extends App with Logging { case class Employee(id:String, name
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