computation

问题 A quite confusing thing is what I got: The Median Absolute Deviation output of the following vector is vec = c( -5.665488 ,3.963051, 14.14956, 0, -5.665488) > mad(vec) [1] 8.399653 However, if I compute that I got the following value: Median absolute deviation = 5.665488 which is equal to the value of the computation I have found online as well: http://www.miniwebtool.com/median-absolute-deviation-calculator/ How can the difference between the calculated value of mine and the website and the

问题 There are two statements: If a decision problem A is polynomial-time reducible to a decision problem B (i.e., A≤ pB ), and B is NP-complete, then A must be NP-complete. And: If a decision problem B is polynomial-time reducible to a decision problem A (i.e., B≤ pA ), and B is NP-complete, then A must be NP-complete. Which of the above statements are true? Can you also give explanation? 回答1: the first statement is false because it means that by solving B and then applying some polynomial time

问题 I want to find optimal parameters i, j, k in 0..99 for a given computational problem, and I need to run: for i in range(100): for j in range(100): for k in range(100): dothejob(i, j, k) # 1 second per computation This takes a total of 10^6 seconds, i.e. 11.5 days. I started doing it by splitting the work among 4 processes (to use 100% computing power of my 4-core CPU computer): for i in range(100): if i % 4 != 0: # replace != 0 by 1, 2, or 3 for the parallel scripts #2, #3, #4 continue for j

问题 I looking for the information about fast GCD computation algorithms. Especially, I would like to take a look at the realizations of that. The most interesting for me: - Lehmer GCD algorithm, - Accelerated GCD algorithm, - k-ary algorithm, - Knuth-Schonhage with FFT. I have completely NO information about the accelerated GCD algorithm, I just have seen a few articles where it was mentioned as the most effective and fast gcd computation method on the medium inputs (~1000 bits) They looks much

问题 I am looking for a way to get all combination of a list item. what i thinking is to have a two dimention array, similary to a bit map e.g bit[][] mybitmap; for example if i have 4 item in my list "A, B, C, D" i want my bitmap to be populate like this A B C D 0, 0, 0, 1 --> D 0, 0, 1, 0 --> C 0, 0, 1, 1 --> C, D 0, 1, 0, 0 --> B 0, 1, 0, 1 0, 1, 1, 0 0, 1, 1, 1 1, 0, 0, 0 1, 0, 0, 1 1, 0, 1, 0 1, 0, 1, 1 --> A, C, D 1, 1, 0, 0 1, 1, 0, 1 1, 1, 1, 0 1, 1, 1, 1 --> A, B, C, D but how can i write

问题 I would like to compute the values in a number of cells, that get updated as one moves a slider bar in a different part of the table. I currently am storing the value after it is defined, but it needs to be updated. I've tried defining something like this: onchange="myFunction()" where myFunction would redefine the variable, but that did not work. I think that the solution is to insert something under the initialized: function (table) area of the code for a dynamic update (which I'm not sure

问题 i have a table with a column vector of type bit(2000) . how does the db engine handle operations AND and OR over this values? does it simply divide into 32bit chunks (or 64, respectively) and then compares each chunk separately and in the end simply concats the results together? or does it handle simply as two strings? my point is to predict, which use case would be faster. i got a key-value data (user-item). userID | itemID U1 | I1 U1 | Ix Un | Ij for each user i want to calculate a list of

问题 I have a dataset of 370k records stored in a Pandas Dataframe which needs to be integrated. I tried multiprocessing, threading, Cpython and loop unrolling. But I was not successful and the time shown to compute was 22 hrs. The task is as follows: %matplotlib inline from numba import jit, autojit import numpy as np import pandas as pd import matplotlib.pyplot as plt with open('data/full_text.txt', encoding = "ISO-8859-1") as f: strdata=f.readlines() data=[] for string in strdata: data.append

问题 I have a round robin tournament where I create all the games necessary (7 games per participant) for 8 teams. I however need 10 games per participant which means I need to duplicate matchups, and on top of that 1 and 5 can't play each other. You can see from the data below the games I generated for each participant (# of games) in the order it was created which would be the round. I am trying to figure out the best possible way to duplicate the matchups and evently distribute the matchups in

问题 As far as I know, a Turing machine can be made to execute loops or iterations of instructions encoded on a Tape. This can be done by identifying Line separators and making the Turing machine go back until a specific count of Line separators is reached (that is, inside the loop). But, can a Turing machine also execute a recursive program? Can someone describe various details for such a Turing Machine? I suppose, if recursion can be executed by a Turing machine, the Quicksort can also be