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题目链接:Construct a Matrix 题意:构造一个矩阵,要求矩阵的每行每列的和都不相同.矩阵的边长是前n项斐波那契的和. 思路:由sn = 2*(fn-1)+(fn-2)-1,只要知道第n-1和第n-2项即可,n的范围是10^9,可由矩阵快速幂求出第n项.然后,构造矩阵,上三角为1,下三角全为-1,对角线1和0交替.[真是个天才...!!!]矩阵快速幂求第n项时,构造的矩阵是a[0][0] = f2, a[1][0] = f1, a[0][1] = 1, a[1][1] = 0...…
题目链接:fzu 1911 Construct a Matrix 题目大意:给出n和m,f[i]为斐波那契数列,s[i]为斐波那契数列前i项的和.r = s[n] % m.构造一个r * r的矩阵,只能使用-1.0.1.使得矩阵的每行每列的和都不相同,输出方案,不行的话输出No. 解题思路:求r的话用矩阵快速幂求,每次模掉m, { {1, 1, 0}, {1, 0, 0}, {1, 1, 1} } * { f[i], f[i -1], s[i] } = { f[i + 1], f[i], s[i…
C. Construct a Matrix Time Limit: 1000ms Case Time Limit: 1000ms Memory Limit: 32768KB Special Judge   64-bit integer IO format:  %I64d      Java class name:  Main Submit  Status Font Size:  +   - There is a set of matrixes that are constructed subje…
There is a set of matrixes that are constructed subject to the following constraints: 1. The matrix is a S(n)×S(n) matrix; 2. S(n) is the sum of the first n Fibonacci numbers modulus m, that is S(n) = (F1 + F2 + … + Fn) % m; 3. The matrix contains on…
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Palindromic Matrix time limit per test 2 seconds memory limit per test 256 megabytes input standard input output standard output Let's call some square matrix with integer values in its cells palindromic if it doesn't change after the order of rows i…
任意门:http://codeforces.com/contest/1118/problem/C C. Palindromic Matrix time limit per test 2 seconds memory limit per test 256 megabytes input standard input output standard output Let's call some square matrix with integer values in its cells palind…
待补充…… AP算法,即Affinity propagation,是Brendan J. Frey* 和Delbert Dueck于2007年在science上提出的一种算法(文章链接,维基百科) 现在只是初步研究了一下官网上提供的MATLAB源码:apcluster.m %APCLUSTER Affinity Propagation Clustering (Frey/Dueck, Science 2007) % [idx,netsim,dpsim,expref]=APCLUSTER(s,p)…
String Matching: Levenshtein distance Purpose: to use as little effort to convert one string into the other Intuition behind the method: replacement, addition or deletion of a charcter in a string Steps Step Description 1 Set n to be the length of s.…
特征值与特征向量 下面这部分内容摘自:强大的矩阵奇异值分解(SVD)及其应用 特征值分解和奇异值分解在机器学习领域都是属于满地可见的方法.两者有着很紧密的关系,在接下来会谈到,特征值分解和奇异值分解的目的都是一样,就是提取出一个矩阵最重要的特征.先谈谈特征值分解吧: 如果说一个向量v是方阵A的特征向量,则可以表示成下面的形式: 这时候λ就被称为特征向量v对应的特征值,一个矩阵的一组特征向量是一组正交向量.特征值分解是将一个矩阵分解成下面的形式: 其中Q是这个矩阵A的特征向量组成的矩阵,Σ是一个对…