Search - k matrix

Search - k matrix - List

[Other] assem DL : 0: Assembel element matrices Ke ( and fe ) into the global stiffness matrix K ( and the global force vector f ) according to the topology matrix edof.-Assembel element matrices Ke ( and fe ) into the global stiffness matrix K ( and the global force vector f ) according to the topology matrix edof.
Date : 2025-12-29 Size : 1kb User : LEBH
[Other] covK_L DL : 0: 根据两类训练集样本分别计算协方差矩阵作为产生矩阵进行K-L变换，与一般的整体产生的产生矩阵进行K-L变换只是存在向量上的平移-According to two kinds of training set samples separately calculated covariance matrix as produced by k-l transform matrix, and the whole of general produced by k-l matrix transformation of vector is existing translation
Date : 2025-12-29 Size : 2kb User : 蔡远学
[Other] wanshell DL : 0: warshall法顺序：置新矩阵，开始运算；置i=1；对所有j如果A[j,i]=1，则对k=1,2,3……n运算啊A[j,k]:=A[j,k]+A[i,k]； i++；如果i<=n转上继续需要： 1.输入矩阵，用二维数组，可以考虑全局变量 2.设置矩阵最大值nMax 3.对i从0到n-1循环，寻找每列为1的项，为1则继续运算，否则返回增强说明： 1.输入方式选择，同时可以选择是否继续运算 2.不再每行确认，增加修改选项 3.使用字符串数组，可直接输出得到的结果-warshall law order: a new matrix set to begin operation set i = 1 for all j if A [j, i] = 1, then for k = 1,2,3 ... ... n operation ah A [j, k]: = A [j, k]+ A [i, k] i++ if i < = n turn on the continuing need: 1. Enter the Matrix, with two-dimensional array, we can consider global variables 2. set matrix maximum nMax 3. for i from 0 to n-1 loop, as 1 per item to find, as a continuing operation, otherwise enhance: 1. input choice and can choose whether to continue operations 2. not confirm each line to increase modified option 3. use an array of strings, the result can be directly output
Date : 2025-12-29 Size : 1kb User : kkk
[Other] Warshall-Algorithm DL : 0: Warshall’s Algorithm You are given Warshall’s Algorithm. Write a C-Program to accept the following initial matrix. 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 Perform the steps of Warshall’s Algorithm and output the final matrix. Algorithm: Begin 1. Set a new Matrix A = B. 2. Set i = 1. 3. For all j is A[j, i] = 1 then i = 1,…….,n. Set A[j, k] = A[j, k] + A[i, k]. 4. Add 1 to i 5. If i <= n then goto step 3 Else stop. End -Warshall’s Algorithm You are given Warshall’s Algorithm. Write a C-Program to accept the following initial matrix. 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 Perform the steps of Warshall’s Algorithm and output the final matrix. Algorithm: Begin 1. Set a new Matrix A = B. 2. Set i = 1. 3. For all j is A[j, i] = 1 then i = 1,…….,n. Set A[j, k] = A[j, k] + A[i, k]. 4. Add 1 to i 5. If i <= n then goto step 3 Else stop. End
Date : 2025-12-29 Size : 1kb User : Merwyn
[Other] kmeans DL : 0: kmeans函数：输入为类别数量k和数据矩阵A；输出为聚类结果A，和迭代次数-the kmeans functions: input number of categories k and data matrix A output of clustering results A, and the number of iterations
Date : 2025-12-29 Size : 2kb User : 刘欢
[Other] kMeansCluster DL : 0: k-means聚类算法，根据属性将矩阵中的对象分类-K-means clustering algorithm，classify the objects in data matrix based on the attributes
Date : 2025-12-29 Size : 1kb User : youyou
[Other] multi-task_lasso DL : 0: MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].-MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].
Date : 2025-12-29 Size : 467kb User : 莫琳
[Other] glasso DL : 1: Matlab implementation of the graphical Lasso model for estimating sparse inverse covariance matrix (a.k.a. precision or concentration matrix)
Date : 2025-12-29 Size : 23kb User : 莫琳
[Other] LGME DL : 0: input: param: parameters of the LMGE algorithm param.mu, param.alpha, param.beta are regularization parameters. param.p: dimension of shared subspace param.k: number of nearest neighbors for Laplacian matrix X: input data Y: groundtruth labels output: model.W: matrix W Reference: Web and Personal Image Annotation by Mining Label Correlation with Relaxed Visual Graph Embedding Yi Yang, Fei Wu, Feiping Nie, Heng Tao Shen, Yueting Zhuang and Alex Hauptmann. contact: yyang@cs.cmu.edu -Web and Personal Image Annotation by Mining Label Correlation with Relaxed Visual Graph Embedding
Date : 2025-12-29 Size : 1kb User : Arron
[Other] BP DL : 0: 一维信号BP重构算法，先生成稀疏度为K的稀疏矩阵，再加入高斯白噪声进行算法重构以及质量衡量。-BP signal reconstruction algorithm for one dimensional, Mr. into sparse matrix sparsity of K, then the Gauss white noise and measure the quality of reconstruction algorithm.
Date : 2025-12-29 Size : 48kb User : 蔡丽桑
[Other] OMP DL : 0: 在分解的每一步对所选择的全部原子进行正交化处理，这使得在精度要求相同的情况下，OMP算法的收敛速度更快。-BP signal reconstruction algorithm for one dimensional, Mr. into sparse matrix sparsity of K, then the Gauss white noise and measure the quality of reconstruction algorithm.
Date : 2025-12-29 Size : 10kb User : 蔡丽桑
[Other] k_meansMethod DL : 0: 聚类分析中的划分聚类算法的k-means算法（随机选取矩阵数据）-Partition clustering algorithm in clustering analysis of k- means algorithm (random matrix data)
Date : 2025-12-29 Size : 1kb User : 张晓薇
[Other] tide_uv DL : 0: 潮流准调和分析 Dd.m [DO1 dO1 DK1 dK1 DM2 dM2 DS2 dS2 DM4 dM4 DMS4 dMS4]=Dd(y,D,Y,tm,A1) 计算6个准调和分潮的振幅系数和初相角公式 aM.m [N n aa liu_u liu_v]=aM() 基础系数矩阵，目前D和d值是取用的各段数据的中间时间值，t是以中间为0左右对称取；视差潮龄A1需要预先得知同时本程序负责读取原始测流数据，格式为站位名称观测数据段数N（即大中小潮）第一段起始时间YYYY MM DD HH mm ss 第一段个数n1 speed direction speed direction ... 第二段起始时间 YYYY MM DD HH mm ss 第二段个数n2 speed direction speed direction ... 第三段起始时间 YYYY MM DD HH mm ss 第三段个数n3 speed direction speed direction ... UVg.m [U V Ug Vg]=UVg() 求调和常数U V和迟角Ug Vg Tuoyuan.m 计算各准调和分潮的椭圆要素W theta K k -The quasi harmonic analysis of tidal current Dd.m [DO1 dO1 DK1 dK1 DM2 dM2 DS2 dS2 DM4 dM4 DMS4 dMS4]=Dd (y, D, Y, TM, A1) calculation of 6 quasi harmonic division of amplitude and initial phase angle formula of the coefficient of tide AM.m [N n AA liu_u liu_v]=aM () foundation coefficient matrix, the current D and D values are the middle time of each section of data taken the value of T is in the middle for symmetric about 0 Age of parallax A1 need to know in advance At the same time, this program is responsible for reading the original flow measurement data, format for Site name The observation data segment number N (that is, in the low tide) The first section of the YYYY MM DD HH mm the starting time of SS The first section number N1 Speed direction Speed direction ... The second section starting time YYYY MM DD HH mm SS The second section number N2 Speed direction Speed direction ... The third section starting time YYYY MM DD HH mm SS The third section number
Date : 2025-12-29 Size : 32kb User : 帅阳
[Other] CAN-code DL : 0: Clustering and Projected Clustering with Adaptive Neighbors -We proposed a CAN clustering algorithm with adaptive neighbors, the learned similarity matrix can be directly used for clustering, without having to perform K-means or other discretization procedures. Theoretical analysis reveals the proposed CAN clustering algorithm is connected with the K-means clustering problem, and the CAN can achieve much better clustering results than traditional K-means algorithm does. For the high-dimensional clustering problem, we propose a Projected CAN (PCAN) algorithm, which performs clustering and dimensionality reduction simultaneously. Theoretical analysis reveals the proposed PCAN clustering algorithm is connected with unsupervised LDA, and the PCAN can achieve better clustering or dimensionality reduction results than previous clustering algorithms or unsupervised dimensionality reduction algorithms do.
Date : 2025-12-29 Size : 1.35mb User : Ye
[Other] siepie_v86 DL : 0: 一个计算声子晶体结构的一维传递矩阵法,到达过程是的泊松过程，基于K均值的PSO聚类算法。- A one-dimensional transfer matrix method to calculate the phonon crystal structure, Arrival process is a Poisson process, K-means clustering algorithm based on the PSO.
Date : 2025-12-29 Size : 4kb User : jktdan
[Other] matrix DL : 0: 矩阵乘法给定两个矩阵 A 和 B,其中 A 是具有 M 行、K 列的矩阵, B 为 K 行、N 列的矩阵, A 和 B 的矩阵积为矩阵 C, C 为 M 行、N 列。矩阵 C 中第 i 行、第 j 列的元素 Cij 就是矩阵 A 第 i 行每个元素和矩阵 B 第 j 列每个元素乘积的和,即要求:每个 Ci j 的计算用一个独立的工作线程,因此它将会涉及生成 M×N 个工作线程。主线程(或称为父线程)将初始化矩阵 A 和 B,并分配足够的内存给矩阵 C,它将容纳矩阵 A 和 B 的积。这些矩阵将声明为全局数据,以使每个工作线程都能访问矩阵 A、B 和 C。(Matrix multiplication Given two matrices A and B, where A is a matrix with M rows, K columns, B is K rows, N columns are matrices, A, and The matrix product of B is matrix C, C is M row, and N column. The element J in column I and column C in matrix Cij is the matrix A Line I, the sum of the products of each element and the matrix B, column J, i.e. Requirements: each Ci J is computed with an independent worker thread, so it will involve generating M * N worker threads. main The thread (or parent thread) will initialize the matrix A and B, and enough memory allocated to the C matrix, it will accommodate matrix A Product of B. These matrices will be declared global data so that each worker thread can access matrices A, B, and C.)
Date : 2025-12-29 Size : 1kb User : leser
[Other] Disfrft DL : 1: 特征分解型求取离散分数阶傅里叶变换特征分解算法通过求ＤＦＴ的核矩阵Ｆ（ｋ，ｎ）的特征值和特征向量构造ＤＦＴ核矩阵的分数幂，以此作为Ｆａ（ｋ，ｎ）来计算ＤＦｒＦＴ。(The kernel matrix eigenvalue decomposition algorithm for DFT F (k, n) characteristic value And the feature vectors are constructed as the fractional powers of the DFT kernel matrix, which are used as Fa (k, n) To calculate DFrFT.)
Date : 2025-12-29 Size : 1kb User : hufei
[Other] 高大上欧美风商务PPT模板 DL : 0: 双色点阵原理图。64*32点阵。四路数据输入。两个138级联(Two color dot matrix schematic. 64*32 dot matrix. Four path data input. Two 138 cascades)
Date : 2025-12-29 Size : 16.98mb User : 啊哈是奥德赛v
[Other] Fourier-k-m-reconstuction DL : 0: 在构造压缩感知测量矩阵中，用傅里叶矩阵实现稀疏度k和m与信噪比之间的关系(In the construction of compressed sensing measurement matrix, the relationship between sparsity k and m and signal-to-noise ratio is realized by Fourier matrix.)
Date : 2025-12-29 Size : 2kb User : 徐徐的非
[Other] BRMUL DL : 1: 求m*n阶矩阵A与n*k阶矩阵B的乘积矩阵(Find the product matrix of M * n matrix A and N * k matrix B)
Date : 2025-12-29 Size : 1kb User : ly1659291762