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算法介绍 矩阵求逆在程序中很常见,主要应用于求Billboard矩阵。按照定义的计算方法乘法运算,严重影响了性能。在需要大量Billboard矩阵运算时,矩阵求逆的优化能极大提高性能。这里要介绍的矩阵求逆算法称为全选主元高斯-约旦法。 高斯-约旦法(全选主元)求逆的步骤如下: 首先,对于 k 从 0 到 n - 1 作如下几步: 从第 k 行、第 k 列开始的右下角子阵中选取绝对值最大的元素,并记住次元素所在的行号和列号,在通过行交换和列交换将它交换到主元素位置上。这一步称为全选主元。 m(k, k) = 1 / m(k, k) m(k, j) = m(k, j) * m(k, k),j = 0, 1, ..., n-1;j != k m(i, j) = m(i, j) - m(i, k) * m(k, j),i, j = 0, 1, ..., n-1;i, j != k m(i, k) = -m(i, k) * m(k, k),i = 0, 1, ..., n-1;i != k 最后,根据在全选主元过程中所记录的行、列交换的信息进行恢复,恢复的原则如下:在全选主元过程中,先交换的行(列)后进行恢复;原来的行(列)交换用列(行)交换来恢复。-algorithm introduced in the matrix inversion process is very common, which are mainly used for Billboard matrix. In accordance with the definition of the method of calculating multiplication, seriously affecting the performance. The need for a large number of Billboard matrix operations, matrix inversion optimization can significantly improve performance. Here we introduce the matrix inversion algorithm called full-elected PCA Gauss-Jordan and France. Gauss-Jordan and France (all elected PCA) inversion of the following steps : First, for k from 0 to n-1 for the following steps : from the first trip k, k started out the bottom right corner Subarray largest absolute selected elements, and element remember meeting the line and out, the adoption OK exchange and the exchange out of its exchange
Date : 2025-12-31 Size : 3kb User : 刘亮

Shamir秘密分享算法的java实现,主要是使用平面上k点可以决定k-1次方的多项式,以k点为门槛值,应用Lagrange多项式插入方法的技术,回复原多项式,再求出主密钥。-Shamir secret sharing algorithm to achieve the java, is the use of the plane could be decided k k-1 power of the polynomial, a k-value of the threshold, Application of Lagrange polynomial method of technology insertion to restore the original polynomial, then calculated the main key.
Date : 2025-12-31 Size : 5kb User : linvg

DL : 0
k阶临近算法 可以用于文本分类和加密的应用-k-approaching algorithm can be used for text classification and encryption applications
Date : 2025-12-31 Size : 229kb User : chidayang

DL : 0
计算xk+1除CRC-32后的余式,k从1到32768-Calculated xk+ 1 In addition to CRC-32 after more than style, k from 1-32768
Date : 2025-12-31 Size : 4kb User : 小刀

DL : 0
Matlab --- --- --- --- --- --- --- --- --- --- --- --- - Function: [class,type]=dbscan(x,k,Eps) ------------------------------------------------------------------------- Aim: Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN) -Matlab ------------------------------------------------------------------------- Function: [class,type]=dbscan(x,k,Eps) ------------------------------------------------------------------------- Aim: Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN) -------------------------------------------------------------------------
Date : 2025-12-31 Size : 2kb User : Fouad Jasser

This paper presents a text line segmentation method for printed or handwritten historical Arabic documents. Documents are first classified into 2 classes using a K-means scheme. These classes correspond to document complexity (easy or not easy to segment). Then, a document which includes overlapping and touching characters, is divided into vertical strips.
Date : 2025-12-31 Size : 310kb User : ammar

迫零和最小均方误差比较,非常好的程序,保证可以仿真出图形-DIP text book kmeans image segmentation Input: ima: grey color image k: Number of classes Output: mu: vector of class means mask:
Date : 2025-12-31 Size : 1kb User : 姬昌

Adaptive k means clustering code
Date : 2025-12-31 Size : 3kb User : RAVIRAJ
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