Location:
Search - k matrix
Search list
Description: Extraction M.C.K MATRIX of A finite element simply supported beam model for vibration analysis
Platform: |
Size: 2066 |
Author: xuxiaoxia |
Hits:
Description: 调用过程 CM = Confusion_matrix(train_predicts, train_targets) [combining_predicts, errorrate] = combining_NB(DP, test_targets, CM) DP，三维数组，(i,j,k)为第k个样本的DP矩阵 targets 为 0 1 2 -process called CM = Confusion_matrix (train_predicts, train_targets) [combining_predicts, errorrate] = combining_NB (DP, test_targets, CM) DP, three-dimensional array (i, j, k) for the k samples of DP matrix targets for 0 1 2
Platform: |
Size: 2048 |
Author: |
Hits:
Description: 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
Platform: |
Size: 3072 |
Author: 刘亮 |
Hits:
Description: gmeans-- Clustering with first variation and splitting
文本聚类算法Gmeans ,使用了3种相似度函数,cosine,euclidean ,KL.文本数据使用的是稀疏矩阵形式.
-gmeans clustering with first variation and splitting
Gmeans,a text clustering algorithm, uses 3 functions,cosine,euclidean and KL in similarity measuring.Text data are described by sparse matrix.
Platform: |
Size: 71680 |
Author: 修宇 |
Hits:
Description: 本程序在对图像进行纹理分析（基于共发矩阵的方法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (based on total fat matrix method), on the basis of access to different regions of the image texture features, these features texture, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
Platform: |
Size: 350208 |
Author: 陈镇静 |
Hits:
Description: 本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
Platform: |
Size: 303104 |
Author: 陈镇静 |
Hits:
Description: 空间后方交汇求解相机外方位元素，变量如下
％ x，y 控制点像点坐标
％ X，Y，Z 控制点空间坐标
％f焦距
％X0，Y0，Z0，a，b，c六个外方位元素
％x0，y0，-f内方位元素：光心坐标
％cha，chb，chc：外方位角元素改正数
％count 记录迭代次数
％R 旋转矩阵
％A 线性化的偏导系数矩阵
％L 常数项矩阵
％M0 外方位元素矩阵
％M1 外方位元素改正数矩阵-meeting space for rear camera position outside elements, as follows% variable x, y control point pixel coordinates% X, Y, Z coordinates control room focal length f%% X0, Y0, Z0, a, b, c 6 exterior orientation elements% x0, y0,- f position within elements : Optical Center coordinates% cha, chb, chc : Foreign elements azimuth correction% record count the number of iterations rotation matrix R%% A linear partial derivative of the coefficient matrix% L constant Matrix% M0 Orientation% M1 matrix elements of exterior orientation correction matrix
Platform: |
Size: 1024 |
Author: 王立钊 |
Hits:
Description: ％ 信道容量C的迭代算法 ％
％ 函数说明： ％
％ [CC,Paa]=ChannelCap(P,k) 为信道容量函数 ％
％ 变量说明： ％
％ P：输入的正向转移概率矩阵，k：迭代计算精度 ％
％ CC：最佳信道容量，Paa：最佳输入概率矩阵 ％
％ Pa：初始输入概率矩阵，Pba：正向转移概率矩阵 ％
％ Pb：输出概率矩阵 ％
％ C：初始信道容量， r：输入符号数，s：输出符号数 ％- Channel capacity C of the iterative algorithm Function Description: [CC, Paa] = ChannelCap (P, k) for the channel capacity function Variable Description: P: input positive transition probability matrix, k: iterative calculation accuracy CC: the best channel capacity, Paa: optimal input probability matrix Pa: initial input probability matrix, Pba: positive transition probability matrix Pb: output probability matrix C: initial channel capacity, r: the number of input symbols, s: output symbol number
Platform: |
Size: 1024 |
Author: |
Hits:
Description: k中心点
编制和调试一个程序，它将用户输入的正规式转换为以状态图和矩阵形式表示的确定有穷自动机。
1．把正规式转换为NFA
2．将NFA确定化为DFA
• ＃作为正规式的终止符
• 考虑复合正规式
• 开始状态号为0
-focal point for the preparation of k and debug a program, it will the user to enter the formal conversion to a state diagram and matrix forms express the determination of DFA. 1. The formal type is converted to NFA2. Will determine the NFA into a DFA
Platform: |
Size: 1024 |
Author: 刘自咏 |
Hits:
Description: 本程序利用奇异值分解对3通道彩色图像进行压缩分解，具体步骤如下：
压缩过程：
1. 选取子图像大小K值，把图像分解成M×M个子图像，IMG(s),s=1,2,…, M2，其中M=N/K，原始图像IMG大小为N×N。
2. 计算这M2个子图像的平均值average，对每幅子图像减去均值图像得到新图像。
3. 计算相关矩阵R，其元素定义为 。
4. 计算R的特征值与特征向量，计算每幅子图像与最大特征向量的内积，便得到编码，即压缩后的图像。
-This procedure using singular value decomposition of 3-channel color image compression decomposition, concrete steps are as follows: compression process: 1. Select the sub-image size K value, the image is decomposed into M × M sub-image, IMG (s), s = 1, 2, ..., M2, in which M = N/K, the original image IMG size N × N. 2. M2 calculate the average of sub-image average, for each sub-image minus the new images mean images. 3. Calculation of correlation matrix R, defined as its elements. 4. Calculation of the characteristics of R value and eigenvector calculated each sub-image with the largest eigenvector of the inner product, they will have to code, that is, the compressed images.
Platform: |
Size: 334848 |
Author: wangweiming |
Hits:
Description: TextureAnlysis.m实现遥感图像的纹理分析，以 方向邻域内的灰度均值 和 灰度共生矩阵的熵 作为纹理特征，使用k-means聚类。-TextureAnlysis.m the realization of remote sensing image texture analysis to the direction of the gray-scale neighborhood of the mean and the entropy of gray level co-occurrence matrix as texture features, the use of k-means clustering.
Platform: |
Size: 207872 |
Author: xxl |
Hits:
Description: KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
Platform: |
Size: 2048 |
Author: 西晃云 |
Hits:
Description: k-均值聚类算法实现灰度图像分割，输入图像矩阵和聚类中心个数，返回为最终的聚类中心和图像中每个像素所属类的编号（对应于图像矩阵）-k-means clustering algorithm to achieve gray-scale image segmentation, the input image matrix and the number of cluster centers, the return for the final image of the cluster centers and their respective categories in each pixel number (corresponding to the image matrix)
Platform: |
Size: 1024 |
Author: cc |
Hits:
Description: 对一个50个结点（更多的节点的网络只需要修改模块中的标量维数就行）的复杂非线性耦合网络进行同步化仿真。首先生成K矩阵，然后运行simulink，即可得到50个洛仑兹混沌节点复杂网络的同步化曲线。-Of a 50-node (more network nodes only need to modify module scalar dimension on the line) the complexity of nonlinear coupling network synchronization simulation. First Generation K matrix, and then run the simulink, can be chaotic 50 Lorentz complex network node synchronization curve.
Platform: |
Size: 9216 |
Author: zhongsir |
Hits:
Description: 如何求K类均值，从给出的多维矩阵中可以求得K类动态均值，-How to order K-type mean, from the given multi-dimensional matrix can be obtained in the K-means type of dynamic,
Platform: |
Size: 29696 |
Author: 燕子 |
Hits:
Description: 名为k-means的MATLAB函数，实现k均值算法。输入矩阵X,w，输出最终估计值和聚类的标识数字。-Called the k-means of the MATLAB function, to achieve k means algorithm. Input matrix X, w, the output value of the final estimates and cluster identification number.
Platform: |
Size: 1024 |
Author: menghang |
Hits:
Description: 基于K-means聚类算法的社团发现方法
先定义了网络中节点关联度,并构建了节点关联度矩阵, 在此基础上给出了一种基于 K-means聚类算法的复杂网络社团发现方法。
以最小关联度原则选取新的聚类中心, 以最大关联度原则进行模式归类,直到所有的节点都划分完为止, 最后根据模块度来确定理想的社团数-K-means clustering algorithm based on the association discovery
To define a network node correlation, and build the node correlation matrix in this basis, given a K-means clustering algorithm based on a complex network of associations that way.
The principle of the minimum correlation to select a new cluster center to the principle of maximum correlation pattern classification until all the nodes are divided until the end, the last under the module to determine the degree of the ideal number of community
Platform: |
Size: 115712 |
Author: maverick |
Hits:
Description: 通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics of the sample frame to replace the class with the center, reducing the dimension of the nuclear matrix, and then use Sparse KPCA method for feature extraction of the nuclear matrix.
Platform: |
Size: 185344 |
Author: piano |
Hits:
Description: pso算法求解多维矩阵的matlab程序-pso algorithm matlab program for multi-dimensional matrix
Platform: |
Size: 3072 |
Author: andy |
Hits:
Description: 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.)
Platform: |
Size: 1024 |
Author: leser
|
Hits:
« 12
3
4
5
6
7
8
9
10
...
13
»