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StrainStressAnaFEACodes
- 应力应变计算的有限元程序。上课的时候用matlab写的一个代码，其中的刚度矩阵计算，刚度矩阵整合和载荷矩阵计算，都具有通用性，可以参考用于有限元程序设计。-stress and strain of finite element program. When the class was using a Matlab code, which the stiffness matrix, Integration stiffness matrix and load matrix, are generic a
ISM
- ISM 解释型结构模型 根据邻接矩阵求可达矩阵，进行级划分的算法-ISM model to explain structure up under the adjacency matrix matrix, the class division algorithm
ar_dlda
- 在ar人脸库上实现Direct Fisher discriminant analysis,该方法首先对类内散布矩阵对角化，然后对类间散布矩阵对角化，使类间散布矩阵对角化的矩阵即使鉴别向量集-In the ar face database to achieve Direct Fisher discriminant analysis, this method first within-class scatter matrix diagonalization, and then between-cla
SVM
- In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is in
symMat
- A matrix of symbolic elements is reasonably useful for many analytic linear algebra applications, and for analytic vector, matrix and tensor differentiation. This function produces a matrix of arbitrary dimension of symbolic elements that are in
matrix_class
- 本C++程序定义了矩阵类，包含操作有：矩阵、行列式及线性方程组的相关操作。-The C++ program defines a matrix class, including operations include: matrices, determinants and linear equations related operations.
AnalgorithmusingtheSchurcomple-
- LDPC(Low-density-parity-check，低密度奇偶校验)码是一类具有稀疏校验矩阵的线性分组码，不仅有逼近Shannon限的良好性能，而且译码复杂度较低，结构灵活，是近年信道编码领域的研究热点-LDPC (Low-density-parity-check, low-density parity check) codes are a class of sparse check matrix of linear block codes, is not only good perfo
H-fliter
- 基于方差约束 ,研究一类不确定线性定常随机离散系统的 H ∞滤波问题。提出了一种 鲁棒滤波的新算法 ,该算法克服构造对角矩阵约束性较强- H ∞filtering problem under the constraint of variance was discussed for a class of linear stochastic uncertain system. A new algorithm of robust filter
juzhen
- 利用matlab仿真软件对矩阵特征值计算的这一类算法进行了编码计算-Matlab simulation software using feature value matrix algorithm of this class of coding using
BPnet
- //1、动态改变学习速率 //2、加入动量项 //3、运用了Matcom4.5的矩阵运算库（可免费下载,头文件matlib.h)， // 方便矩阵运算,当然，也可自己写矩阵类 //4、可暂停运算 //5、可将网络以文件的形式保存、恢复-//1, dynamically changing learning rate// 2, adding momentum item// 3, the use of matrix operations Matcom4.5 library (ava
matlab-chap11
- 主要包括两个源代码。其中一个源代码是识别程序的，另一个是一个矩阵类库的。-Consists of two source code. One is to identify the program source code, and the other is a matrix class library.
lda
- 对有类别监督的数据进行先行判别分析用于降维 数据为结构体类型，返回数据也是降维后的结构体类型。如(a.X a.y)a.X是带有类别的数据，a.y是类别信息-Synopsis: model = lda(data) model = lda(data,new_dim) Descr iption: This function is implementation of Linear Discriminant Analysis. The goal
Etap1
- It is the corresponding testing module of the function that is specified in the training phase. test_set is a NxD matrix where N is the number of samples in the test set and D is the dimension of the feature space. true_labels is a Nx1
GhostPoints
- This function takes a training-by-training matrix of distance scores, and the testing-by-training matrix of distance scores, and augments them by adding ghost points to the minority class.
lifting_97
- 实现9/7小波正反变换 注1： 采用标准正交方法，对行列采用不同矩阵（和matlab里不同） 注2： 为了保证正交，所有边界处理，全部采用循环处理 注3： 正交性验证，将单位阵带入函数，输出仍是单位阵（matlab不具有此性质） 注4： 此程序是矩阵实现，所以图像水平分量和垂直分量估计被交换位置 注5： 此程序实现的是类小波（wavelet-like）变换，是介于小波包变换与小波变换之间的变换 注6： 此程序每层变换相对原图像矩阵，产生的矩阵都是正交阵，这和小
kmeans
- function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers
fisher
- fisher判别，输入两类样本，根据类间相似度和类内相似度矩阵求解权值w和偏值b-fisher discriminant, enter the two types of samples, according to the similarity between class and class similarity matrix to solve the weights w and the partial value of b
Two-dimensional-random-number
- 学习采用Matlab程序产生正态分布的二维随机数 估计类均值向量和协方差矩阵的方法 类间离散度矩阵、类内离散度矩阵的计算方法-Learning using Matlab program to generate two-dimensional normal distribution random number estimated class mean vector and covariance matrix method class scatter matrix calculation metho
comp_gauss_dens_val
- Implementing comp_gauss_dens_val(m, S, x) function in MATLAB to compute Gaussian probability density value. x : feature vector m : class mean vector S : class covariance matrix
Confusion-matrix
- This file displays the Confusion matrix, which is always used to show the error according to each class.