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t2_4
- 本题采用的计算方法为：主要求解三对角阵方程组得解。采用的计算方法为“追赶法”。 算法思路为：求解方程Ly=d（追）——〉求解Ux=y（赶） -that the use of the method of calculating : three pairs for the main diagonal matrix equations in the solution. Using the method of calculating "catch up law." Algor
kalman
- runs Kalman-Bucy filter over observations matrix Z for 1-step prediction onto matrix X (X can = Z) with model order p V = initial covariance of observation sequence noise returns model parameter estimation sequence A, sequence of predicted
math
- 矩阵运算：求逆、转置、相乘等，然后通过矩阵运算求多项式纠正的值，多项式如：X=a0+a1*x+a2*y+a3*x*x+a4*x*y+a5*y*y Y=b0+b1*x+b2*y+b3*x*x+b4*x*y+b5-Matrix operations: inversion, transpose, multiply, etc., and then through the matrix for computing the value of correct polynomial, polynomial
Matlabcodes-RobustPCA
- Matlab codes for Robust PCA multivariate control chart-Robust PCA multivariate control chart mainly consists two steps: Step1 Calculates the robust mean and the robust covariance of original dataset using the minimum covariance determinant (M
lu
- 用lu分解法求解线性代数方程组，可以输出u|y增广矩阵-Using lu decomposition method for solving linear algebraic equations, can output u | y augmented matrix
erpang
- 输入矩阵的行列，选择旋转方式，输出旋转后的矩阵。 （1）绕X轴旋转； （2）绕Y轴旋转； （3）绕原点旋转。 -Reflecting the rotation matrix
sova0
- This function implememts Soft Output Viterbi Algorithm in trace back mode Input: rec_s: scaled received bits. rec_s(k) = 0.5 * L_c(k) * y(k) L_c = 4 * a * Es/No, reliability value of the channel y: received bits g: encoder generato
sequence-alignment-matrix-
- 求解序列比对得分矩阵，对于两个序列S和T，令[S]和[T]分别为序列S和T的长度，S[i]和T[j](其中正整数i，j满足0≤j< [S]和0≤j <[T])都属于字符集Ω={A,T,C,G,-},对Ω中的任何元素和空符号。 设计S[i]和T[j]两两之间的一个记分值来比较序列间的同一性的优劣,用记分函数σ(x，y)表示， σ(S[i],T[j])=2 ( S[i]=T[j]≠-)；σ(S[i],T[j])= -1 ( S[i]≠T[j],S[i]≠-,T[j]≠-)；σ(S
TSP_TS
- Solution for Travelling Salesman Problem by using Tabu Search heuristics. Archive contains sources and some data to test the appllication. As an input, we take the coordinates of cities (x,y) and then transform them into distances matrix. All co
TSP_SA
- Solution for Travelling Salesman Problem using the simulated annealing heuristic. As an input, we take coordinates of cities (x,y) and then transform them into distances matrix (we assume, the distance between x and y is the same as between y and
LU-Decomposition
- Suppose we are able to write the matrix A as a product of two matrices, L.U = A, where L is lower triangular (has elements only on the diagonal and below) and U is upper triangular (has elements only on the diagonal and above). We can use a decomposi
COPXY
- COPXY is n x m matrix which contains center of preasure (COP) data of static posturography tests on Parkinson s Disease (PD) Subjects. COPXY m columns are n/2 trials made of X Y data from a PD subject. COPXY n rows are n observations within a trial.
GAUSSJ
- 线性方程组的解法 全主元高斯－约当(Gauss-Jordan)消去法 用高斯－约当消去法求解A[XY]=[BI]，其中A为n*n非奇异矩阵，B为n*m矩阵，均已知；X(n*m)，Y(n*n)未知。-Solution of linear equations the main yuan Gaussian- Jordan (Gauss-Jordan) elimination method Gauss- Jordan elimination method to solve A [XY] = [B
test
- 匈牙利算法矩阵优化解决最大权匹配问题。在一个二分图内，左顶点为X，右顶点为Y，现对于每组左右连接Xi,Yj，有权值wij。 求匹配使得所有wij的和最大。-Hungary matrix optimization algorithm to solve the maximum weight matching problem
ldlt
- 利用ldlt分解求解系数矩阵为一维存储时的方程组-请键入文字或网站地址，或者上传文档。 取消 Lìyòng ldlt fēnjiě qiújiě xìshù jǔzhèn wéi yī wéi cúnchú shí de fāngchéng zǔSolving equations use ldlt coefficient matrix decomposition when stored as a one-dimensional
myomp
- 应用正交匹配追踪求解等式y=Ax,要求： 待求x是稀疏向量，A为高斯随机矩阵 调用形式：x = myomp(A,y,err) A -线性投影矩阵； y -投影向量 err -所需精度-apply Orthogonal matching pursuit to solve the equation y = Ax, requirements: the unknown x is sparse vector, A is a Gaussian random. ca