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[Algorithm] pattern_recognition_v6.1 DL : 0: 完整的模式识别库，包括矩阵运算，各种模式识别算法，如K均值、SVM、RVM、NN、LDA等-Complete pattern recognition library, including matrix computation, a variety of pattern recognition algorithms, such as K-means, SVM, RVM, NN, LDA, etc.
Date : 2025-12-29 Size : 1.11mb User : mountyang
[Algorithm] matlabmatrix DL : 0: 1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if A = 1 2 3 then B = 3 2 1 4 5 6 6 5 4 7 8 9 9 8 7 Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A). 2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output: Enter k>0: 100 144 is the smallest fibonacci number greater than or equal to 100. It is the 12th fibonacci number.
Date : 2025-12-29 Size : 1kb User : wangshujuan
[Algorithm] Iluk DL : 0: 这是用于线性方程组求解的ILUK预处理算法的实现。在VC++编译通过。矩阵采用压缩稀疏行格式存储（CSR），采用如下结构存储：struct Distmatrix {double **ma int **ja,dimension,*nnzrow } 很容易移植到自己定义数值计算软件包中。经本人测试计算效率比Fortran写的高很多（比如与Sparskit2比较）。-This is used for solving linear equations ILUK preconditioning algorithm. In VC++ Compiled through. Matrix using compressed sparse row storage format (CSR), using the following structure of memory: struct Distmatrix (double** ma int** ja, dimension,* nnzrow) be easily transplanted to their own definition of numerical computation package. I tested by calculating the efficiency ratio is much higher Fortran write (for example, compared with Sparskit2).
Date : 2025-12-29 Size : 525kb User : 张德柱
[Algorithm] iclass DL : 0: 此程序是用分治算法思想将两个规模为2^k*2^k的矩阵相乘-This procedure is divided algorithm thinking the two size 2 ^ k* 2 ^ k matrix multiply
Date : 2025-12-29 Size : 1kb User : alexandar
[Algorithm] 2 DL : 0: 计算双通道散射BUBBLEGUM v.2 的源代码-The FORTRAN code BUBBLEGUM.f calculates the unitary, analytic T-matrix for a two-channel problem with an arbitrary number of resonances. It also gives the approximate results from the K-matrix method, the Breit-Wigner approximation, or the perturbative method for the same problem. The FORTRAN code EPLOT.f generates a standard eps figure "plot.eps" from the data files generated by BUBBLEGUM.f. It allows one to include and exclude certain data files. The Coherent Bubble Sum Approximation (hence the name "bubblegum") which is used to determine the exact T-matrix is explained in the paper bubblegum.ps (bubblegum.pdf). Three key features of bubblegum, which are absent in most coupled channel codes: ANALYTICITY AND UNITARITY MASS SHIFTS DIRECTLY CORRELATED WITH THE HADRONIC FORM FACTORS EXPANSION FOR THE FORM FACTORS ALLOWS ONE TO FIT THE FORM FACTORS FROM THE DATA
Date : 2025-12-29 Size : 294kb User : liuxc
[Algorithm] single DL : 0: 一般性的奇异值分解算法，float浮点型。-SGGSVD computes the generalized singular value decomposition (GSVD) * of an M-by-N real matrix A and P-by-N real matrix B: * * U*A*Q = D1*( 0 R ), V*B*Q = D2*( 0 R ) * * where U, V and Q are orthogonal matrices, and Z is the transpose * of Z. Let K+L = the effective numerical rank of the matrix (A ,B ) , * then R is a K+L-by-K+L nonsingular upper triangular matrix, D1 and * D2 are M-by-(K+L) and P-by-(K+L) "diagonal" matrices and of the * following structures,
Date : 2025-12-29 Size : 36kb User : muxihan
[Algorithm] r-K DL : 0: 数值分析中R-K迭代算法，用于矩阵的求解问题-RK iterative algorithm in numerical analysis, solving problems for the matrix
Date : 2025-12-29 Size : 1kb User : wangtong
[Algorithm] buaa_shuzhi_diyiti DL : 0: 已知题目501×501的矩阵A，要求求出：①A的最小特征值、最大特征值、绝对值最小的特征值，②A的与数最接近的特征值（k=1，2，…，39），③A的条件数和行列式的值。-Known subject of the matrix 501 × 501 A, requires obtained: ① A minimum eigenvalue, maximum eigenvalue, the smallest absolute eigenvalue, ② A number of the closest with eigenvalue (k = 1,2, ..., 39 ), ③ A condition number and determinant of value.
Date : 2025-12-29 Size : 394kb User : zxy
[Algorithm] satu DL : 0: int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }-int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }
Date : 2025-12-29 Size : 1kb User : cranda
[Algorithm] satu DL : 0: int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }-int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }
Date : 2025-12-29 Size : 1kb User : cranda
[Algorithm] konversi-bilangan DL : 0: int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }-int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }
Date : 2025-12-29 Size : 1kb User : cranda
[Algorithm] satu DL : 0: int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }-int i, j, k,b,n,s,t,r=1 float sum, c float a[12][12] //pengisian matrix printf("Masukkan ordo matriks : ") scanf(" d",&n) printf("masukkan banyak persamaan : ") scanf(" d",&b) for(i=0 i<n i++){ for(j=0 j<b j++){ printf("masukkan nilai matrik baris d kolom d : ",i+1,j+1) scanf(" f",&a[i][j]) }
Date : 2025-12-29 Size : 1kb User : cranda
[Algorithm] sova0 DL : 0: 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 generator matrix in binary form, g(1,:) for feedback, g(2,:) for feedforward L_a: a priori information about the info. bits. Extrinsic info. from the previous component decoder ind_dec: index of the component decoder. =1: component decoder 1 The trellis is terminated to all zero state =2: component decoder 2 The trellis is not perfectly terminated. Output: L_all: log ( P(x=1|y) ) / ( P(x=-1|y) ) Frame size, info. + tail bits- 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 generator matrix in binary form, g(1,:) for feedback, g(2,:) for feedforward L_a: a priori information about the info. bits. Extrinsic info. from the previous component decoder ind_dec: index of the component decoder. =1: component decoder 1 The trellis is terminated to all zero state =2: component decoder 2 The trellis is not perfectly terminated. Output: L_all: log ( P(x=1|y) ) / ( P(x=-1|y) ) Frame size, info. + tail bits
Date : 2025-12-29 Size : 1kb User : yk
[Algorithm] kfilter DL : 0: KFILTER calculates the kalman gain, k, and the stationary covariance matrix,using the Kalman filter.
Date : 2025-12-29 Size : 1kb User : Neda
[Algorithm] gaosiguocheng DL : 0: 高斯过程回归算法中计算K矩阵的简单算法，比较简单-Simple algorithm, Gaussian process regression algorithm to calculate the K matrix is relatively simple
Date : 2025-12-29 Size : 1kb User : 马宏
[Algorithm] haar-noiselets DL : 0: 本代码用内积的方式求两个矩阵的相关性，即K值大小，用haar矩阵和noiselet矩阵式相关性最低，为1，附带英文文档辅助参考-This code with the inner product sum of two matrices, the K value of size, use the haar matrix and noiselet matrix least 1, with English documentation auxiliary reference
Date : 2025-12-29 Size : 101kb User : 柠檬
[Algorithm] k-means DL : 0: Description of k means algorithm, with matrix explanation and code example
Date : 2025-12-29 Size : 146kb User : runaway54
[Algorithm] BCMUL DL : 0: 求m*n阶直矩阵A与n*k阶重短阵B的乘积矩阵C= AB-Find m* n-order linear matrix A and n* k matrix B short-order heavy product matrix C = AB
Date : 2025-12-29 Size : 1kb User : 代持
[Algorithm] BRMUL DL : 0: 求m*n阶矩阵A与n*k阶矩阵B的乘积短阵.-Find m* n order matrix A and n* k matrix B short order matrix multiplication of.
Date : 2025-12-29 Size : 1kb User : 代持
[Algorithm] k DL : 0: 用K均值聚类分析把多组数据分成两类本程序为给定20组数据（用矩阵A表示）分成B、C两组。-K-means clustering analysis of the multiple sets of data into two categories This program is given 20 sets of data (represented by the matrix A) into B, C groups.
Date : 2025-12-29 Size : 2kb User : 王瑶

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