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[Other] eigen_power DL : 0: 强调正确性、可移植性和可维护性的基础上，对C语言的具体细节、运行库以及C语言编程风格做了完整、准确的描述。本书涵盖了传统C语言、C89、C95、C99等所有C语言版本的实现，同时讨论了C++与C语言.. -stressed correctness, portability and maintainability, based on the C language specific details of the runtime and C language programming style to do a complete and accurate description. The book covers the traditional language C, C89, High, 12-23 all C language version of the achievement, also discussed the C and C language ..
Update : 2025-04-07 Size : 2kb Publisher : 万宏兴
[Crack Hack] eigamal DL : 0: C++写的椭圆曲线加密算法库源码椭圆曲线加密算法，解密一步的源代码-C++ Written in elliptic curve encryption algorithm library source elliptic curve encryption algorithm, decryption step source code
Update : 2025-04-07 Size : 3kb Publisher : mwb
[CA auth] RSA DL : 0: 这个vc++代码实现了RSA，Elamal，弗吉尼亚加密算法。-This vc++ Code realize the RSA, Elamal, Virginia encryption algorithm.
Update : 2025-04-07 Size : 93kb Publisher : 山东省
[CSharp] eig DL : 0: 求特征值和特征向量的源代码请大家指教用c++编译-Eigenvalues and eigenvectors of seeking the source code, please teach us to use c++ compiler
Update : 2025-04-07 Size : 19kb Publisher : iamhere
[Algorithm] eig DL : 0: 矩阵特征值分解的C++代码，编译形成一个动态链接库，供其它地方调用，可以计算矩阵的特征值与特征向量-Matrix eigenvalue decomposition of C++ of code, compile the formation of a dynamic link library for other places to call, we can calculate the matrix of eigenvalues and eigenvectors
Update : 2025-04-07 Size : 3.28mb Publisher : HalfLegend
[2D Graphic] PcA DL : 0: C语言源程序，PCA人脸识别算法，主要是eig特征分量选取的源程序和欧式空间匹配程序、特征脸提取程序-C language source code, PCA face recognition algorithm, eig characteristic component selected source and match the European space program, eigenface extraction procedures
Update : 2025-04-07 Size : 5kb Publisher : 君君
[matlab] music(doa) DL : 0: 七单元天线阵MUSIC DOA估计： d=1 ，天线阵元的间距； lma=2，信号中心波长；四输入信号； A=[A1,A2,A3,A4]，得出A矩；四信号的频率d=[1.3*cos(v1*n) 1*sin(v2*n) 1*sin(v3*n) 1*sin(v4*n)] 构造输入信号矢量 U=A*d 总的输入信号总输入信号的协方差矩阵 [s,h]=eig(c) 求协方差的特征矢量及特征值取出与零特征值对应的特征矢量求协方差矩阵的逆矩阵应用Music法估计输出绘出各波达方向图-Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* ⁢ sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure
Update : 2025-04-07 Size : 1kb Publisher : xiang
[Data structs] MAT DL : 0: 仿MATLAB矩阵C++运算库,包括加、减、乘、除、点加、点减、点乘、点除、赋值、转置、rank、det、eig、svd、pinv、power等的运算。inv运算使用pinv运算。最难实现的是非方阵的除法。-MatLab Matrix simulator
Update : 2025-04-07 Size : 23kb Publisher : maguangzhi
[Other] matrixl(eig) DL : 0: 求解矩阵的特征值和特征向量的C++源代码，经过测试的，下载编译即可使用- Solving the eigenvalues and eigenvectors of C++ source code, tested, and download the compiler can use
Update : 2025-04-07 Size : 1.05mb Publisher : 周高伟
[Algorithm] Zero DL : 0: Fast Numerical Computational C++ lib: Including the following classes: class Complex • class ComplexVector • class ComplexMatrix • class RealVector • class RealMatrix • class Kronecker • class Gauss_Jordan • class magic • class lu • class eig • class svd • class chol • class qr • class norm • class pinv • class Rand • class solution • class fit • class polyfit • class integration • class ode • class interp • class stats-Fast Numerical Computational C++ lib: Including the following classes: class Complex • class ComplexVector • class ComplexMatrix • class RealVector • class RealMatrix • class Kronecker • class Gauss_Jordan • class magic • class lu • class eig • class svd • class chol • class qr • class norm • class pinv • class Rand • class solution • class fit • class polyfit • class integration • class ode • class interp • class stats
Update : 2025-04-07 Size : 848kb Publisher : 章隆
[Windows Develop] 附件 DL : 0: lc; clear; A=[1 1.2 1.5 1.5; 0.833 1 1.2 1.2; 0.667 0.833 1 1.2; 0.667 0.833 0.833 1]; %因素对比矩阵A，只需要改变矩阵A [m,n]=size(A); %获取指标个数 RI=[0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51]; R=rank(A); %求判断矩阵的秩 [V,D]=eig(A); %求判断矩阵的特征值和特征向量，V特征值，D特征向量； tz=max(D); B=max(tz); %最大特征值 [row, col]=find(D==B); %最大特征值所在位置 C=V(:,col); %对应特征向量 CI=(B-n)/(n-1); %计算一致性检验指标CI CR=CI/RI(1,n); if CR<0.10 disp('CI=');disp(CI); disp('CR=');disp(CR); disp('对比矩阵A通过一致性检验，各向量权重向量Q为：'); Q=zeros(n,1); for i=1:n Q(i,1)=C(i,1)/sum(C(:,1)); %特征向量标准化 end(lc; clear; A=[1 1.2 1.5 1.5; 0.833 1 1.2 1.2; 0.667 0.833 1 1.2; 0.667 0.833 0.833 1];)
Update : 2025-04-07 Size : 68kb Publisher : 嘻嘻13