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, r is the upper triangular Schur matrix, and the element on its main diagonal is the characteristic value of x. [U,r]=schur (X) (5) The Hessenberg decomposition of arbitrary square matrices can be decomposed into x=php ', where p is unitary matrix, and the elements under the first diagonal of H are 0, i.e. H is the Hessenberg matrix. [P,h]=hess (X) (6) The eigenvalue decomposition of arbitrary matrices evd any one n-order matrix X can be decomposed into XV=VD, where D is the eigenvalues o

use the command to modify the K valueR1/2/3/4 (config-if) #router Eig 90R1/2/3/4 (config-router) #metric weight 0 0 0 1 0 0Default metric Calculation formula: metric= [10^7/bandwidth (min) + delay (sum)/10] * 256See 3.3.3.3 's routing table on R1D 3.3.3.0 [90/665600] via 14.1.1.4, 00:00:58, FASTETHERNET0/1[90/665600] via 12.1.1.2, 00:00:58, fastethernet0/0Let's figure out how the next 665600 came from.Show Inter f0/0 s1/0 lo0We can see the delay of e

matrix on each base. The larger the eigenvalues, the greater the variance of the matrix on the corresponding eigenvector, the greater the power and the more information. However, eigenvalue decomposition also has a lot of limitations, such as the transformation of the matrix must be a square.In machine learning feature extraction, the meaning is that the maximum eigenvalue corresponding to the direction of the eigenvector contains the most information, if a certain number of characteristics are

The Method for Finding feature values and feature vectors sets a as a matrix of n-order. If the formula is true for the number "" and n-dimensional column vector X, it is called the feature value of matrix, A non-zero vector X is a feature vector corresponding to the feature value. For details, see section 1.3.5 and 1.3.6 feature value decomposition. For example, 1-89 calculate the matrix feature value and feature vector solution:> A = [-2 1 1; 0 2 0;-4 1 3];> [V, d] =

Clc, clearFidbench fopen('txt3.txt ', 'R ');N1 = 6; n2 = 3;A = [];For I = 1: n1Tmp = str2num (fgetl (fid ));A = [a; tmp]; % read criterion layer Judgment MatrixEnd For I = 1: n1Str1 = char (['B', int2str (I), '= [];']);Str2 = char (['B', int2str (I), '= [B', int2str (I), '; tmp];']);Eval (str1 );For j = 1: n2Tmp = str2num (fgetl (fid ));Eval (str2); % judgment matrix of the read solution LayerEndEnd Ri = [0.58, 0.90, 1.12, 1.24, 1.32, 1.41, 1.45,]; % consistency indicator[X, y] =

or N * 1 matrices. Therefore, addition and multiplication operations of vectors are the same as those of matrices. Inner Product Operation: A = [1 + 5I, 2, 3 + 6i, 7-2I]; B = [2-I, 4 + 3I, 3-I, 6]; S = sum (conj (B ). * A) S = A * B '% solve AB Inner Product S = dot (B, A) % solve AB Inner Product 6. Linear Equations % Solution of Linear Equations A = [1, 2, 3; 1, 4, 9; 1 8 27]; B = [5,-2, 6] '; X = inv (a) * B % efficiency not high X = A \ B 7. Similarity simplification and decomposition of M

, then the covariance matrix has only a diagonal value, and since X is independent, the covariance between XM and xn is 0 for m≠n. In addition the covariance matrix is symmetric.Be able to refer to the wiki: (http://zh.wikipedia.org/wiki/%E5%8D%8F%E6%96%B9%E5%B7%AE%E7%9F%A9%E9%98%B5)2. Code implementation pseudocode such as the following (excerpt from Machine learning Combat):' @author: Garvin ' from numpy import *import Matplotlib.pyplot as Pltdef loaddataset (fileName, delim= ' \ t '): FR = Op

([[1, 2], [3, 4]]) print("Inv:") print(inv(lst)) print("T:") print(lst.transpose()) print("Det:") print(det(lst)) print("Eig:") print(eig(lst)) y = np.array([[5], [7]]) print("Solve") print(solve(lst, y))Others#encoding=utf-8import numpy as npdef main(): ## Other print("FFT:") print(np.fft.fft(np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]))) print("Coef

. %--------------------------------------------------------------------------%% calculate sample covariance r= CoV (z '); %1 means dividing by N to calculate covariance %% whitening Z [UNBSP;DNBSP;~]NBSP;=NBSP;SVD (r, ' econ '); % with EIG, [U,d]=eig (R); %% The following whitening matrix t=u*inv (sqrt (D)) *u ';% is called the inverse RMS of the covariance matrix, The INV calculation is not too time consu

This is why, is the coding problem or what, I use is hostgator in the local all normal upload up, is running the webpage is not normal, or permissions problem, I have set is 644, someone used it, Reply to discussion (solution) What's wrong with the error message posted up Warning:include (conn/conn.php) [function.include]: failed to open stream:permission denied in/home/leo/public_html/ Shop/top1.php on line 3Warning:include (conn/conn.php) [fun

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The Debiansid driver is a Chinese star microcamera-the Linux Release Technology-Debian information. The following is a detailed description. Not even a year ago. The gspca driver is the one written by a legendary French veteran who can support hundreds of fake cameras. I saw an article yesterday and tried it, who knows apt-get install this driver will be successfully driven (gspca-modules-2.6.24-1-686, modprobe gspca. the new kernel can also keep up. This camera has poor performance, color disto

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The space uploaded from a website written in windows to a LINUX system is not displayed properly. why is this? is it encoding or something? I use hostgator to upload everything locally normally, that is, the webpage is not running normally, or the permission is incorrect. I set it to 644. has anyone used it, Reply to discussion (solution) Why do I paste the error message? Warning: include (conn/conn. php) [function. include]: failed to open stream:

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Principle of principal component analysis and its Python implementation preface:This article mainly refers to Andrew Ng's machine learning course handout, I translated, and with a Python demo demo to deepen understanding.This paper mainly introduces a dimensionality reduction algorithm, principal component analysis method, Principal components analyses, referred to as PCA, the goal of this method is to find a sub-space of the approximate concentration of data, as to how to find this subspace, th

sampled.3, some basic functions of the arrayZeros (m): M-Order all zeros policyZeros (m,n): m*n-Order all zeros policyEye (m): M-Order Unit matrixMatrix operations:Left except \ ax=b; X=a-1 times the square is multiplied by BRight divide/xa=b;x=b times A-1 Times SquareIn the operation of matrices and constants, constants are usually only used as divisorThe Inverse (ab=ba=e (unit matrix)) of the matrix is obtained, and there are corresponding methods.Inverse can be obtained by the function invTo