代码搜索：Matrices

找到约 3,616 项符合「Matrices」的源代码

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m plot_matrix.m

function plot_matrix(G, bw) % PLOT_MATRIX Plot a 2D matrix as a grayscale image, and label the axes % % plot_matrix(M) % % For 0/1 matrices (eg. adjacency matrices), use % plot_matrix(M,1) if
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www.eeworm.com/read/466801/7020896

m dftuv.m

function [U, V] = dftuv(M, N) %DFTUV Computes meshgrid frequency matrices. % [U, V] = DFTUV(M, N) computes meshgrid frequency matrices U and % V. U and V are useful for computing frequency-dom
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www.eeworm.com/read/448535/7531482

m diagstack.m

function D = diagstack(X,Y) % Stack matrices diagonally: % D = [X 0 % 0 Y]; % % function D = diagstack(X,Y) % X, Y = input matrices % % D = diagonal stack % Copyright 1999 by Todd K. M
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www.eeworm.com/read/443653/7629388

m prepare_housing.m

data = load('housing.data'); % make X and y matrices [n,d] = size(data); X = data(:, 1:d-1); y = data(:,d); % standardize feature values and center target mu_y = mean(y); y = y - mu_y; [X, mu, s
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www.eeworm.com/read/441325/7671869

m t_jacobian.m

function t_jacobian(quiet) %T_JACOBIAN Numerical tests of partial derivative code. % MATPOWER % $Id: t_jacobian.m,v 1.2 2004/08/23 20:59:46 ray Exp $ % by Ray Zimmerman, PSERC Cornell % Copy
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www.eeworm.com/read/437794/7741505

asv bartwo.asv

clear;clc; % Defination % Link properties m1=1;m2=1; a1=1;a2=1; % Coordinate frames i1=[1;0;0]; j1=[0;1;0]; e1=[0;0;1];e2=[0;0;1]; Izz1=1/12*m1*a1^2;Izz2=1/12*m2*a2^2; % trajectory pi
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www.eeworm.com/read/437794/7741506

m bartwo.m

clear;clc; % Defination % Link properties m1=1;m2=1; a1=1;a2=1; % Coordinate frames i1=[1;0;0]; j1=[0;1;0]; e1=[0;0;1];e2=[0;0;1]; Izz1=1/12*m1*a1^2;Izz2=1/12*m2*a2^2; % trajectory pi
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www.eeworm.com/read/298590/7950375

m t_jacobian.m

function t_jacobian(quiet) %T_JACOBIAN Numerical tests of partial derivative code. % MATPOWER % $Id: t_jacobian.m,v 1.3 2005/07/08 18:58:38 ray Exp $ % by Ray Zimmerman, PSERC Cornell % Copy
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www.eeworm.com/read/196836/8055276

version

What's new in version 3.x: 1. You can initialise the state transition matrices (hmm.P) before you do hmmtrain. 2. A demonstration, demar, showing an HMM with an AR observation model applied to a sec
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www.eeworm.com/read/397102/8068295

m ldc.m

%LDC Linear Discriminant Classifier % % W = ldc(A,r,s) % % Computation of a linear discriminant between the classes of the % dataset A assuming normal densities with equal covariance % matrices.
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