代码搜索：decomposition

找到约 1,689 项符合「decomposition」的源代码

代码结果 1,689
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m lpdec.m

function [c, d] = lpdec(x, h, g, opt, mode) % LPDEC Pyramid Decomposition % % [c, d] = lpdec(x, h, g, opt, mode) % % Input: % x: input image % h, g: two one or two-dimesional filters, dep
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ldpc-

Description LDPC codes BER simulation under AWGN channel. MacKay-Neal based LDPC matrix. Message encoding uses sparse LU decomposition. There are 4 choices of decoder: hard-decision/bit-flip decoder,
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bas module1.bas

Attribute VB_Name = "triangular_decomposition" Option Explicit Public Function triangular(n As Integer, a() As Double, b() As Double, x() As Double) '三角分解法 Dim i, j, k, q, p As Integer Dim U()
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m lpdemo.m

% LPDEMO % Demo of the Laplacian pyramid functions x = imread('cameraman.tif'); x = double(x)/256; % Laplacian decomposition using 9/7 filters and 5 levels pfilt = '9/7'; n = 5; y = lpd(x, '9/7', n)
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m~ oaoksk.m~

function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb) % OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK. % % [model] = oaoksk( data, labels, ker, arg, C, stop, t
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m oaoksk.m

function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb) % OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK. % % [model] = oaoksk( data, labels, ker, arg, C, stop, t
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cpp evalue.cpp

//$$evalue.cpp eigen-value decomposition // Copyright (C) 1991,2,3,4: R B Davies #define WANT_MATH #include "include.h" #include "newmatap.h" #include "newmatrm.h"
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m~ oaoksk.m~

function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb) % OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK. % % [model] = oaoksk( data, labels, ker, arg, C, stop, t
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www.eeworm.com/read/421949/10676928

m oaoksk.m

function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb) % OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK. % % [model] = oaoksk( data, labels, ker, arg, C, stop, t
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www.eeworm.com/read/255967/7098217

m bivariate_emd_principle.m

%bivariate_EMD_principle.m %shows principle of the bivariate EMD extension %reproduces Fig. 1 in "Bivariate Empirical Mode Decomposition", G. Rilling, %P. Flandrin, P. Goncalves and J. M. Lilly, IEEE
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