In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標(biāo)簽： Rauch-Tung-Striebel algorithm smoother which
上傳時(shí)間： 2016-04-15
上傳用戶：zhenyushaw
該程序?yàn)閙atlab環(huán)境下程序，主要演示“科學(xué)與藝術(shù)奇才”皮克歐沃的國(guó)王映射(King map)，可以生成許多類似三維的復(fù)雜曲面，調(diào)整參數(shù)，或者加上高階攝動(dòng)項(xiàng)，會(huì)得到更多的花樣一個(gè)
上傳時(shí)間： 2014-01-06
上傳用戶：fhzm5658
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
標(biāo)簽： LDPC introduction simulation software
上傳時(shí)間： 2014-01-14
上傳用戶：大融融rr
本程序是一個(gè)行計(jì)算器（即對(duì)表達(dá)式求值）。計(jì)算器能實(shí)現(xiàn)加、減、乘、除、取余（％）和乘方（^）運(yùn)算；能實(shí)現(xiàn)三角函數(shù)（正弦函數(shù)sin和余弦函數(shù)cos），求10為底的對(duì)數(shù)log，求2為底的對(duì)數(shù)ln，求e的指數(shù)冪exp，其參數(shù)也可以是合法的表達(dá)式； 計(jì)算器并能對(duì)表達(dá)式的合法性進(jìn)行測(cè)試，錯(cuò)誤的能給出表達(dá)式錯(cuò)誤的信息。 ] 輸入文件格式：第一行是一個(gè)正整數(shù)N，表示有多少行表達(dá)式。接下來(lái)的N行每一行是 一個(gè)表達(dá)式。表達(dá)式使用由浮點(diǎn)數(shù)（只用小數(shù)點(diǎn)表示）和運(yùn)算符表示。 輸出格式：每個(gè)行輸出一個(gè)表達(dá)式的結(jié)果（浮點(diǎn)輸出結(jié)果使用小數(shù)點(diǎn)表示法表示，）
上傳時(shí)間： 2016-05-27
上傳用戶：aeiouetla
包含附件中的頭文件，就可以在出log時(shí)，將log寫到文件中，方便調(diào)試。
上傳時(shí)間： 2013-12-22
上傳用戶：lmeeworm
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標(biāo)簽： multidimensional estimation algorithm Gaussian
上傳時(shí)間： 2013-12-03
上傳用戶：我們的船長(zhǎng)
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
標(biāo)簽： LDPC introduction simulation software
上傳時(shí)間： 2014-12-05
上傳用戶：change0329
msLinux v1.0 可以在arm ads 下仿真運(yùn)行的linux，對(duì)學(xué)習(xí)linux很有幫助 運(yùn)行于ARMulate時(shí)，請(qǐng)選擇在ARMulate目錄下的map文件
標(biāo)簽： msLinux linux 1.0 arm
上傳時(shí)間： 2014-01-27
上傳用戶：qq521
用VHTL描述7段數(shù)碼管器，輸入為一個(gè)四位二進(jìn)制，在數(shù)碼管上顯示數(shù)字的同時(shí)也顯示這四位二進(jìn)制。使用了port map語(yǔ)句
上傳時(shí)間： 2016-06-17
上傳用戶：ruixue198909
C++STL STL（Standard Template Library，標(biāo)準(zhǔn)模板庫(kù))是惠普實(shí)驗(yàn)室開(kāi)發(fā)的一系列軟件的統(tǒng)稱。它是由Alexander Stepanov、Meng Lee和David R Musser在惠普實(shí)驗(yàn)室工作時(shí)所開(kāi)發(fā)出來(lái)的?，F(xiàn)在雖說(shuō)它主要出現(xiàn)在C++中，但在被引入C++之前該技術(shù)就已經(jīng)存在了很長(zhǎng)的一段時(shí)間。 STL的代碼從廣義上講分為三類：algorithm（算法）、container（容器）和iterator（迭代器），幾乎所有的代碼都采用了模板類和模版函數(shù)的方式，這相比于傳統(tǒng)的由函數(shù)和類組成的庫(kù)來(lái)說(shuō)提供了更好的代碼重用機(jī)會(huì)。在C++標(biāo)準(zhǔn)中，STL被組織為下面的13個(gè)頭文件：<algorithm>、<deque>、<functional>、<iterator>、<vector>、<list>、<map>、<memory>、<numeric>、<queue>、<set>、<stack>和<utility>。以下筆者就簡(jiǎn)單介紹一下STL各個(gè)部分的主要特點(diǎn)。
標(biāo)簽： STL Standard Template Library
上傳時(shí)間： 2016-06-20
上傳用戶：cylnpy
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