<li id="csegk"></li> <button id="csegk"></button> ％求輸入一維信號的計盒分形維數 ％y是一維信號 ％cellmax:方格子的最大邊長,可以取2的偶數次冪次(1,2,4,8...),取大于數據長度的偶數 ％D是y的計盒維數（一般情況下D>=1）,D=lim(LOG(N(e))/LOG(k/e)),
上傳時間： 2013-12-13
上傳用戶：671145514
一個實現程序日志功能的com組件，在程序中直接調用即可實現操作或數據的LOG文件寫入操作，大小組件自動維護
上傳時間： 2014-08-31
上傳用戶：jackgao
3、使用如下命令更改密碼： shell> mysqladmin -u root -p password ‘newpass’ Enter Password:******* 出現Enter Password的提示后輸入原來的密碼oldpass即可。 讀者可以嘗試其它所有本章介紹的方法。 4、首先以root用戶的身份連接到服務器： shell> mysql -u root -p Enter password:******* 出現Enter password提后輸入root用戶的密碼，然后即進入mysql客戶機的交互模式，可以看到下面的提示： Welcome to the MySQL monitor. Commands end with or \g. Your MySQL connection id is 4 to server version: 3.23.25-beta-LOG Type help or \h for help. Type \c to clear the buffer mysql> 然后發布查詢，直接鍵入題目中的語句： mysql> SELECT User,Host FROM mysql.user
標簽： Enter mysqladmin Password password
上傳時間： 2016-03-17
上傳用戶：talenthn
基于DSP平臺實現turbo碼Max_LOG_MAP算法，基于標準C 語言研究 了Turbo 碼Max - LOG - MAP 譯碼算法的軟件編程與實現,為了提高程序的運行效率,結合TMS320C6000 系列DSP 芯片 的結構與特點采用循環展開、數據的存取優化設計、算法改進等措施進行了代碼優化,給出了測試結果，非常值得一看
標簽： Max_LOG_MAP turbo DSP 算法
上傳時間： 2014-01-11
上傳用戶：
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.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
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).
標簽： LDPC introduction simulation software
上傳時間： 2014-01-14
上傳用戶：大融融rr
本程序是一個行計算器（即對表達式求值）。計算器能實現加、減、乘、除、取余（％）和乘方（^）運算；能實現三角函數（正弦函數sin和余弦函數cos），求10為底的對數LOG，求2為底的對數ln，求e的指數冪exp，其參數也可以是合法的表達式； 計算器并能對表達式的合法性進行測試，錯誤的能給出表達式錯誤的信息。 ] 輸入文件格式：第一行是一個正整數N，表示有多少行表達式。接下來的N行每一行是 一個表達式。表達式使用由浮點數（只用小數點表示）和運算符表示。 輸出格式：每個行輸出一個表達式的結果（浮點輸出結果使用小數點表示法表示，）
上傳時間： 2016-05-27
上傳用戶：aeiouetla
包含附件中的頭文件，就可以在出LOG時，將LOG寫到文件中，方便調試。
上傳時間： 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 ％
標簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
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).
標簽： LDPC introduction simulation software
上傳時間： 2014-12-05
上傳用戶：change0329
