兩種解決方案 Richard just finished building his new house. Now the only thing the house misses is a cute little wooden fence. He had no idea how to make a wooden fence, so he decided to order one. Somehow he got his hands on the ACME Fence Catalogue 2002, the ultimate resource on cute little wooden fences. After reading its preface he already knew, what makes a little wooden fence cute. A wooden fence consists of N wooden planks, placed vertically in a row next to each other. A fence looks cute if and only if the following conditions are met: ?The planks have different lengths, namely 1, 2, . . . , N plank length units. ?Each plank with two neighbors is either larger than each of its neighbors or smaller than each of them. (Note that this makes the top of the fence alternately rise and fall.)
標簽： house the finished building
上傳時間： 2014-01-26
上傳用戶：541657925
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
標簽： CENTRES KMEANS OPTIONS cluster
上傳時間： 2014-01-07
上傳用戶：zhouli
ofdm信道特性 Channel transmission simulator Channel transmission simulator ％ ％ inputs: ％ sig2 - noise variance ％ Mt - number of Tx antennas ％ Mr - number of Rx antennas ％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column ％ is the value of the antenna outputs at a single time instance) ％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission ％ N - number of symbols transmitted in OFDM frame ％ ％ outputs: ％ y - vector of channel outputs (matrix for MIMO again, just like x matrix) ％ create noise vector sequence (each row is a different antenna, each column is a ％ different time index) note: noise is spatially and temporally white
標簽： transmission simulator Channel inputs
上傳時間： 2016-07-22
上傳用戶：kelimu
三角形基本上就是在解nCr ，因為三角形上的每一個數字各對應一個nCr ，其中n為row，而r為colnmu
上傳時間： 2016-07-30
上傳用戶：dave520l
（1） 設計和編寫代表矩陣的Matrix類。該類包括矩陣行列數變量int rows和int cols，矩陣數據數組double data[][]，構造方法Matrix()、Matrix(int rows,int cols)、Matrix(int rows,int cols,double data[][])，獲取某元素值的方法getData(int row,int col)，設置某元素值的方法setData(int row,int col,double value)，計算兩個矩陣的乘積的方法multiply(Matrix m)以及toString()等內容。
上傳時間： 2016-08-19
上傳用戶：qiao8960
The goal of SPID is to provide the user with tools capable to simulate, preprocess, process and classify in vivo and ex vivo MRS signals. These tools are embedded in a matlab graphical user interface (GUI). (Pre)processing and classification methods can also be automatically run in a row using the matlab command line
標簽： preprocess simulate capable provide
上傳時間： 2014-11-29
上傳用戶：chenbhdt
The program constructs girth-twelve column-weight QC-LPDC codes. The rate can be changed by changing k(row-weight), size is changed by varying m(sub-matrix size).
標簽： column-weight girth-twelve constructs The
上傳時間： 2016-11-01
上傳用戶：erkuizhang
動態添加表格，可實現添加N多row,很實用，也很好用的。
上傳時間： 2014-01-13
上傳用戶：李彥東
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
標簽： back-propagation corresponding input-output algorithm
上傳時間： 2016-12-27
上傳用戶：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
