構造哈夫曼樹 哈弗曼樹中沒有度為一的節(jié)點，是標準的二叉樹，所以有n個葉子結點時，需要一個長度為2n-1的一維數(shù)組存儲哈弗曼樹的結點。 (1)、n個葉子節(jié)點只有weight權值，處理非葉子節(jié)點，從ht[i]（ht[1]~ht[n-1]）中找到ht[i].weight最小的兩個節(jié)點ht[s1]和ht[s2]，這就是Select(int n,int &s1,int & s2,HTNode *ht)函數(shù)完成的功能。 (2)、調(diào)用select函數(shù)，并將ht[s1]和ht[s2]作為ht[l]的左右子樹，即ht[s1]和ht[s2]雙親節(jié)點為ht[l]，新的根節(jié)點的權值為其左右子樹權值之和， ht[l].weight=ht[s1].weight+ht[s2].weight
上傳時間： 2016-06-13
上傳用戶：ztj182002
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
學上的基本神經(jīng)元，人工的神經(jīng)網(wǎng)絡也有基本的神經(jīng)元。每個神經(jīng)元有特定數(shù)量的輸入，也會為每個神經(jīng)元設定權重（weight）。權重是對所輸入的資料的重要性的一個指標。然后，神經(jīng)元會計算出權重合計值（net value），而權重合計值就是將所有輸入乘以它們的權重的合計。每個神經(jīng)元都有它們各自的臨界值（threshold），而當權重合計值大于臨
標簽：
上傳時間： 2014-06-06
上傳用戶：luke5347
j2me設計的界面包，很漂亮實用。 light weight UI toolkit
標簽： j2me
上傳時間： 2013-12-21
上傳用戶：kristycreasy
* The keyboard is assumed to be a matrix having 4 rows by 6 columns. However, this code works for any * matrix arrangements up to an 8 x 8 matrix. By using from one to three of the column inputs, the driver * can support "SHIFT" keys. These keys are: SHIFT1, SHIFT2 and SHIFT3.
標簽： keyboard However assumed columns
上傳時間： 2016-11-14
上傳用戶：ardager
編寫一個Java程序，設計一個運輸工具類Transport，包含的成員屬性有：速度pace、載重量load；汽車類Vehicle是Transport的子類，其中包含的屬性有：車輪的個數(shù)wheels和車重weight；飛機Airplane類是Transport的子類其中包含的屬性有：機型enginertype和發(fā)動機數(shù)量enginers。每個類都有相關所有數(shù)據(jù)的輸出方法。
上傳時間： 2016-11-16
上傳用戶：miaochun888
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2014-12-03
上傳用戶：maizezhen
％ 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
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
This software is a Matlab implementation of restricted sampling from Gaussian distribution, and sample x (column vector) from N(x_mu, x_var), restricted in x_min<=x<=x_max.
標簽： implementation distribution restricted Gaussian
上傳時間： 2016-12-30
上傳用戶：6546544
