构造哈夫曼树 哈弗曼树中没有度为一的节点，是标准的二叉树，所以有n个叶子结点时，需要一个长度为2n-1的一维数组存储哈弗曼树的结点。 (1)、n个叶子节点只有weight权值，处理非叶子节点，从ht[i]（ht[1]~ht[n-1]）中找到ht[i].weight最小的两个节点ht[s1]和ht[s2]，这就是Select(int n,int &s1,int & s2,HTNode *ht)函数完成的功能。 (2)、调用select函数，并将ht[s1]和ht[s2]作为ht[l]的左右子树，即ht[s1]和ht[s2]双亲节点为ht[l]，新的根节点的权值为其左右子树权值之和， ht[l].weight=ht[s1].weight+ht[s2].weight
上传时间： 2016-06-13
上传用户：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
标签： transmission simulator Channel inputs
上传时间： 2016-07-22
上传用户：kelimu
学上的基本神经元，人工的神经网络也有基本的神经元。每个神经元有特定数量的输入，也会为每个神经元设定权重（weight）。权重是对所输入的资料的重要性的一个指标。然后，神经元会计算出权重合计值（net value），而权重合计值就是将所有输入乘以它们的权重的合计。每个神经元都有它们各自的临界值（threshold），而当权重合计值大于临
标签：
上传时间： 2014-06-06
上传用户：luke5347
j2me设计的界面包，很漂亮实用。 light weight UI toolkit
标签： j2me
上传时间： 2013-12-21
上传用户：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.
标签： keyboard However assumed columns
上传时间： 2016-11-14
上传用户：ardager
编写一个Java程序，设计一个运输工具类Transport，包含的成员属性有：速度pace、载重量load；汽车类Vehicle是Transport的子类，其中包含的属性有：车轮的个数wheels和车重weight；飞机Airplane类是Transport的子类其中包含的属性有：机型enginertype和发动机数量enginers。每个类都有相关所有数据的输出方法。
上传时间： 2016-11-16
上传用户：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. ％
标签： generalization calculates prediction function
上传时间： 2014-12-03
上传用户：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.
标签： Levenberg-Marquardt desired network neural
上传时间： 2016-12-27
上传用户：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. ％
标签： generalization calculates prediction function
上传时间： 2016-12-27
上传用户：脚趾头
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.
标签： implementation distribution restricted Gaussian
上传时间： 2016-12-30
上传用户：6546544
