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  • NET WORK

    2015-04-25 23:40:14
    中继器:还原衰弱的信号

    中继器:还原衰弱的信号

    展开全文
  • net work安装

    2017-01-22 15:17:33
    NetworkX是一个用Python语言开发的图论与复杂网络建模工具,内置了常用的图与复杂网络分析算法,可以方便的进行复杂网络数据分析、仿真建模等工作。...  结合本人的使用经验,希望做到知其然而知其所以然的使用,...

     NetworkX是一个用Python语言开发的图论与复杂网络建模工具,内置了常用的图与复杂网络分析算法,可以方便的进行复杂网络数据分析、仿真建模等工作。网络上有阎老师的相关博客教程,在此表示敬意,点击进入

            结合本人的使用经验,希望做到知其然而知其所以然的使用,提供相对傻瓜和全面的使用经验,也是自己的一笔知识财富积累。

           1.环境的安装

                     首先要安装python解释器,当然这是废话,但是安装的版本确有将就。先介绍几个部件:python解释器、networkx类库、还有使用支持networkx绘图的numpy和matplotlib,总共四个。

                     由于目前matplotlib只支持到python2.6解释器,所以还是下载python 2.6和各种类库2.6版本比较好。(更正:目前支持到了2.7版本

                     下载链接:python 2.6  (一路点击即可)networkx-1.6-py2.6.egg  (等会儿介绍安装介个)numpy(一路)  matpotlib(win32-py2.6版本,一路next)

                     对于networkx第三方类库的安装,需要弄点东东。

                    由于本人是win7系统,右键“以管理员身份运行”cmd,然后先安装ez_setup.py,下载完毕,进行安装。

                     

                     然后进行networkx的egg库的安装(关于egg库的安装详细解释,点击进入)

                     

                     安装完毕,进行测试。

                     打开python-command line,进行如图输入“import networkx as nx”+"print nx",就有相关显示,如果没有成功,会提示不认识networkx类库的

    展开全文
  • <p>Thanks for opening sourcing this amazing work. <p>While trying to follow the installation, I came across a few issues and tried to address them in four different commits. <ol><li>I was facing ...
  • t determine if this is something that is supported or should work. I don't have a specific tie to a version of python or pythonnnet. At the moment I'm using python 3.6.7 and just updated to ...
  • <div><p>I could not find any trace of someone who succeeded / failed with using ser2net under Windows Subsystem Linux? Is is doable?</p><p>该提问来源于开源项目:cminyard/ser2net</p></div>
  • <div><p>ok i placed fishing net over water dug out another 3+ blocks under the net and placed a small crate over the net. Am i doing something wrong here?</p><p>该提问来源于开源项目:...
  • ![图片说明](https://img-ask.csdn.net/upload/201509/15/1442280011_780849.png) 怎么解决
  • GOOGLE:NET WORK CAMERA

    千次阅读 2006-02-20 16:51:00
    要在google的搜索框内输入以下代码,就可以搜索到全球数以万计的摄像头直播。inurl:"ViewerFrame?Mode=" 相关报道: 近日,各博客和信息论坛上议论纷纷。因为有人发现只要在Google中进行简单的搜索就可以连入世界...

    要在google的搜索框内输入以下代码,就可以搜索到全球数以万计的摄像头直播。
    inurl:"ViewerFrame?Mode="
     
    相关报道:

      近日,各博客和信息论坛上议论纷纷。因为有人发现只要在Google中进行简单的搜索就可以连入世界上超过1000个没有连接限制措施的监控摄像头。显然,摄像头的拥有者对此并不知情。

      键入某个特定字段便可搜索到这些具有网页界面的网络摄像头。通过这些摄像头,其拥有者即可通过网络进行远程观察,甚至可以通过电脑控制摄像头进行变焦等操作。这些摄像头,分布在全球各个角落。

      这条消息开始是在一个网络社区上张贴出来的,其后很快在周三和周四被转载到热门博
    客论坛BoingBoing上。

      其中一条查找字段,可以在Google上找出超过1500个有网络接入功能的监控摄像头。其中1000个由瑞典公司Axis Communications制造,500个由Panasonic生产。

      根据两家公司的网站上的说明,摄像头的网络接入都提供了密码保护功能。Axis的摄像头更可以限制只能由特定的IP进行接入。但还不知道这些保护功能在产品出厂时是否默认为打开。在Panasonic网站的FAQ页面中,公司声明:“我们并没有收到客户对摄像头提供的安全保护的任何相关投诉。”

    搜索:
    http://www.google.com/search?q=inurl:%22ViewerFrame%3FMode ... =&c2coff=1&start=0&sa=N

    小日本的洗衣店
    http://sueyoshi.aa0.netvolante.jp/Vi ... =Motion&Language=1

    http://24.39.100.161:82/ViewerFrame?Mode=Motion&Language=1
    一个小日本的餐馆
    http://improve.plala.jp/ViewerFrame?Mode=Motion&Resolution=640x480&Quali ... Size=STD&PresetOperation=Move&Language=1

    http://miyakonojyo.aa1.netvolante.jp/Mul ... de=Motion&Language=1

    http://ky-asuka.ddo.jp:3030/Viewe ... otion&Language=1

    一个监控室:
    http://candidcamera.ecasd.k12.wi.us/ViewerFrame?Mode=Motion


    经典的学校(计算机/办公室/打印室)
    http://163.136.136.17:8181/MultiCameraFrame?Mode=Motion

    栗林山庄:
    http://211.7.252.127:83/ViewerFrame?
    Mode=Motion&Resolution=640x480&Quality=Standard&Interval=30&Size=STD&PresetOperation=Move&Language=1


    可能需要JAVA支持 网络好象是IPV6的哦。。
    http://ioc.jpn.ph:81/CgiStart?page=Multi&Language=1

    十字路口:
    http://219.117.243.105:8080/MultiCam ... Motion&Language=1

    在搜索框上输入: “index of/ ”  inurl:lib

    再按搜索你将进入许多图书馆,并且一定能下载自己喜欢的书籍。


    在搜索框上输入: “index of /”  cnki

    再按搜索你就可以找到许多图书馆的CNKI、VIP、超星等入口!


    在搜索框上输入: “index of /”  ppt

    再按搜索你就可以突破网站入口下载powerpint作品!


    在搜索框上输入: “index of /”  mp3

    再按搜索你就可以突破网站入口下载mp3、rm等影视作品!


    在搜索框上输入: “index of /”  swf

    再按搜索你就可以突破网站入口下载flash作品!


    在搜索框上输入: “index of /”  要下载的软件名

    再按搜索你就可以突破网站入口下载软件!


    注意引号应是英文的!


    再透露一下,如果你输入:


    “index of /”  AVI

    你会找到什么呢?同理,把AVI换为MPEG看看又会找到什么呢?呵呵!接下来不用我再教了吧?
    近日消息,上周的网络论坛炸开了锅,原因是Google搜索能够使用户访问全球1000多个没有安全保护措施的监视摄像机,可在摄像机主人不知情下进行。

    搜索URL中一些字符串就能够发现连接到互联网上的摄像机,看到原本只有摄像头主人才能够看到的内容,甚至能够指令摄像头改变角度,更适合观看。视频冲浪者已经利用这一方法对许多办公室和家庭进行了“偷窥”。

    能够利用Google搜索“偷窥”的消息最先出现在一个论坛上,后来在上周三、上周四传播到了读者很多的BoingBoing网络日记系统中。过去,想通过监视用摄像机“偷窥”的人必须来到接收装置附近,利用特制天线获取来自无线摄像机的信号。

    在网上传播的一个Google搜索字符串能够发现近1000部Axis公司生产的摄像机,另一个搜索字符串能够发现约500部由松下生产的摄像机。据二家公司的网站称,它们的网络摄像机都提供有密码保护功能,Axis还采取了只能从经过批准的IP地址访问 网络摄像机的技术。松下网站上的资料称,它的网络摄像机可能不适合在“机密应用”中使用。

    可以在google查找
    intitle:"Live View / - AXIS" | inurl:view/view.shtml
    inurl:indexFrame.shtml Axis
    intitle:"Live View / - AXIS"
    intext:"MOBOTIX M1" intext:"Open Menu"
    inurl:"ViewerFrame?Mode="
    intitle:"WJ-NT104 Main Page"
    intitle:snc-rz30 inurl:home/
    展开全文
  • 1、Recursive Nerual Networks能够更好地体现每个词与词之间语法上的联系这里我们选取的损失函数仍然是交叉熵函数 2、整个网络的结构如下图所示: 每个参数的更新时的梯队值如何计算,稍后再给大家计算相应的数学...
     
    

    1、Recursive Nerual Networks能够更好地体现每个词与词之间语法上的联系
    这里我们选取的损失函数仍然是交叉熵函数

    2、整个网络的结构如下图所示:

    每个参数的更新时的梯队值如何计算,稍后再给大家计算相应的数学公式

    这里先列出节点的合并规则

     

     

    1、即假设将一句话中的词先两个合并,并通过神经网络计算出合并后的得分情况

    2、然后找出合并后得分最高的两个词进行真正的合并,得到新的节点,其余节点不合并

    3、将得到的新节点加入到下一轮两两合并的计算过程中,直至得到最终节点

    下面是计算的代码:

    '''
    Created on 2017年10月5日
    
    @author: weizhen
    '''
    # 一个简单的递归神经网络的实现,有着一个ReLU层和一个softmax层
    # TODO : 必须要更新前向和后向传递函数
    # 你可以通过执行 python rnn.py 方法来执行一个梯度检验
    # 插入pdb.set_trace()  在你不确定将会发生什么的地方
    
    import numpy as np
    import collections
    import pdb
    import tree as treeM
    import pickle
    
    class RNN:
        
        def __init__(self, wvecDim, outputDim, numWords, mbSize=30, rho=1e-4):
            self.wvecDim = wvecDim
            self.outputDim = outputDim
            self.numWords = numWords
            self.mbSize = mbSize
            self.defaultVec = lambda : np.zeros((wvecDim,))
            self.rho = rho
        
        def initParams(self):
            np.random.seed(12341)
            
            # Word vectors
            self.L = 0.01 * np.random.randn(self.wvecDim, self.numWords)
            
            # Hidden layer parameters
            self.W = 0.01 * np.random.randn(self.wvecDim, 2 * self.wvecDim)
            self.b = np.zeros((self.wvecDim))
            
            # Softmax weights
            # note this is " U "in the notes and the handout...
            # there is a reason for the change in notation
            self.Ws = 0.01 * np.random.randn(self.outputDim, self.wvecDim)
            self.bs = np.zeros((self.outputDim))
            
            self.stack = [self.L, self.W, self.b, self.Ws, self.bs]
            
            # Gradients
            self.dW = np.empty(self.W.shape)
            self.db = np.empty((self.wvecDim))
            self.dWs = np.empty(self.Ws.shape)
            self.dbs = np.empty((self.outputDim))
            
        def costAndGrad(self, mbdata, test=False):
            """
                        每一个datum在minibatch里边都是一个树
                        前向计算每一个树,反向传播到每一个树
                        返回值:
                cost:
                                        梯度:w.r.t W,Ws,b,bs
                                        以上变量的梯度都是在稀疏形式存储的
                                        或者是以测试状态下的
                Returns:
                    cost,correctArray,guessArray,total
            """
            cost = 0.0
            correct = []
            guess = []
            total = 0.0
            
            self.L, self.W, self.b, self.Ws, self.bs = self.stack
            # 初始化所有梯度都是0
            self.dW[:] = 0
            self.db[:] = 0
            self.dWs[:] = 0
            self.dbs[:] = 0
            self.dL = collections.defaultdict(self.defaultVec)
            
            # 在每一个batch中前向计算每一个tree
            for tree in mbdata:
                c, tot = self.forwardProp(tree.root, correct, guess)
                cost += c
                total += tot
            if test:
                return (1. / len(mbdata)) * cost, correct, guess, total
            
            # 在每一个batch上进行反向传播
            for tree in mbdata:
                self.backProp(tree.root)
            
            # 通过mb的大小来计算损失和梯度
            scale = (1. / self.mbSize)
            for v in self.dL.values():
                v *= scale
            
            # 添加L2正则化项
            cost += (self.rho / 2) * np.sum(self.W ** 2)
            cost += (self.rho / 2) * np.sum(self.Ws ** 2)
            
            return scale * cost, [self.dL, scale * (self.dW + self.rho * self.W), scale * self.db, scale * (self.dWs + self.rho * self.Ws), scale * self.dbs]
        
        def forwardProp(self, node, correct=[], guess=[]):
            """损失应该是一个不断更新的变量,总损失是我们需要用在准确率报告里边的数据"""
            cost = total = 0.0
            # 下面实现递归神经网络前向传播的函数
            # 你应该更新 node.probs, node.hActsl,node.fprop,and cost
            # node :你当前节点是在语法树上的
            # correct : 这是一个不断更新的标记真值的列表
            # guess: 这是一个不断更新的猜测我们的模型会预测为哪一个结果的列表
            #       (我们会同时使用正确的和猜测的值来构造我们的混淆矩阵)
            L = self.L
            # 隐藏层的参数
            W = self.W
            b = self.b
            
            # Softmax 权重
            Ws = self.Ws
            bs = self.bs
            
            if node.isLeaf:
                node.hActsl = L[:, node.word]
            else:
                if not node.left.fprop:
                    cost_left, total_left = self.forwardProp(node.left, correct, guess)
                    cost += cost_left
                    total += total_left
                if not node.right.fprop:
                    cost_right, total_right = self.forwardProp(node.right, correct, guess)
                    cost += cost_right
                    total += total_right
                
                node.hActsl = W.dot(np.hstack((node.left.hActsl, node.right.hActsl))) + b
                node.hActsl[node.hActsl < 0] = 0
            
            x = Ws.dot(node.hActsl) + bs
            x -= np.max(x)
            node.probs = np.exp(x) / np.sum(np.exp(x))
            
            correct += [node.label]
            guess += [np.argmax(node.probs)]
            
            cost -= np.log(node.probs[node.label])
            
            node.fprop = True
            
            return cost, total + 1
        
        def backProp(self, node, error=None):
            """
                        实现递归神经网络的反向传播函数
                        应该更新 self.dWs, self.dbs, self.dW, self.db, and self.dL[node.word] 相关地
            node:你在语法树种的当前节点
            error:误差从之前一个迭代过程中传递进来的
            """
            # 清空节点
            node.fprop = False
            
            L = self.L
            # 隐藏节点的参数
            W = self.W
            b = self.b
            
            # Softmax层的权重
            Ws = self.Ws
            bs = self.bs
            
            error_this = node.probs
            error_this[node.label] -= 1.0
            delta = Ws.T.dot(error_this)
            
            self.dWs += np.outer(error_this, node.hActsl)
            self.dbs += error_this
            
            if error is not None:
                delta += error
            
            delta[node.hActsl == 0] = 0
            
            if node.isLeaf:
                self.dL[node.word] += delta
            else:
                self.dW += np.outer(delta, np.hstack([node.left.hActsl, node.right.hActsl]))
                self.db += delta
                
                delta = np.dot(self.W.T, delta)
                self.backProp(node.left, delta[:self.wvecDim])
                self.backProp(node.right, delta[self.wvecDim:])
        
        def updateParams(self, scale, update, log=False):
            """
                        如下这样更新参数
                        p:=p-scale*update
                        如果log是真的,输出根节点的均方误差,并且更新根节点的值
            """
            if log:
                for P, dP in zip(self.stack[1:], update[1:]):
                    pRMS = np.sqrt(np.mean(P ** 2))
                    dpRMS = np.sqrt(np.mean((scale * dP) ** 2))
                    print("weight rms=%f -- update rms=%f" % (pRMS, dpRMS))
            self.stack[1:] = [P + scale * dP for P, dP in zip(self.stack[1:], update[1:])]
            
            # 解决词典并且进行稀疏的更新
            dL = update[0]
            for j in dL.iterkeys():
                self.L[:, j] += scale.dL[j]
        
        def toFile(self, fid):
            pickle.dump(self.stack, fid)
        
        def fromFile(self, fid):
            self.stack = pickle.load(fid)
        
        def check_grad(self, data, epsilon=1e-6):
            cost, grad = self.costAndGrad(data)
            
            err1 = 0.0
            count = 0.0
            print("Checking dW...")
            for W, dW in zip(self.stack[1:], grad[1:]):
                W = W[..., None]
                dW = dW[..., None]
                for i in range(W.shape[0]):
                    for j in range(W.shape[1]):
                        W[i, j] += epsilon
                        costP, _ = self.costAndGrad(data)
                        W[i, j] -= epsilon
                        numGrad = (costP - cost) / epsilon
                        err = np.abs(dW[i, j] - numGrad)
                        err1 += err
                        count += 1
            if 0.001 > err1 / count:
                print("Grad Check Passed for dW")
            else:
                print("Grad Check Failed for dW:Sum of Error=%.9f" % (err1 / count))
            
            
            # check dL separately since dict
            dL = grad[0]
            L = self.stack[0]
            err2 = 0.0
            count = 0.0
            print("Checking dL...")
            for j in dL.keys():
                for i in range(L.shape[0]):
                    L[i, j] += epsilon
                    costP, _ = self.costAndGrad(data)
                    L[i, j] -= epsilon
                    numGrad = (costP - cost) / epsilon
                    err = np.abs(dL[j][i] - numGrad)
                    err2 += err
                    count += 1
            if 0.001 > err2 / count:
                print("Grad Check Passed for dL")
            else:
                print("Grad Check Failed for dL: Sum of Error = %.9f" % (err2 / count))
    
    if __name__ == '__main__':
    
        train = treeM.loadTrees()
        numW = len(treeM.loadWordMap())
        
        wvecDim = 10
        outputDim = 5
        
        rnn = RNN(wvecDim, outputDim, numW, mbSize=4)
        rnn.initParams()
        
        mbData = train[:4]
        print("Numerical gradient check...")
        rnn.check_grad(mbData)
            

     下面部分是构造节点的python文件tree.py

    在进行计算时需要先运行tree.py文件进行tree结构的生成,然后进行合并计算

    import collections
    import pickle
    UNK = 'UNK'
    # This file contains the dataset in a useful way. We populate a list of Trees to train/test our Neural Nets such that each Tree contains any number of Node objects.
    
    # The best way to get a feel for how these objects are used in the program is to drop pdb.set_trace() in a few places throughout the codebase
    # to see how the trees are used.. look where loadtrees() is called etc..
    
    
    class Node: # a node in the tree
        def __init__(self,label,word=None):
            self.label = label 
            self.word = word # NOT a word vector, but index into L.. i.e. wvec = L[:,node.word]
            self.parent = None # reference to parent
            self.left = None # reference to left child
            self.right = None # reference to right child
            self.isLeaf = False # true if I am a leaf (could have probably derived this from if I have a word)
            self.fprop = False # true if we have finished performing fowardprop on this node (note, there are many ways to implement the recursion.. some might not require this flag)
            self.hActs1 = None # h1 from the handout
            self.hActs2 = None # h2 from the handout (only used for RNN2)
            self.probs = None # yhat
    
    class Tree:
    
        def __init__(self,treeString,openChar='(',closeChar=')'):
            tokens = []
            self.open = '('
            self.close = ')'
            for toks in treeString.strip().split():
                tokens += list(toks)
            self.root = self.parse(tokens)
    
        def parse(self, tokens, parent=None):
            assert tokens[0] == self.open, "Malformed tree"
            assert tokens[-1] == self.close, "Malformed tree"
    
            split = 2 # position after open and label
            countOpen = countClose = 0
    
            if tokens[split] == self.open: 
                countOpen += 1
                split += 1
            # Find where left child and right child split
            while countOpen != countClose:
                if tokens[split] == self.open:
                    countOpen += 1
                if tokens[split] == self.close:
                    countClose += 1
                split += 1
    
            # New node
            node = Node(int(tokens[1])) # zero index labels
    
            node.parent = parent 
    
            # leaf Node
            if countOpen == 0:
                node.word = ''.join(tokens[2:-1]).lower() # lower case?
                node.isLeaf = True
                return node
    
            node.left = self.parse(tokens[2:split],parent=node)
            node.right = self.parse(tokens[split:-1],parent=node)
    
            return node
    
            
    
    def leftTraverse(root,nodeFn=None,args=None):
        """
        Recursive function traverses tree
        from left to right. 
        Calls nodeFn at each node
        """
        nodeFn(root,args)
        if root.left is not None:
            leftTraverse(root.left,nodeFn,args)
        if root.right is not None:
            leftTraverse(root.right,nodeFn,args)
    
    
    def countWords(node,words):
        if node.isLeaf:
            words[node.word] += 1
    
    def clearFprop(node,words):
        node.fprop = False
    
    def mapWords(node,wordMap):
        if node.isLeaf:
            if node.word not in wordMap:
                node.word = wordMap[UNK]
            else:
                node.word = wordMap[node.word]
        
    
    def loadWordMap():
        with open('wordMap.bin','rb') as fid:
            return pickle.load(fid)
    
    def buildWordMap():
        """
        Builds map of all words in training set
        to integer values.
        """
    
    
        file = 'trees/train.txt'
        print("Reading trees to build word map..")
        with open(file,'r') as fid:
            trees = [Tree(l) for l in fid.readlines()]
    
        print("Counting words to give each word an index..")
        
        words = collections.defaultdict(int)
        for tree in trees:
            leftTraverse(tree.root,nodeFn=countWords,args=words)
        
        wordMap = dict(zip(words.keys(),range(len(words))))
        wordMap[UNK] = len(words) # Add unknown as word
        
        print("Saving wordMap to wordMap.bin")
        with open('wordMap.bin','wb') as fid:
            pickle.dump(wordMap,fid)
    
    def loadTrees(dataSet='train'):
        """
        Loads training trees. Maps leaf node words to word ids.
        """
        wordMap = loadWordMap()
        file = 'trees/%s.txt'%dataSet
        print("Loading %sing trees.."%dataSet)
        with open(file,'r') as fid:
            trees = [Tree(l) for l in fid.readlines()]
        for tree in trees:
            leftTraverse(tree.root,nodeFn=mapWords,args=wordMap)
        return trees
          
    if __name__=='__main__':
        buildWordMap()
        
        train = loadTrees()
    
        print("Now you can do something with this list of trees!")

     

    更详细的代码请参考github:

    https://github.com/weizhenzhao/cs224d_problem_set3

     

    展开全文
  • <div><p>该提问来源于开源项目:SocalNick/ScnSocialAuth</p></div>
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