andrew ng coursera machine learning notes

= {bestfeatlabel:{}} #创建节点del (labels[ Bestfeat]) featvalues = [Example[bestfeat] For example in dataset]uniquevals = Set (featvalues) for value in Uniquevals: #划分, Create subtree sublabels = Labels[:]mytree[bestfeatlabel][value] = Createtree (Splitdataset (dataset,bestfeat,value), subLabels) Return mytreeThus, the decision tree is constructed.Construction Result:Using Decision Trees:def classify (Inputtree,featlabels,testvec): Firststr = Inputtree.keys () [0]seconddict = Inputtree[firststr]fea

Classlabelvector.append (int (listfromline[-1])) Index + =1 returnReturnmat,classlabelvector#归一化数据 def autonorm(dataSet): #找出样本集中的最小值Minvals = Dataset.min (0)#找出样本集中的最大值Maxvals = Dataset.max (0)#最大最小值之间的差值Ranges = Maxvals-minvals#创建与样本集一样大小的零矩阵Normdataset = Zeros (Shape (dataSet)) m = dataset.shape[0]#样本集中的元素与最小值的差值Normdataset = Dataset-tile (Minvals, (M,1))#数据相除, NormalizationNormdataset = Normdataset/tile (ranges, (M,1))returnNormdataset, Ranges, minvals def datingclasstest(): #选取多少数据

) [0] ]# calculates the mean value of the Pstinclust columns: mean (Ptsinclust,axis = 0): axis=0 #按列计算 Clustercents[cent,:] = mean (Ptsinclust,axis = 0)4.3.4 Evaluation Classification Results:Fifth stage: Visualization of classification results.　　# returns the cluster Center for the completion of the calculation Print " clustercents:\n " , Clustercents # classify and depict data points Color_cluster (clusdist[:,0:1],dataset,plt)# based on Clustdist to draw a cluster center Drawscatter (plt

Nano Bring up Help: F1 or Ctrl+g ^ represents Ctrl,m on behalf of Alt Shutdown: Due Process Check System usage Status: Who: Check online users NETSTAT-A: Check the network connection Ps-aux: Checking background program execution Notify online users of shutdown time: Shutdown command Data write to drive: Sync Shutdown or restart: Shutdown/restart/halt/poweroff/init 0 Shutdown-h now: Shut down imm

! = TEST.Y)#结果是50行预测错了16个点, the accuracy rate is only 68%, so the conclusion is that if the problem is not linear at all, K-nearest neighbor behaves better than GLM.#三, the following recommended cases, using kaggle data, according to a programmer has installed the package to predict whether the programmer will install another packageInstallations Head (installations)Library (' reshape ')#数据集中共三列, respectively, is package,user,installed.#cast函数的作用: Data in DataSet, user as row, package as column,

entire database of the cluster is divided into 16,384 slots (slots), each node in the cluster can handle 0~16384 slots, when the 16,384 slots in the database have nodes in the processing, the cluster is on-line (OK), otherwise in the offline state (fail). The Clusternode structure records which slots are processed by the node, and the assignment information for the 16,384 slots is recorded in the clusterstate structure. CRC16 then modulo 16384 takes the remainder to calculate which slot the key