coursera machine learning python

is the custom of naming in Python? I found that if the variable name was completely expanded, it would be too long-my MacBook Pro was too ugly to show up. This is followed by the variable shorthand naming of C + +.V. Entrance Call functionThe main function, similar to C + +. As soon as you run the knn.py script, the code is executed first:if __name__ = = ' __main__ ': print "You are running knn.py " CLASSIFYSAMPLEFILEBYKNN (' datingSetOne.txt '

Prediction problems in machine learning are usually divided into 2 categories: regression and classification .Simply put, regression is a predictive value, and classification is a label that classifies data.This article describes how to use Python for basic data fitting, and how to analyze the error of fitting results.This example uses a 2-time function with a ra

module. But this and the original SSH ratio is still not very stable, not very useful. Not suitable for production environments. To be useful or to change the native SSH, but we will not, we will only change Python. In short this chapter is to achieve a fortress machine function, really want to do a good thing to say later.The more famous is probably this: jumpserver-open-source Springboard machineLong con

different features to the same interval: normalization and normalizationNormalization:From sklearn.preprocessing import MinmaxscalerStandardization:From sklearn.preprocessing import StandardscalerSelect a feature that is meaningfulIf a model behaves much better than a test data set on a training dataset, it means that the model is too fit for training data.The commonly used schemes to reduce generalization errors are:(1) Collect more training data(2) Introduction of penalty by regularization(3)

Stitching raw DATA:Train_data = pd.read_csv (' train.csv ') Test_data = pd.read_csv (' test.csv ') All_data = Np.vstack ((train_data.ix[:,1:-1], TEST_DATA.IX[:,1:-1]))Merge array Vstack and Hstack functions under NumPy:>>> a = Np.ones ((2,2)) >>> B = Np.eye (2) >>> print Np.vstack ((A, b)) [[1. 1.] [1. 1.] [1. 0.] [0. 1.]]>>> Print Np.hstack ((A, b)) [[1. 1. 1. 0.] [1. 1. 0. 1.]Generate a high (2) secondary feature:def group_data (data, degree=2, Hash=hash): new_data = [] m,n = D

(labels[:: -1]) Xgtrain = XGB. Dmatrix (Train[offset:,:], Label=labels[offset:]) Xgval = XGB. Dmatrix (Train[:offset,:], label=labels[:offset]) watchlist = [(Xgtrain, ' Train '), (Xgval, ' val ')]model = Xgb.train (plst , Xgtrain, Num_rounds, watchlist, early_stopping_rounds=120) preds2 = Model.predict (xgtest,ntree_limit=model.best_ Iteration) #combine Predictions#since the metric only cares on relative rank we don ' t need to Averagepreds = (PREDS1) * *. 4 + (PREDS2) *8.6return Preds(Code fro

data.X = [[1.,-1., 2.], [2., 0., 0.], [0.,1.,-1.]] Binarizer= preprocessing. Binarizer (). Fit (X)#The default threshold value is 0.0PrintBinarizer#Binarizer (copy=true, threshold=0.0)Printbinarizer.transform (X)#[1.0. 1.]#[1.0. 0.]#[0.1. 0.]Binarizer= preprocessing. Binarizer (threshold=1.1)#set the threshold value to 1.1Printbinarizer.transform (X)#[0.0. 1.]#[1.0. 0.]#[0.0. 0.]4. Label preprocessing (label preprocessing)4.1) Label binary value (label binarization)Labelbinarizer is typica

-0.576525 11.778922 0-0.346811-1.678730 1-2.124484 2.672471 11.217916 9.597015 0-0.733928 9.098687 0-3.642001-1.618087 10.315985 3.523953 11.416614 9.619232 0-0.386323 3.989286 10.556921 8.294984 11.224863 11.587360 0-1.347803-2.406051 11.196604 4.951851 10.275221 9.543647 00.470575 9.332488 0-1.889567 9.542662 0-1.527893 12.150579 0-1.185247 11.309318 0-0.445678 3.297303 11.042222 6.105155 1-0.618787 10.320986 01.152083 0.548467 10.828534 2.676045 1-1.237728 10.549033 0-0.683565-2.166125 10.229

Citycluster[label[i]].append (Cityname[i]) #将每个簇的城市输出For I in range (len (citycluster)):Print ("expenses:%.2f"% expenses[i]) #将每个簇的平均花费输出Print (Citycluster[i])Click to run, you can come out results.Where the N_clusters class, the consumption level of similar cities gathered in a classExpense: The numerical plus of the central point of the cluster, that is, the average consumption levelImplementation process:1, establish the project, import Sklearn related packageImport NumPy as NPFrom Sklearn.cl

)]=1 else:print "The word:%s is not in my vocabulary!" %word return returnvecdef TRAINNBC (trainsamples,traincategory): Numtrainsamp=len (Trainsamples) NumWords=len (train Samples[0]) pabusive=sum (traincategory)/float (numtrainsamp) #y =1 or 0 feature Count P0num=np.ones (numwords) P1NUM=NP.O NES (numwords) #y =1 or 0 category count P0numtotal=numwords p1numtotal=numwords for I in Range (Numtrainsamp): if Traincategory[i]==1:p0num+=trainsamples[i] P0numtotal+=sum (Trainsamples[i]) E

attribute in the data set. The general situation is somewhere between the two.D. High-dimensional mappingMap properties to high-dimensional space. This is the most precise approach, which completely retains all the information and does not add any additional information. For example, Google, Baidu's CTR Prediction model, pre-processing will be all the variables to deal with this, up to hundreds of millions of dimensions. The benefit of this is that the entire information of the original data is

=linearr.predict (X_train) #基于训练集得到的线性y值Plt.figure ()Plt.scatter (x_train,y_train,color= ' green ') #原始训练集数据散点图Plt.plot (x_train,y_train_pred,color= ' black ', linewidth=4) #线性回归的拟合线Plt.title (' Train ') #标题Plt.show ()Y_test_pred=linearr.predict (X_test)Plt.scatter (x_test,y_test,color= ' green ') #绘制测试集数据散点图Plt.plot (x_test,y_test_pred,color= ' black ', linewidth=4) #基于线性回归的预测线Plt.title (' Test ')Plt.show ()Print (' mse= ', Sm.mean_squared_error (y_test,y_test_pred)) #MSE值Print (' r2= ', Sm.r2_

Installation sudo yum install NumPy From numpy Import * Produces an array Random.rand (4,5) Result Array ([[0.79056842, 0.31659893, 0.34054779, 0.97328131, 0.32648329], [0.51585845, 0.70683055, 0.31476985, 0.07952725, 0.80907845], [0.81623517, 0.61038487, 0.66679161, 0.77412742, 0.03394483], [0.41758993, 0.54425978, 0.65350633, 0.90397197, 0.72706079]]) Produce a matrix >>> Randmat=mat (Random.rand (bis)) >>> randmat.i Matrix ([[[1.72265179, 0.82071484, 0.8218207,-3.20005387], [0.60602642,-1.28

Misc.forsale 0.91 0.70 0.79 257 the Rec.autos 0.89 0.89 0.89 238 - Rec.motorcycles 0.98 0.92 0.95 276 - Rec.sport.baseball 0.98 0.91 0.95 251 the Rec.sport.hockey 0.93 0.99 0.96 233 the Sci.crypt 0.86 0.98 0.91 238 the sci.electronics 0.85 0.88 0.86 249 the sci.med 0.92 0.94 0.93 245 - sci.space 0.89 0.96 0.92 221 the Soc.religion.christian 0.78 0.96 0.86 232 the talk.politics.guns 0.88 0.96 0.92 251 the talk.politics.mideast 0.90 0.98 0.94 23194 Talk.politics.misc 0.79 0.89 0.84 188 the Talk.r

:", X) - Print("Y:", Y) - innumiterations=100000 -alpha=0.0005 toTheta=np.ones (x.shape[1]) +Theta=graientdescent (x,y,theta,alpha,x.shape[0],numiterations) - Print(Theta)Operation Result:...... Too many output data to intercept only the next more than 10 linesIteration 99988/cost:3.930135Iteration 99989/cost:3.930135Iteration 99990/cost:3.930135Iteration 99991/cost:3.930135Iteration 99992/cost:3.930135Iteration 99993/cost:3.930135Iteration 99994/cost:3.930135Iteration 99995/cost:3.930135Iterat

* (XMAT.T * (Weights *Ymat)) returnTestPoint *SigmadefLwlrtest (Testarr,xarr,yarr,k = 1.0): M=shape (Testarr) [0] Yhat=zeros (m) forIinchRange (m): Yhat[i]=LWLR (testarr[i],xarr,yarr,k)returnYhatThe LWLR () function is the code for locally weighted linear regression, and the function of the lwlrtest () function is to make the LWLR () function traverse the entire data set. We also need to draw a picture to see how the results fit. def PlotLine1 (testarr,xarr,yarr,k = 1.0 = Mat (Xarr) ymat = Ma

享平台来找到numpy, scipy and Matplotlib, Here are all. WHL files, which need to be installed via PIP, so there is an important preparation is easy_install pip to complete the PIP installation, after the installation is successful, it can be installed on the above three respectively. WHL for installation in Pip install **.py.5. Download the most important machine learning package: Scikit-learn, the package install

In the previous chapters, we have been using the accuracy rate (accuracy) to evaluate the performance of the model, which is usually a good choice. In addition, there are many evaluation indicators, such as precision (precision), recall rate (recall) and F1 value (F1-score).Confusion matrixBefore explaining the different evaluation indicators, let's start by learning a concept: The confusion matrix (confusion matrix), which shows the matrix of the

are slightly different, and many very small elements are left in the matrix, which results from the computer processing error. Enter the following command to get the error value:>>> MyEye = Randmat*invrandmat>>> Myeye-eye (4)Matrix ([[ 0.00000000e+00, -4.44089210e-16, -4.44089210e-16, -3.33066907E-16], [ -8.88178420e-16, 2.22044605e-16, 0.00000000e+00, 5.55111512E-17], [ 4.44089210e-16, 0.00000000e+00, 0.00000000e+00, -5.55111512E-17],

]) $self.errors_=[] - - for_inchRange (self.n_iter): theerrors=0 - forXi,targetinchzip (x, y):Wuyi #calculates the error between the forecast and the actual value multiplied by the learning rate theupdate=self.eta* (target-Self.predict (xi)) -self.w_[1:]+=update*XI WuSelf.w_[0]+=update*1 -Errors + = Int (update!=0) About self.errors_.append (Errors) $ return Self - - #define the p