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:", 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

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)

written text can really deepen the understanding of the problem, and constantly self-thinking. After all, I write these things not for the sake of books, but to accumulate the key content of learning flexibly, and to do better knowledge management. Of course, it would be better if it helped the reader.Reprint please indicate the author Jason Ding and its provenanceGitHub home page (http://jasonding1354.github.io/)CSDN Blog (http://blog.csdn.net/jason

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

System: OS X 10.11.6 The MAC system has its own Python2.7, using the Easy_install command with its own system to install the modules online. If you need to use the PYTHON3 environment, python3.5 is invoked at the terminal input Python3 after installing the Python3.5.1, view Python version Python 2, install NumPyNumPy is a Python package. It represents "Numer

classes in the data. - -Many, many more ... the the a total of 150 data samples the evenly distributed over 3 subspecies the 4 petals per sample, calyx shape Description - " " the the " " the 2 dividing the training set and the test set94 " " theX_train, X_test, y_train, y_test =train_test_split (Iris.data, the Iris.target, thetest_size=0.25,98Random_state=33) About - " "101 3 K Nearest Neighbor Classifier learning model and prediction102 " "10

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See original book 2.1-2.2 sectionThe new dataset is like a wrapped gift, filled with promise and hope!But until you open it, it remains mysterious!I. Structure and terminology of the underlying problem, characteristics of the machine learning data setTypically, rows represent instances, columns represent attribute characteristicsproperty, the data used in the ins

[:, 1].max () + 1 xx1, xx2 = Np.meshgrid (Np.arange (X1_min, X1_max, resolution), Np.aran GE (X2_min, X2_max, resolution)) Z = Classifier.predict (Np.array ([Xx1.ravel (), Xx2.ravel ()]). T) Z = Z.reshape (xx1.shape) Plt.contourf (xx1, xx2, Z, alpha=0.4, Cmap=cmap) Plt.xlim (Xx1.min (), Xx1.max ()) Plt.ylim (Xx2.min (), Xx2.max ()) # Plot all samples for IDX, C1 in enumerate (Np.unique (y)): Print Idx,c1 Plt.scatter (X=x[y = = C1, 0], Y=x[y = = C1, 1], alpha=0.8, C=cmap (IDX), MARKER=M

', Standardscaler ()), ('CLF', Logisticregression (penalty='L2', random_state=0)]) train_sizes, train_scores, Test_scores= Learning_curve (ESTIMATOR=PIPE_LR, X=x_train, Y=y_train, Train_sizes=np.linspace (0.1, 1.0, ten), cv=10, N_jobs=1) Train_mean= Np.mean (Train_scores, Axis=1) TRAIN_STD= NP.STD (Train_scores, Axis=1) Test_mean= Np.mean (Test_scores, Axis=1) TEST_STD= NP.STD (Test_scores, Axis=1) Plt.plot (train_sizes, Train_mean, color='Blue', marker='0', Markersize=5, label='Training Accurac

understand computer knowledge, psychology and philosophy. Artificial intelligence consists of a very wide range of sciences, consisting of a variety of fields, such as machine learning, computer vision, and so on, in general, one of the main goals of AI research is to make machines capable of doing complex work that normally requires human intelligence. But different times, different people's understanding

transform and discrete Fourier transform, filtering, convolution, differential and integral, modulation sampling and so on. Each chapter starts with an example and guides the reader through programming to understand the concept in a precise way. In addition, each chapter of the book provides exercises and code examples to help readers understand this knowledge.This book is suitable for readers who are inte

(Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) $ Print("the mean square error of the linear is:", Lr_mse) -Lr_mae =Mean_absolute_error (Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) - Print("the average absolute error of the linear is:", Lr_mae) - A #evaluation of the SGD model +Sgdr_score =Sgdr.score (x_test, y_test) the Print("the default evaluation value for SGD is:", Sgdr_score) -sgdr_r_squared =R2_score (y_test, sgdr_y_predict) $ Print("

regression tree is:", Dtr.score (X_test, y_test)) - Print("the r_squared values for the flat regression tree are:", R2_score (Y_test, dtr_y_predict)) - Print("the mean square error of the regression tree is:", Mean_squared_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dtr_y_predict))) A Print("the average absolute error of the regression tree is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), + Ss_y.inverse_transform (dtr_y_predict))) the - " " $ the default evalua

.score (X_train_poly2, Y_train))#0.9816421639597427Two-time linear regression model fitted curves:The fitting degree is better than 1 linear fitting.The following 4 linear regression models are performed:1 #four-time linear regression model fitting2Poly4 = Polynomialfeatures (degree=4)#4-time polynomial feature generator3X_train_poly4 =poly4.fit_transform (X_train)4 #Building Model Predictions5Regressor_poly4 =linearregression ()6 Regressor_poly4.fit (X_train_poly4, Y_train)7 #draw a graph of 2