multivariate linear regression python example

I. OutlineNormal equation method for linear regressionLocal weighted linear regressionIi. details of the contents　　1. Normal equation solution of linear regressionLinear regression is the prediction of a continuous type of data. The example of

matrixA λi is added to the x^t*x to make the matrix non-singular, and then the x^t*x+λi can be reversed. Where I is the unit matrix, λ is the user-determinedA numeric value of righteousness.Ridge regression is one of the reduction methods, which is equivalent to limiting the size of the regression coefficients. Another good method of reduction is lasso. Lasso is difficult to solve, but it can be calculated

A linear/Nonlinear regression fitting example using R language (1) 1. Generate a set of data vector vector Ofstreamfout ("Data2.txt"); for (int i =1;i { float x =i*0.8; Float randdnum= rand ()%10 * 10; Floatrandomflag = (rand ()%10)%2==0? (1):(-1); Float y = 3 *x*x + 2*x + 5 + randomflag*randdnum; fout Xxvec.push_back (x); Yyvec.push_back (y); } Fout.close (); S

This time will be the next issue of SHUANGSE Qiu number forecast, think of a little excitement ah. The code uses the linear regression algorithm, which uses this algorithm to predict the effect, and you can consider using other algorithms to try the results. Before discovering a lot of code is repetitive work, in order to make the code look more elegant, define the function, to call, suddenly tall #!/usr/b

Rate the Fl-score the Support the 98 Logistic regression accuracy rate: 0.9707602339181286 About Other indicators of logistic regression: - Precision recall F1-score support101 102 benign 0.96 0.99 0.98103 Malignant 0.99 0.94 0.96104 the avg/total 0.97 0.97 0.97 171106 107 estimation accuracy of stochastic parameters: 0.9649122807017544108 Other indicators of stochastic parameter estimation:109 Precision

Example of the 2D regression linear scatter effect implemented by jQuery plug-in HighCharts [with demo source code download], jqueryhighcharts The example in this article describes the 2D regression linear scatter effect implement

= Polynomialfeatures (degree=4)#4-time polynomial feature generator -X_train_poly4 =poly4.fit_transform (X_train) Wu #Building Model Predictions -Regressor_poly4 =linearregression () About Regressor_poly4.fit (X_train_poly4, Y_train) $X_test_poly4 =poly4.transform (x_test) - Print("four-time linear model prediction score:", Regressor_poly4.score (X_test_poly4, Y_test))#0.8095880795746723 - - #learning and predicting using L1 norm regularization

Use the Linear_model of the Sklearn library. Linearregression (), can be very simple linear regression analysisHere is the code:1 #Import the Linear_model class under the Sklearn library2 fromSklearnImportLinear_model3 #Import Pandas Library, alias for PD4 ImportPandas as PD5 6filename = r'D:\test.xlsx'7 #reading data Files8data =pd.read_excel (filename)9 Ten #transform the argument data into a matrix Onex

), 'STD': List (Np.diag (np.sqrt (Res.cov_params ))),'T': List (res.tvalues),'Sig': [I forIinchMap (lambda x:float(x), ("". Join ("{:. 4f},"*len (res.pvalues)). Format (*list (res.pvalues)). Rstrip (","). Split (",")]}returnvalue= {'Model': Model,'coefficient': Coefficient}print (returnvalue){ 'Model': { 'DF':3.0, 'N':665, 'prob_f_statistic':1.185607423551511E-17, 'R_squared_adj':0.11247707470462853, 'f_statistic':29.049896130

In this paper, the theory and proof of least square method and the calculation process are introduced, and the program code of two least squares is given.Octave CodeClear All;close all;% fitted Data set x = [2;6;9;13]; y = [4;8;12;21];% Data lengthN = length (x);% 3Percent Count x mean m_x = SUM (x)/N%%% calculates the average of t m_t = SUM (y)/N%%% calculates the average of t*x m_xt = SUM (y.*x)/N%%% calculates the average of x squared m_xx = SUM (x.*x)/NPercent percent calculates the value of

') plt.ylabel (' Ratio_sugar ') plt.title (' LDA ') plt.show () W=calulate_w () plot (W)The results are as follows: The corresponding W value is:[ -6.62487509e-04, -9.36728168e-01]Because of the relationship between data distribution, LDA's effect is not obvious. So I changed the number of samples of several label=0, rerun the program to get the result as follows:The result is obvious, the corresponding W value is:[-0.60311161,-0.67601433]Transferred from: http://cache.baiducontent.com/c?m= 9d7

=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_

Linear regression is the basis of machine learning and is very useful in daily work.1. What is linear regressionOne-dimensional linear regression can be accomplished by finding the curve of the function with multiple points.2. Mathematical representationis the Intercept valu

Python linear equations solution example: python Linear Equations This article describes how to solve Python linear equations. We will share this with you for your reference. The detai

+ = Reg *W2 $DW + = Reg *W141 142 #perform a parameter update143W + =-step_size *DW144b + =-step_size *DB145W2 + =-step_size *dW2146B2 + =-step_size *DB2147 #evaluate training set accuracy148Hidden_layer = Np.maximum (0, Np.dot (X, W) +b)149Scores = Np.dot (Hidden_layer, W2) +B2 MaxPredicted_class = Np.argmax (scores, Axis=1)151 Print 'Training accuracy:%.2f'% (Np.mean (Predicted_class = =y)) the #plot the resulting classifier153H = 0.02154X_min, X_max = x[:, 0].min ()-1, x[:, 0].max () + 1