实验报告 人工神经网络

1、 实验报告人工神经网络实验原理：利用线性回归和神经网络建模技术分析预测。实验题目：利用给出的葡萄酒数据集，解释获得的分析结论。library(plspm);data(wines);wines实验要求：1、探索认识意大利葡萄酒数据集，对葡萄酒数据预处理，将其随机划分为训练集和测试集，然后创建一个线性回归模型；2、利用neuralnet包拟合神经网络模型；3、评估两个模型的优劣，如果都不理想，提出你的改进思路。分析报告：1、线性回归模型rm(list=ls()gc()used(Mb)gctrigger(Mb)maxused(Mb)Ncells25034013.460839432.540871221

2、.9Vcells4983343.9838860864.0160673612.3library(plspm)data(wines)winesc(1:5),1classalcoholmalic.acidashalcalinitymagnesium127phenolsflavanoids114.231.712.4315.62.803.062113.201.782.1411.21002.652.763113.162.362.6718.61012.803.244114.371.952.5016.81133.853.495113.242.592.8721.01182.802.69nofla.phenpro

3、enshuedilutedproline10.282.295.641.043.92106520.261.284.381.053.40105030.302.815.681.033.17118500.863.45148050.391.824.321.042.93735datasummary(wines)Min.1.000Min.11.03Min.0.740Min.1.3601stQu.1.0001stQu.12.361stQu.1.6031stQu.2.210Median2.000Median13.05Median1.865Median2.360Me

4、an1.938Mean13.00Mean2.336Mean2.3673rdQu.3.0003rdQu.13.683rdQu.3.0833rdQu.2.558Max.3.000Max.14.83Max.5.800Max.3.230alcalinitymagnesiumphenolsflavanoidsMin.10.60Min.70.00Min.:0.980Min.:0.3401stQu.17.201stQu.88.001stQu:1.7421stQu:1.205Median19.50Median98.00Median:2.355Median:2.135Mean19.49Mean99.74Mean

5、:2.295Mean:2.0293rdQu.21.503rdQu.107.003rdQu:2.8003rdQu:2.875Max.30.00Max.162.00Max.:3.880Max.:5.080enshueMin.0.1300Min.:0.410Min.:1.280Min.:0.48001stQu.0.27001stQu:1.2501stQu:3.2201stQu.:0.7825Median0.3400Median:1.555Median:4.690Median:0.9650Mean0.3619Mean:1.591Mean:5.058Me

6、an:0.95743rdQu.0.43753rdQu:1.9503rdQu:6.2003rdQu.:1.1200Max.0.6600Max.:3.580Max.:13.000Max.:1.7100dilutedprolineMin.1.270Min.278.01stQu.1.9381stQu.500.5Median2.780Median673.5Mean2.612Mean746.93rdQu.3.1703rdQu.985.0Max.4.000Max.1680.0classalcoholmalic.acidashNumVariableDescription解释1classTypeofwine葡萄

7、酒的种类2alcoholAlcohol醇3malic.acidMalicacid苹果酸4ashAsh灰5alcalinityAlcalinity碱度6magnesiumMagnesium镁7phenolsTotalphenols酚类8flavanoidsFlavanoids黄酮9nofla.phenNonflavanoidphenols非黄烷类酚类10proanthoProanthocyanins化青素11ensColorintensity颜色强度12hueHue色调13dilutedOD280/OD315ofdilutedwines稀释的葡萄酒14prolineProline脯

8、氨酸apply(data,2,function(x)sum(is.na(x)enshuediluted000ashalcalinitymagnesiumphenols0proline0dim(wines)117814set.seed(2)test=sample(1:nrow(wines),100)wines.trainwines.testdim(wines.train);dim(wines.testm.fitsummary(lm.fit)

9、Call:glm(formula=alcohol.,data=wines.train)DevianceResiduals:Min1QMedian3QMax-0.98017-0.31067-0.004050.361841.23885Coefficients:EstimateStd.ErrortvaluePr(|t|)(Intercept)13.06613611.266491010.3173.04e-15*class-0.40439940.2389115-1.6930.09538malic.acid0.16129620.07305592.2080.03085*ash0.26214480.36692

10、350.7140.47755ens0.15800590.04478353.5280.00078*hue

11、0.12262600.42054200.2920.77154diluted-0.08890850.1967579-0.4520.65289proline0.00081120.00039432.0580.04371*0.1Signif.codes:0*0.001*0.01*0.051(Dispersionparameterforgaussianfamilytakentobe0.2968956)Nulldeviance:57.473on77degreesoffreedomResidualdeviance:19.001on64degreesoffreedomAIC:141.2NumberofFish

12、erScoringiterations:2pr.lm-predict(lm.fit,wines.test)MSE.lm-sum(pr.lm-wines.test$alcohol)2)/nrow(wines.test)print(MSE.lm)10.30436252、神经网络模型maxs-apply(wines,2,max)mins-apply(wines,2,min)scaled-as.data.frame(scale(wines,center=mins,scale=maxs-mins)index-sample(1:nrow(wines),round(0.75*nrow(wines)train

13、_-scaledindex,test_-scaledindex,library(neuralnet)nf-as.formula(paste(alcohol,paste(n!n%in%alcohol,collapse=+)nnpr.nnpr.nn_test.r1par(mfrow=c(l,2)plot(test_$alcohol,pr.nn_,col=red,main=RealvspredictedNN,pch=l8,cex=0.7)abline(0,l,lwd=2)legend(bottomright,legend=NN,pch=l8,col=red,bty=n)plot(wines.test

14、$alcohol,pr.lm,col=blue,main=Realvspredictedlm,pch=18,cex=0.7)abline(0,1,lwd=2)legend(bottomright,legend=LM,pch=18,col=blue,bty=n,cex=0.7)RealvspredictedNNRealvspredictedlm_nn.rpo06.04.02.00.0ml.rp5.410.415.310.3.5I3I5I2.5test_$alcoholwines.test$alcoholpar(mfrow=c(1,1)plot(test_

15、$alcohol,pr.nn_,col=red,main=RealvspredictedNN,pch=18,cex=0.7)points(wines.test$alcohol,pr.lm,col=blue,pch=18,cex=0.7)abline(0,1,lwd=2)legend(bottomright,legend=c(NN,LM),pch=18,col=c(red,blue)RealvspredictedNN00009000CNO0-0匚匚d0.00.81.0test_$alcohollibrary(boot)set.seed(200)lm.fitcv.glm(data,lm.fit,K=10)$delta110.3058061679set.seed(450)cv.errorklibrary(plyr)pbarpbar$init(k)for(iin1:k)index-sample(1:nrow(data),round(0.9*nrow(data