plot(gin1)
#运行一下network可以看一下小摘要 network #=== Estimated network === #Number of nodes: 25 #Number of non-zero edges: 168 / 300 #Mean weight: 0.01947714 #Network stored in x$graph #Default set used: EBICglasso #Use plot(x) to plot estimated network #Use bootnet(x) to bootstrap edge...
net<-pegas::haploNet(h,d=NULL,getProb=TRUE)net ind.hap<-with(utils::stack(setNames(attr(h,"index"),rownames(h))),table(hap=ind,individuals=names(nbin)))ind.happlot(net,size=attr(net,"freq"),scale.ratio=2,cex=0.6,labels=TRUE,pie=ind.hap,show.mutation=1,font=2,fast=TRUE)legen...
Before R2021a, use commas to separate each name and value, and enclose Name in quotes. Example: Plots="training-progress",Metrics="accuracy",Verbose=false specifies to disable the verbose output and display the training progress in a plot that also includes the accuracy metric. Monitoring expand...
Visualize some of the sequences in a plot. numChannels = size(data{1},1); idx = [3 4 5 12]; figure tiledlayout(2,2) for i = 1:4 nexttile stackedplot(data{idx(i)}', ... DisplayLabels="Channel " + string(1:numChannels)) xlabel("Time Step") title("Class: " + string(label...
We plot the MSEs of MHN and SQHN for each data set to show that MHN have no performance difference between training and in-distribution data, making it good at generalization but unable to perform recognition. Hyperparameters A grid search was used to find the β and learning rate values ...
))) exp.dat %>% head library(CBNplot) bngeneplot(ko.res, exp=exp.dat, pathNum=1, orgDb=NULL)Another customized plot.About Bayesian network plot for the enrichment analysis results (Fork for R version compatibility) noriakis.github.io/software/CBNplot Resources Readme Activity Stars ...
a, Cross-validation errors of multi-omic data sets. 16S and 18S rRNA gene data were collapsed to SILVA taxonomic level 7 (L7) and 12 (L12). Boxplots represent average prediction MAE in ADD of individual bodies during nested cross-validation of 36 body dataset. 16S rRNA soil face, soil ...
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plot(episodes_list, mv_return) plt.xlabel('Episodes') plt.ylabel('Returns') plt.title('DQN on {}'.format(env_name)) plt.show() 可以看到,DQN 的性能在 100 个序列后很快得到提升,最终收敛到策略的最优回报值 200。我们也可以看到,在 DQN 的性能得到提升后,它会持续出现一定程度的震荡,这主要...