totalplot<-cowplot::plot_grid(plotlist=plotlist,ncol=1) Error in `geom_line()`: ! Problem while computing aesthetics. ℹ Error occurred in the 2nd layer. Caused by error in `FUN()`: ! object 'EMD' not found Run `rlang::last_trace()` to see where the error occurred.

plot-number-train.rar_plot data_site:www.pudn.com_train_标号 matla

matlab曲线的颜色代码-matlab_plot_in_C:matlab_plot_in_C

curvePlot.plot({t,x}); curvePlot.exec(); 上面的代码将x相对于t绘制，两个轴均带有默认标签。函数调用.exec()必须是最后一个命令，而对象初始化eggp::Eggplot objectName必须是第一个。所有其他...

sklearn-SVM.zip_-baijiahao_AUC_plot_hyperplane_sklearn_sklearn s

支持向量机(SVM)——分类预测，包括核函数调参，不平衡数据问题，特征降维，网格搜索，管道机制，学习曲线，混淆矩阵，AUC曲线等

Backtrace: ▆ 1. ├─cowplot::plot_grid(plotlist = plotlist, ncol = 1) 2. │ └─cowplot::align_plots(...) 3. │ └─base::lapply(...) 4. │ └─cowplot (local) FUN(X[[i]], ...) 5. │ ├─cowplot::as_gtable(x) 6. │ └─cowplot:::as_gtable.default(x) 7. │ ├─cowplot::as_grob(plot) 8. │ └─cowplot:::as_grob.ggplot(plot) 9. │ └─ggplot2::ggplotGrob(plot) 10. │ ├─ggplot2::ggplot_gtable(ggplot_build(x)) 11. │ │ └─ggplot2:::attach_plot_env(data$plot$plot_env) 12. │ │ └─base::options(ggplot2_plot_env = env) 13. │ ├─ggplot2::ggplot_build(x) 14. │ └─ggplot2:::ggplot_build.ggplot(x) 15. │ └─ggplot2:::by_layer(...) 16. │ ├─rlang::try_fetch(...) 17. │ │ ├─base::tryCatch(...) 18. │ │ │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 19. │ │ │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 20. │ │ │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 21. │ │ └─base::withCallingHandlers(...) 22. │ └─ggplot2 (local) f(l = layers[[i]], d = data[[i]]) 23. │ └─l$compute_aesthetics(d, plot) 24. │ └─ggplot2 (local) compute_aesthetics(..., self = self) 25. │ └─ggplot2:::scales_add_defaults(...) 26. │ └─base::lapply(aesthetics[new_aesthetics], eval_tidy, data = data) 27. │ └─rlang (local) FUN(X[[i]], ...) 28. └─base::.handleSimpleError(...) 29. └─rlang (local) h(simpleError(msg, call)) 30. └─handlers[[1L]](cnd) 31. └─cli::cli_abort(...) 32. └─rlang::abort(...)

在这段backtrace中，可以看到程序最开始是调用了cowplot::plot_grid函数，然后调用了其内部的align_plots函数，接着调用了lapply函数，最后调用了ggplot2的相关函数进行图形的绘制和计算。在这个过程中，程序出现了...

NameError Traceback (most recent call last) Input In [49], in <cell line: 1>() ----> 1 plot_confusion_matrix(model, X_test, y_test,cmap='Blues') 2 plt.tight_layout() NameError: name 'plot_confusion_matrix' is not defined

这个错误提示说明在当前的作用域中找不到名为 plot_confusion_matrix 的函数或类。这通常是因为您没有正确导入所需的库或模块。在这种情况下，我猜测您可能是想使用 sklearn.metrics.plot_confusion_matrix ...

PCA_Plot_3=function (data,Annotation,VAR,Color) { # logcountdata row:genes,column: samples pca <- prcomp(data) pca_out<-as.data.frame(pca$x) df_out<- pca_out %>%tibble::rownames_to_column(var=VAR) %>% left_join(., Annotation) #df_out<- merge (pca_out,Annotation,by.x=0,by.y=0) # label_color<- factor(df_out[,group]) ggplot(df_out,aes_string(x="PC1",y="PC2")) +geom_point(aes_string(colour = Color)) } Deseq2_Deseq_function_2=function (Countdata,Coldata) { dds_fil <- DESeq2:: DESeqDataSetFromMatrix(countData =Countdata, colData = Coldata, design = ~Group) dds_fil_Deg<- DESeq2::DESeq(dds_fil) return(dds_fil_Deg) } pheatmap_singscore=function (pathways,data,Annotation) { Gene_select_anno= data[,colnames(data) %in% pathways] %>%t()%>%.[,rownames(Annotation)] # return(Gene_select_anno) # Anno_expression_data=Gene_select_anno[,c("SYMBOL",Group_select)] %>% as.data.frame() %>% distinct() %>% na.omit() # rownames(Anno_expression_data)=Anno_expression_data[,"SYMBOL"] # Annotation=group_anno["Gene_type"] # input= Anno_expression_data[,Group_select] # F2_pheatmap <- pheatmap::pheatmap(input, cellwigermline calling GATKdth = 10, cellheight = 12, scale = "row", # treeheight_row = 5, # show_rownames = T,show_colnames = T, # annotation_col= Annotation, # # annotation_row=Annotation, # annotation_legend=Label_def, # cluster_rows = T, cluster_cols = F,clustering_distance_rows = "euclidean") pheatmap::pheatmap(Gene_select_anno, cellwigermline=5, cellheight = 10,cellwidth = 10, scale = "row", treeheight_row = 5, show_rownames = T,show_colnames = F, annotation_col= Annotation, # annotation_row=Annotation, #annotation_legend=Label_def, cluster_rows = T, cluster_cols = F,clustering_distance_rows = "euclidean") } matrix.please<-function(x) { m<-as.matrix(x[,-1]) rownames(m)<-x[,1] m } 这是r语言的代码，告诉我每一条代码的作用和意义

PCA_Plot_3: 这个函数用来绘制主成分分析（PCA）的散点图。它接受四个参数：data（数据矩阵），Annotation（注释信息），VAR（行名），Color（颜色）。首先，它对数据进行主成分分析（prcomp函数），然后将主成分...

NameError: name 'plot_roc_curve' is not defined

NameError: name 'plot_roc_curve' is not defined是一个常见的错误，它表示在当前的代码环境中找不到名为'plot_roc_curve'的函数或变量。这通常是由于以下几种情况导致的： 1. 未导入必要的库或模块：如果使用了...

NameError: name 'plot_tree' is not defined

以下是可能导致NameError: name 'plot_tree' is not defined的原因和解决方法： 1. 没有正确导入plot_tree函数。请确保你已经从正确的库中导入了plot_tree函数。例如，如果你使用的是sklearn库，则应该使用以下代码...

> plot_grid(p1, p2, ncol = 2, align = "h", axis = "tb") Error in geom_boxplot(): ! Problem while computing aesthetics. ℹ Error occurred in the 1st layer. Caused by error in FUN(): ! object 'Sepal.Length' not found Run rlang::last_trace() to see where the error occurred.

这个错误提示说找不到 Sepal.Length 这个对象，很可能是因为 Sepal.Length 这个变量名没有定义或者拼写错误。请检查一下代码中是否有定义这个变量，或者确认变量名是否正确。如果问题仍然存在，可以使用 rlang:...

NameError: name 'plot_acf' is not defined

NameError: name 'plot_acf' is not defined 错误可能是由于未正确导入需要的库而引起的。在这种情况下，您需要导入 plot_acf 和 plot_pacf 函数所在的库 statsmodels.graphics.tsaplots。请尝试以下代码...

在运行以下R代码时：library(glmnet) library(ggplot2) # 生成5030的随机数据和30个变量 set.seed(1111) n <- 50 p <- 30 X <- matrix(runif(n * p), n, p) y <- rnorm(n) # 生成三组不同系数的线性模型 beta1 <- c(rep(1, 3), rep(0, p - 3)) beta2 <- c(rep(0, 10), rep(1, 3), rep(0, p - 13)) beta3 <- c(rep(0, 20), rep(1, 3), rep(0, p - 23)) y1 <- X %% beta1 + rnorm(n) y2 <- X %% beta2 + rnorm(n) y3 <- X %% beta3 + rnorm(n) # 设置交叉验证折数 k <- 10 # 设置不同的lambda值 lambda_seq <- 10^seq(10, -2, length.out = 100) # 执行交叉验证和岭回归，并记录CV error和Prediction error cv_error <- list() pred_error <- list() for (i in 1:3) { # 交叉验证 cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k) cv_error[[i]] <- cvfit$cvm # 岭回归 fit <- glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq) pred_error[[i]] <- apply(X, 2, function(x) { x_mat <- matrix(x, nrow = n, ncol = p, byrow = TRUE) pred <- predict(fit, newx = x_mat) pred <- t(pred) mean((x_mat %% fit$beta - switch(i, y1, y2, y3))^2) }) } # 绘制图形 par(mfrow = c(3, 2), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0)) for (i in 1:3) { # CV error plot cv_plot_data <- cv_error[[i]] plot(log10(lambda_seq), cv_plot_data, type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i)) abline(v = log10(cvfit$lambda.min), col = "red") # Prediction error plot pred_plot_data <- pred_error[[i]] plot(log10(lambda_seq), pred_plot_data, type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i)) abline(v = log10(lambda_seq[which.min(pred_plot_data)]), col = "red") }。发生了以下问题：Error in xy.coords(x, y, xlabel, ylabel, log) : 'x'和'y'的长度不一样。请对原代码进行修正

for (i in 1:3) { # 交叉验证 cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k) cv_error[[i]] <- cvfit$cvm # 岭回归 fit <- glmnet(X, switch(i, y1, y2, y3), ...

####### # Purpose: Visualize fastANI core-genome comparison # Usage: Rscript <this_script> <query sequence in fasta format> <subject sequence> <fastANI visualization output> # Output: <fastANI visualization output filename>.pdf # Uses genoPlotR package: http://genoplotr.r-forge.r-project.org #Parse command line arguments query_fasta=commandArgs(TRUE)[1] subject_fasta=commandArgs(TRUE)[2] fastANI_visual_file=commandArgs(TRUE)[3] library(genoPlotR) #Read fastANI output comparison <- try(read_comparison_from_blast(fastANI_visual_file)) #Read sequences into genoPlotR objects Query <- try(read_dna_seg_from_file(query_fasta)) Ref <- try(read_dna_seg_from_file(subject_fasta)) plotTitle = paste(query_fasta, subject_fasta, sep=" v/s ") pdf( paste(fastANI_visual_file,".pdf",sep="") ) plot_gene_map(dna_segs=list(Query, Ref), comparisons=list(comparison), main=plotTitle, scale=FALSE, scale_cex=1, n_scale_ticks=4) dev.off()

这是一段R语言的脚本，用于将fastANI的核基因组比较结果进行可视化。它需要三个输入参数：查询序列（以fasta格式提供）、参考序列（以fasta格式提供）和fastANI可视化输出文件。...输出结果是一个PDF文件，包含了比较...

def select_plot(): plot_options = { '1': Classic_fig, '2': MassFlowReport_fig, '3': Temperature_fig, '4': Pressure_food_fig } while True: print("Please select (Input 1-4,q for exit):") for option, fig in plot_options.items(): print(f"{option}. {fig}") selection = input().lower() if selection == 'q': break elif selection in plot_options: fig = plot_options[selection] fig.show() else: print("Invalid option, please re-enter") 以上代码报错Traceback (most recent call last): File "smart_cfd_curve.py", line 107, in <module> select_plot() File "smart_cfd_curve.py", line 87, in select_plot '1': Classic_fig, NameError: name 'Classic_fig' is not defined 请修改代码

根据代码提示，Classic_fig未定义，所以我们需要先定义Classic_fig，或者将其从plot_options中删除。下面是两种修改方案：方案一：定义Classic_fig 可以先定义Classic_fig，例如： python Classic_fig = plt....

在运行以下R代码时：library(glmnet) library(ggplot2) # 生成5030的随机数据和30个变量 set.seed(1111) n <- 50 p <- 30 X <- matrix(runif(n * p), n, p) y <- rnorm(n) # 生成三组不同系数的线性模型 beta1 <- c(rep(1, 3), rep(0, p - 3)) beta2 <- c(rep(0, 10), rep(1, 3), rep(0, p - 13)) beta3 <- c(rep(0, 20), rep(1, 3), rep(0, p - 23)) y1 <- X %% beta1 + rnorm(n) y2 <- X %% beta2 + rnorm(n) y3 <- X %% beta3 + rnorm(n) # 设置交叉验证折数 k <- 10 # 设置不同的lambda值 lambda_seq <- 10^seq(10, -2, length.out = 100) # 执行交叉验证和岭回归，并记录CV error和Prediction error cv_error <- list() pred_error <- list() for (i in 1:3) { # 交叉验证 cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k) cv_error[[i]] <- cvfit$cvm # 岭回归 fit <- glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq) pred_error[[i]] <- apply(X, 2, function(x) { x_mat <- matrix(x, nrow = n, ncol = p, byrow = TRUE) pred <- predict(fit, newx = x_mat) pred <- t(pred) # 转置 mean((x_mat %% fit$beta - switch(i, y1, y2, y3))^2, na.rm = TRUE) # 修改此处 }) } # 绘制图形 par(mfrow = c(3, 2), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0)) for (i in 1:3) { # CV error plot cv_plot_data <- cv_error[[i]] plot(log10(lambda_seq), cv_plot_data, type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i)) abline(v = log10(cvfit$lambda.min), col = "red") # Prediction error plot pred_plot_data <- pred_error[[i]] plot(log10(lambda_seq), pred_plot_data, type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i)) abline(v = log10(lambda_seq[which.min(pred_plot_data)]), col = "red") }。发生以下错误：Error in h(simpleError(msg, call)) : 在为'mean'函数选择方法时评估'x'参数出了错: non-conformable arguments 。请对原代码进行修正

for (i in 1:3) { # 交叉验证 cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k) cv_error[[i]] <- cvfit$cvm # 岭回归 fit <- glmnet(X, switch(i, y1, y2, y3), ...

# 将树状图添加到距离图中 p1 <- fviz_dist(dist_obs, gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07")) p2 <- fviz_dend(hc, k = 3, cex = 0.5, k_colors = c("#2E9FDF", "#00AFBB", "#E7B800")) p3 <- p1 + p2 报错如下 Error in ggplot_add(): ! Can't add p2 to a <ggplot> object. Run rlang::last_trace() to see where the error occurred.

这个错误可能是因为p1和p2不兼容，无法...p4 <- plot_grid(p1, p2, ncol = 2, align = "h") p4 这将创建一个新的绘图对象p4，其中p1和p2将水平排列在一起。你可以根据需要调整ncol和align参数。

基于以下R代码：# ①建立50×30的随机数据和30个变量 set.seed(123) X <- matrix(rnorm(50*30), ncol=30) y <- rnorm(50) # ②生成三组不同系数的线性模型 beta1 <- rnorm(30, mean=1, sd=0.5) beta2 <- rnorm(30, mean=2, sd=0.5) beta3 <- rnorm(30, mean=3, sd=0.5) # 定义一个函数用于计算线性回归的CV值 cv_linear <- function(X, y, k=10, lambda=NULL) { n <- nrow(X) if (is.null(lambda)) { lambda <- seq(0, 1, length.out=100) } mse <- rep(0, length(lambda)) folds <- sample(rep(1:k, length.out=n)) for (i in 1:k) { X_train <- X[folds!=i, ] y_train <- y[folds!=i] X_test <- X[folds==i, ] y_test <- y[folds==i] for (j in 1:length(lambda)) { fit <- glmnet(X_train, y_train, alpha=0, lambda=lambda[j]) y_pred <- predict(fit, newx=X_test) mse[j] <- mse[j] + mean((y_test - y_pred)^2) } } mse <- mse / k return(mse) } # ③（线性回归中）分别计算这三组的CV值 lambda <- seq(0, 1, length.out=100) mse1 <- cv_linear(X, y, lambda=lambda) mse2 <- cv_linear(X, y, lambda=lambda) mse3 <- cv_linear(X, y, lambda=lambda) # ④（岭回归中）分别画出这三组的两张图，每组两张图均以lambda为横坐标： library(glmnet) par(mfrow=c(2,3)) # 画Beta1的CV error图 plot(lambda, mse1, type="l", xlab="lambda", ylab="CV error", main="Beta1 CV error") # 画Beta1的Prediction error图 fit1 <- glmnet(X, y, alpha=0, lambda=lambda[which.min(mse1)]) y_pred1 <- as.vector(predict(fit1, newx=X)) pred_error1 <- mean((y - y_pred1)^2) lambda <- as.vector(lambda) pred_error1 <- as.vector(pred_error1) if (length(lambda) != length(pred_error1)) { if (length(lambda) > length(pred_error1)) { pred_error1 <- rep(pred_error1, length.out = length(lambda)) } else { lambda <- rep(lambda, length.out = length(pred_error1)) } } plot(lambda, pred_error1, type="l", xlab="lambda", ylab="Prediction error", main="Beta1 Prediction error") # 画Beta2的CV error图 plot(lambda, mse2, type="l", xlab="lambda", ylab="CV error", main="Beta2 CV error") # 画Beta2的Prediction error图 fit2 <- glmnet(X, y, alpha=0, lambda=lambda[which.min(mse2)]) y_pred2 <- predict(fit2, newx=X) pred_error2 <- mean((y - y_pred2)^2) plot(lambda, pred_error2, type="l", xlab="lambda", ylab="Prediction error", main="Beta2 Prediction error") # 画Beta3的CV error图 plot(lambda, mse3, type="l", xlab="lambda", ylab="CV error", main="Beta3 CV error") # 画Beta3的Prediction error图 fit3 <- glmnet(X, y, alpha=0, lambda=lambda[which.min(mse3)]) y_pred3 <- predict(fit3, newx=X) pred_error3 <- mean((y - y_pred3)^2) plot(lambda, pred_error3, type="l", xlab="lambda", ylab="Prediction error", main="Beta3 Prediction error")。对每组的预测误差图进行代码修改

pred_error1 <- as.vector(pred_error1) if (length(lambda) != length(pred_error1)) { if (length(lambda) > length(pred_error1)) { pred_error1 <- rep(pred_error1, length.out = length(lambda)) } else { ...

matlab凑代码-matlab_plot_lattice:matlab_plot_lattice

matlab凑代码写的一些小程序所需软件 MATLAB较近版本。笔者本人用的是MATLAB R2018a。 Python为3.6.1。编码文件内所有中文编码为utf-8。...一般来说，MATLAB和Python都运行主函数即可，具体根据不同的程序决定。...

totalplot<-cowplot::plot_grid(plotlist=plotlist,ncol=1) Error in `geom_line()`: ! Problem while computing aesthetics. ℹ Error occurred in the 2nd layer. Caused by error in `FUN()`: ! object 'EMD' not found Run `rlang::last_trace()` to see where the error occurred.

p_wh <- get_wh(plot = p, unit = "in")

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totalplot<-cowplot::plot_grid(plotlist=plotlist,ncol=1) Error in `geom_line()`: ! Problem while computing aesthetics. ℹ Error occurred in the 2nd layer. Caused by error in `FUN()`: ! object 'EMD' not found Run `rlang::last_trace()` to see where the error occurred.

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> plot_grid(p1, p2, ncol = 2, align = "h", axis = "tb") Error in geom_boxplot(): ! Problem while computing aesthetics. ℹ Error occurred in the 1st layer. Caused by error in FUN(): ! object 'Sepal.Length' not found Run rlang::last_trace() to see where the error occurred.

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