iris

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to make code iris plugin work on Android studio. I press right click -> Create Code Iris graph and then I get a notification that my graph is ready. But I do not know when this graph is stored, what is the name of the file created and how to open it. Any ideas? 回答1: Complete Guidance of CODE IRIS Graph Creation:- You have to generate Code Iris by just right clicking on project, and then select "Create Code Iris Graph" , (Check the snapshot below) Now your graph will be created, you can get the graph on the right side of Android

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: stargazer automatically centres values within tables. How can I left align the columns? Put this code in an .Rnw file and use knitr to convert to .tex: <<load, echo=FALSE, warning=FALSE, message=FALSE>>= opts_chunk$set(eval=TRUE, echo=FALSE, warning=FALSE, message=FALSE, dpi=300) @ \documentclass[a4paper,11pt]{article} \usepackage{lipsum} % Required to insert dummy text \begin{document} \title{} \author{} \date{\today} \maketitle \section{Header} Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am using a seaborn pairplot to plot a scatter plot of different dimensions of my datapoints. However, I want the markers of the datapoints to have a size that corresponds to one of the dimensions of the datapoints. I have the following code: markersize = 1000* my_dataframe['dim_size'] / sum(my_dataframe['dim_size']) sns.set_context("notebook", font_scale=1.5, rc={'figure.figsize': [11, 8]}) sns.set_style("darkgrid", {"axes.facecolor": ".9"}) kws = dict(s=markersize, linewidth=.5, edgecolor="w") sbax = sns.pairplot(my_dataframe, hue='dim

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am doing some data processing of some large tables stored in sqlserver that creating an index sometimes reduce the time needed for some R script to run. I try to use the mutate function of dplyr to create a new column ( idx ) with consecutive number, then use that idx column as index. But the mutate function seems not working and constantly give me this error: > tbl(channel,'tbl_iris') %>% mutate(idx=1:n()) Error in from:to : NA/NaN argument In addition: Warning message: In 1:n() : NAs introduced by coercion\ Right now I am doing something

# Iris鸢尾花数据集是常用的分类实验数据集，由Fisher, 1936收集整理。 # 是一类多重变量分析的数据集。分为3类，每类50个数据，每个数据包含4个属性。 # 可通过4个属性预测鸢尾花属于（Setosa，Versicolour，Virginica）三个种类中的哪一类。 sklearn决策树 from sklearn import datasets,tree import numpy as np #载入数据集 iris=datasets.load_iris() iris_data=iris['data'] iris_label=iris['target'] X=np.array(iris_data) Y=np.array(iris_label) #训练 clf=tree.DecisionTreeClassifier(max_depth=5) clf.fit(X,Y) #预测 print clf.predict([[4.1, 2.2, 2.3, 5.4]]) sklearn随机森林 from sklearn import datasets, ensemble import numpy as np iris=datasets.load_iris() iris_data=iris['data'] iris_label=iris['target'] X=np.array(iris

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: Usually when I do dendrograms and heatmaps, I use a distance matrix and do a bunch of SciPy stuff. I want to try out Seaborn but Seaborn wants my data in rectangular form (rows=samples, cols=attributes, not a distance matrix)? I essentially want to use seaborn as the backend to compute my dendrogram and tack it on to my heatmap. Is this possible? If not, can this be a feature in the future. Maybe there are parameters I can adjust so it can take a distance matrix instead of a rectangular matrix? Here's the usage: My code below: from sklearn

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: (Somewhat related question: Enter new column names as string in dplyr's rename function ) In the middle of a dplyr chain ( %>% ), I would like to replace multiple column names with functions of their old names (using tolower or gsub , etc.) library(tidyr); library(dplyr) data(iris) # This is what I want to do, but I'd like to use dplyr syntax names(iris) % gather(measurement, value, -species) %>% group_by(species,measurement) %>% summarise(avg_value = mean(value)) I see ?rename takes the argument replace as a named character vector, with new

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I would like to use cross validation to test/train my dataset and evaluate the performance of the logistic regression model on the entire dataset and not only on the test set (e.g. 25%). These concepts are totally new to me and am not very sure if am doing it right. I would be grateful if anyone could advise me on the right steps to take where I have gone wrong. Part of my code is shown below. Also, how can I plot ROCs for "y2" and "y3" on the same graph with the current one? Thank you import pandas as pd Data=pd.read_csv ('C:\\Dataset.csv'

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: When using R it's handy to load "practice" datasets using data(iris) or data(mtcars) Is there something similar for Pandas? I know I can load using any other method, just curious if there's anything builtin 回答1: The rpy2 module is made for this: from rpy2.robjects import r, pandas2ri pandas2ri.activate() r['iris'].head() yields Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa Up to pandas 0.19 you could use

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm trying to use the dplyr package to apply a function to all columns in a data.frame that are not being grouped, which I would do with aggregate() : aggregate(. ~ Species, data = iris, mean) where mean is applied to all columns not used for grouping. (Yes, I know I can use aggregate, but I'm trying to understand dplyr.) I can use summarize like this: species <- group_by(iris, Species) summarize(species, Sepal.Length = mean(Sepal.Length), Sepal.Width = mean(Sepal.Width)) But is there a way to have mean() applied to all columns that are not