pearson | 易学教程

Scipy: Pearson's correlation always returning 1

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问题 I am using Python library scipy to calculate Pearson's correlation for two float arrays. The returned value for coefficient is always 1.0, even if the arrays are different. For example: [-0.65499887 2.34644428] [-1.46049758 3.86537321] I am calling the routine in this way: r_row, p_value = scipy.stats.pearsonr(array1, array2) The value of r_row is always 1.0. What am I doing wrong? 回答1: Pearson's correlation coefficient is a measure of how well your data would be fitted by a linear regression

落地案例｜使用 Kubernetes 重新部署全球最大的教育公司

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Pearson 是一家全球教育公司，对象面向全世界，目前公司用户已经达到 750 万。Pearson 接下来的目标是 2025 年在线用户人数达到 2000 万。要达到这样的增长，关键因素在于数字化学习体验，这就需要一个能够快速缩放，更快交付产品到市场的基础设施平台。为了满足这样的业务需求，Pearson 的云技术团队选择了 Kubernetes 来帮助构建这个平台。 “基础设施要进行转型，我们考虑的是启用自动配置，我们意识到必须要建立一个平台，Pearson 开发人员在这上面可以用全新的方式来创建管理部署应用程序。我们选择 Kubernetes 是因为它灵活，便于管理，还能够提升工程师的生产力。” ——Chris Jackson，Pearson 云产品工程部门主管面临的挑战面对在线学员人数的增长，Pearson 遇到了困难。他们想把网络作为制作、分发课程的主要途径。为什么选择 Kubernetes Kubernetes 允许 Pearson 的团队用一致的方式来开发他们的应用，而且节省时间，降低了复杂性。途径创建一个全企业通用的中心化平台。使用容器技术作为平台的核心。部署 Kubernetes 来管理该平台。结果 Pearson 正在创建一个企业级的平台，为的是交付创新的、基于网络的教育内容。他们期待工程师的生产力能够提升 20%。 Kubernetes

pandas.DataFrame.corr——计算列之间相关性

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DataFrame.corr(self, method=‘pearson’, min_periods=1) API 作用：计算列之间的相关性，不包括缺省值参数说明： method：可选值为{‘pearson’, ‘kendall’, ‘spearman’} pearson：Pearson相关系数来衡量两个数据集合是否在一条线上面，即针对线性数据的相关系数计算，针对非线性数据便会有误差。 kendall：用于反映分类变量相关性的指标，即针对无序序列的相关系数，非正太分布的数据 spearman：非线性的，非正太分析的数据的相关系数 min_periods：样本最少的数据量返回值：各类型之间的相关系数DataFrame 表格。原文链接：https://blog.csdn.net/walking_visitor/article/details/85128461 来源： CSDN 作者：凯旋的铁铁链接： https://blog.csdn.net/qq_41870157/article/details/104678106

Constructing correlated variables

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问题 I have a variable with a given distribution (normale in my below example). set.seed(32) var1 = rnorm(100,mean=0,sd=1) I want to create a variable (var2) that is correlated to var1 with a linear correlation coefficient (roughly or exactly) equals to "Corr". The slope of regression between var1 and var2 should (rougly or exactly) equals 1. Corr = 0.3 How can I achieve this? I wanted to do something like this: decorelation = rnorm(100,mean=0,sd=1-Corr) var2 = var1 + decorelation But of course

Why Pearson correlation output is NaN?

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问题 I'm trying to get the Pearson correlation coefficient between to variables in R. This is the scatterplot of the variables: ggplot(results_summary, aes(x =D_in, y = D_ex)) + geom_point(col=ifelse(results_summary$FDR < 0.05, ifelse(results_summary$logF>0, "red", "green" ), "black")) As you can see, the variables correlate pretty well, so I'm expecting a high correlation coefficient. However when I try to get the Pearson correlation coefficient I'm getting a NaN! > cor(results_summary$D_in,

Why Pearson correlation output is NaN?

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What is wrong with the pearson algorithm from “Programming Collective Intelligence”?

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问题 This function is from the book "Programming Collective Intelligence”, and is supposed to calculate the Pearson correlation coefficient for p1 and p2, which is supposed to be a number between -1 and 1. If two critics rate items very similarly the function should return 1, or close to 1. With real user data I sometimes get weird results. In the following example the dataset critics2 should return 1 - instead it returns 0. Does anyone spot a mistake? (This is not a duplicate of What is wrong

How is NaN handled in Pearson correlation user-user similarity matrix in a recommender system?

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问题 I am generating a user-user similarity matrix from a user-rating data (particularly MovieLens100K data). Computing correlation leads to some NaN values. I have tested in a smaller dataset: User-Item rating matrix I1 I2 I3 I4 U1 4 0 5 5 U2 4 2 1 0 U3 3 0 2 4 U4 4 4 0 0 User-User Pearson Correlation similarity matrix U1 U2 U3 U4 U5 U1 1 -1 0 -nan 0.755929 U2 -1 1 1 -nan -0.327327 U3 0 1 1 -nan 0.654654 U4 -nan -nan -nan -nan -nan U5 0.755929 -0.327327 0.654654 -nan 1 For computing the pearson

Pearson's Coefficient and Covariance calculation in Matlab

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问题 I want to calculate Pearson's correlation coefficent in Matlab (without using Matlab's corr function). Simply, I have two vectors A and B (each of them is 1x100) and I am trying to calculate the Pearson's coefficient like this: P = cov(x, y)/std(x, 1)std(y,1) I am using Matlab's cov and std functions. What I don't get is, the cov function returns me a square matrix like this: corrAB = 0.8000 0.2000 0.2000 4.8000 But I expect a single number as the covariance so I can come up with a single P

How to generate correlation plot of my data.frame in R?

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问题 It might be a simple question. I have a df and I want to generate a correlation plot for my data in R. head(df) x y 1 -0.10967469 1 2 1.06814661 93 3 0.71805993 46 4 0.60566332 84 5 0.73714006 12 6 -0.06029712 5 I've found a package called corPlot and I've generated two plots based on pearson & spearman methods. corPlot(df, method = 'pearson') corPlot(df, method = 'spearman') here is my output with pearson method: I wondered if there is another package to generate the same correlation plots