pearson

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 with this python function from “Programming Collective Intelligence” ) from __future__ import division

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 (pearson's coefficient) number. What is the point I'm missing? I think you're just confused with

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): 由 翻译 强力驱动 问题: I am trying to generate a table output of a correlation matrix. Specifically, I am using a for loop in order to identify a correlation between all data in columns 4:40 to column 1. While the results of the table are decent, it does not identify what is being compared to what. In checking attributes of cor.test ,I find that data.name is being given as x[1] and y[1] which is not good enough to trace back which columns is being compared to what. Here is my code: input <- read . delim ( file = "InputData.txt" , header = TRUE ) x <-

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 when running: cor(var1,var2) The result is not close to Corr! I did something similar a while ago. I am

This is the function in question. It calculates the Pearson correlation coefficient for p1 and p2, which is supposed to be a number between -1 and 1. When I use this with real user data, it sometimes returns a number greater than 1, like in this example: def sim_pearson(prefs,p1,p2): si={} for item in prefs[p1]: if item in prefs[p2]: si[item]=1 if len(si)==0: return 0 n=len(si) sum1=sum([prefs[p1][it] for it in si]) sum2=sum([prefs[p2][it] for it in si]) sum1Sq=sum([pow(prefs[p1][it],2) for it in si]) sum2Sq=sum([pow(prefs[p2][it],2) for it in si]) pSum=sum([prefs[p1][it]*prefs[p2][it] for it