mean

问题 I have a football dataset for a season and some variable are: player_id , week and points (a grade for each player in a match). So, each player_id appears several times in my dataset. My goal is to calculate the average points for each player, but just to previous weeks. For example, to the row where player_id=5445 and week=10 , I want the mean when data has player_id=5445 and week is from 1 to 9. I know I can do it filtering data for each row and calculating it. But I hope to do it in a

问题 I would like to aggregate each two cell values by mean and continue with the same process down the column of the dataframe. To be more precise see the following dataframe extract: X Y Z 1 FRI 200101010000 -6.72 2 FRI 200101010030 -6.30 3 FRI 200101010100 -6.26 4 FRI 200101010130 -5.82 5 FRI 200101010200 -5.64 6 FRI 200101010230 -5.29 7 FRI 200101010300 -5.82 8 FRI 200101010330 -5.83 9 FRI 200101010400 -5.83 10 FRI 200101010430 -6.04 11 FRI 200101010500 -5.80 12 FRI 200101010530 -6.09 I would

问题 Below is the code I am currently using to summarize my data and it is working. My question is that I want to actually take the average of the "CE100" column vs the sum. How can I manipulate the below code to do this? library(data.table, warn.conflicts = FALSE) library(magrittr) ### MODIFIED # library(lubridate, warn.conflicts = FALSE) ### MODIFIED ################ ## PARAMETERS ## ################ # Set path of major source folder for raw transaction data in_directory <- "C:/Users/NAME

问题 I'm trying to calculate the mean of all columns in a data frame but I get the error : argument is not numeric or logical: returning NA. Here's the code I'm using: UI.R library(shiny) library(shinydashboard) library(plyr) library(reshape2) #library(data.table) shinyUI(pageWithSidebar( headerPanel("CSV Viewer"), sidebarPanel( fileInput('file1', 'Choose CSV File', accept=c('text/csv', 'text/comma-separated-values,text/plain', '.csv')) ), mainPanel( tableOutput('data'), verbatimTextOutput('mean')

问题 Is there an efficient way to find the centre of mass of a tensor? I'm working with N stacked volumes (Nx64x64x64) and would like to obtain an Nx3 tensor with the x,y,z position of the centre of mass of each 64x64x64 volume 回答1: Following the formula, you should just need to multiply each coordinate by the corresponding mass, sum everything and divide by the total mass: import tensorflow as tf # Input volumes volumes = tf.placeholder(tf.float32, [None, 64, 64, 64]) # Make array of coordinates

问题 In the openCV cheat sheet (C++), I have found the matrix opration mean() . When I use it: float myMatMean = mean( MyMat ); I get the error: no suitable conversion function from "cv::Scalar" to "float" exists What can I do in order to use this data? 回答1: Thanks. The problem was that although myMat was a 2D image. The return type was still a Scalar of size 4 . The solution was cv:Scalar tempVal = mean( myMat ); float myMAtMean = tempVal.val[0]; 来源： https://stackoverflow.com/questions/12687437

问题 I am trying to make a loop to redo a Matlab function 1000 times. Here's the program d = unifrnd (0,10,[10,1]); c = d.^(-2); a = round(unifrnd(0,1,[1,10]); e = a*c btotal = e+1 SIR = 1/btotal What I want is to iterate this function 1000 times, each time the value of SIR will vary due to the random number generated. For every iteration, I want the value of SIR to be added together (summed up), and in the end of the 1000th iteration, find the average SIR(mean). Thanks for the help 回答1: The code

问题 I have a data like below : > dplyr::tbl_df(sbp) Country X1980 X1981 X1982 X1983 X1984 X1985 Albania 132.9270 133.0296 133.1459 133.1868 133.2048 133.2577 Algeria 132.4093 132.1710 131.9649 131.7835 131.6161 131.4345 Andorra 140.8585 140.1076 139.3727 138.6457 137.9525 137.3192 I want to get mean of values for each year for all countries and add a row like World to the end of the dataframe, so that I can plot the change of the mean value through years, in that format. I tried using gather() so

问题 The time series model is expressed as y(t) = 0.5 + 0.3y(t-1) + n(t) where n(t) = 0.1*randn(500,1) for t=1,2,...,500 Slides contain the Correlation and covariance matrix. The formula for correlation is: E[y(t)*y(t)^T] which can be invoked by using xcorr . I would like to know how one can calculate the individual Correlation matrix for its lagged version E[y(t-1)*y(t-1)^T] without using the inbuilt commands so that I can finally implement the following expression trace([E[y(t-1)*y(t-1)']]^-1)

问题 Assume I have a dataset like this: userID productID rating a i 5 b i 4 c i 4 a j 3 b j 5 The question is, how can I calculate the mean rating of each user? I saw this answer, but I didn't quite understand it. I would really appreciate your time, if you show some guidance. 回答1: I work in an IPython Notebook. Let's assume you have this file user_ratings.csv : userID productID rating a i 5 b i 4 c i 4 a j 3 b j 5 The example in the link uses pandas. So import pandas: In [1]: import pandas as pd