mean

I have a data set that looks like this: id a b 1 AA 2 1 AB 5 1 AA 1 2 AB 2 2 AB 4 3 AB 4 3 AB 3 3 AA 1 I need to calculate the cumulative mean for each record within each group and excluding the case where a == 'AA' , So sample output should be: id a b mean 1 AA 2 - 1 AB 5 5 1 AA 1 5 2 AB 2 2 2 AB 4 (4+2)/2 3 AB 4 4 3 AB 3 (4+3)/2 3 AA 1 (4+3)/2 3 AA 4 (4+3)/2 I tried to achieve it using dplyr and cummean by getting an error. df <- df %>% group_by(id) %>% mutate(mean = cummean(b[a != 'AA'])) Error: incompatible size (123), expecting 147 (the group size) or 1 Can you suggest a better way to

For simplicity say we have a sample set of possible scores {0, 1, 2}. Is there a way to calculate a mean based on the number of scores without getting into hairy lookup tables etc for a 95% confidence interval calculation? dreeves posted a solution to this here: How can I calculate a fair overall game score based on a variable number of matches? Now say we have 2 scenarios ... Scenario A) 2 votes of value 2 result in SE=0 resulting in the mean to be 2 Scenario B) 10000 votes of value 2 result in SE=0 resulting in the mean to be 2 I wanted Scenario A to be some value less than 2 because of the

How do I deploy a MEAN STACK+WEBPAC application? I have a MEAN Stack + Webpac application and nodejs server which provides the rest api I run the angular application using webpack dev server after building the webpack application,I have the build.js file. How do I reference the build file from the nodejs application? Normally,with requirejs, I would use the html script tag, this way <script src="build.js" /> I understand this is not the webpac way, Generally speaking, we can leverage Custom Deployment Script to install the nodejs modules and run custom scripts during the Azure Deployment task,

I tried searching a function for matlab that gives the average neighbor degree of a graph. There is a function for the same in python in network-X package. So i was wondering if there's a similar function in matlab. ***********Edit**************** I cannot convert it to an adjacency matrix.. this will occupy too much of space actually. What i have is the following edge list(Actually this is just a test matrix.. the actual one is pretty large ) as in there's an edge between node 2 to node 1 and so on.. and yes this is an un-directed graph 2 1 3 1 4 1 5 1 1 2 3 2 4 2 1 3 2 3 5 3 1 4 2 4 5 4 1 5

看过前面的例子，会发现实现深度神经网络需要使用 tensorflow.nn 这个核心模块。我们通过源码来一探究竟。 1 # Copyright 2015 Google Inc. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the

user schema: var UserSchema = new Schema({ review_likes : [{type:String}], review_dislikes : [{type:String}] }); review schema: var ReviewSchema = new Schema({ productID:{type: String, required: true}, numoflikes:{type:Number, required:true}, numofdislikes:{type:Number, required:true} }) review controller: .controller('reviewsController', function($route, reviewsFactory, $scope, $routeParams){ var that=this; reviewid = $routeParams.id; productID = $routeParams.id; likestats = false; dislikestats = false; /* console.log('controller',questionid) */ var getallReviews = function(){ reviewsFactory

In an effort to be as lazy as possible I read in a matrix as vector< vector<double> > data ( rows, vector<double> ( columns ) ); and try to use as many STL goodies as I can. One thing I need to do next is to compute the row means. In C-style programming that would be vector<double> rowmeans( data.size() ); for ( int i=0; i<data.size(); i++ ) for ( int j=0; j<data[i].size(); j++ ) rowmeans[i] += data[i][j]/data[i].size(); In In C++, how to compute the mean of a vector of integers using a vector view and gsl_stats_mean? it is explained that for a vector of numbers you can compute a vector mean