weighted

I'm facing a problem that I hope some others have faced before because I can't find a way out ! I have a regular triangulation in CGAL in which I wish to insert some weighted points with info std::pair<myweightpoint, myinfo> one by one and to get the handle to the vertex ( Vertex_handle ) once it is inserted ! The thing is that there is no such function. It exists several functions to insert : Vertex_handle Regular_triangulation::insert ( const Weighted_point & p ) ; That returns a Vertex_handle which is cool but does not take weighted points WITH INFO which is very important for me and what I

I need to calculate the frequency of individuals by age and marital status so normally I'd use: table(age, marital_status) However each individual has a different weight after the sampling of the data. How do I incorporate this into my frequency table? You can use function svytable from package survey , or wtd.table from rgrs . EDIT : rgrs is now called questionr : df <- data.frame(var = c("A", "A", "B", "B"), wt = c(30, 10, 20, 40)) library(questionr) wtd.table(x = df$var, weights = df$wt) # A B # 40 60 That's also possible with dplyr : library(dplyr) count(x = df, var, wt = wt) # # A tibble:

I have a program where I'm keeping track of the success of various things using collections.Counter — each success of a thing increments the corresponding counter: import collections scoreboard = collections.Counter() if test(thing): scoreboard[thing]+ = 1 Then, for future tests, I want to skew towards things which have generated the most success. Counter.elements() seemed ideal for this, since it returns the elements (in arbitrary order) repeated a number of times equal to the count. So I figured I could just do: import random nextthing=random.choice(scoreboard.elements()) But no, that raises

I have an array of doubles and I want to select a value from it with the probability of each value being selected being inversely proportional to its value. For example: arr[0] = 100 arr[1] = 200 In this example, element 0 would have a 66% of being selected and element 1 a 33% chance. I am having difficulty coding this. What I have done so far is to calculate the total value of the array (the example would be 300), then I've played around with inversing the numbers before calculating them as a percentage of the total. I can't get anything to work. In the end I desire: new randomNumber for(int

How can I calculate the weighted coefficient of variation (CV) over a NumPy array in Python? It's okay to use any popular third-party Python package for this purpose. I can calculate the CV using scipy.stats.variation , but it's not weighted. import numpy as np from scipy.stats import variation arr = np.arange(-5, 5) weights = np.arange(9, -1, -1) # Same size as arr cv = abs(variation(arr)) # Isn't weighted This can be done using the statsmodels.stats.weightstats.DescrStatsW class in the statsmodels package for calculating weighted statistics . from statsmodels.stats.weightstats import

I am looking for a solution to compute weighted sum of some variables by groups with data.table. I hope the example is clear enough. require(data.table) dt <- data.table(matrix(1:200, nrow = 10)) dt[, gr := c(rep(1,5), rep(2,5))] dt[, w := 2] # Error: object 'w' not found dt[, lapply(.SD, function(x) sum(x * w)), .SDcols = paste0("V", 1:4)] # Error: object 'w' not found dt[, lapply(.SD * w, sum), .SDcols = paste0("V", 1:4)] # This works with out groups dt[, lapply(.SD, function(x) sum(x * dt$w)), .SDcols = paste0("V", 1:4)] # It does not work by groups dt[, lapply(.SD, function(x) sum(x * dt$w