data frame with sample probabilities # in your case the rows are 1000 and the columns 4, # but it is just to show the procedure samp_prob <- data.frame(A = rep(.25, 4), B = c(.5, .1, .2, .2), C = c(.3, .6, .05, .05)) data frame of values to sample...

You can use the using parameter to specify a column for the errors. With three using specifiers, the third one is interpreted as standard deviation s and is used to compute a weight 1/s**2 for the corresponding value: f(x) = mean_y fit f(x) "data" using 1:2:3 via mean_y That assumes,...

algorithm,theory,graph-theory,weighted

This is an NP-hard optimization problem. For example, the Partition problem can be reduced into this easily (the planarity property does not cause a problem). So an algorithm that calculates an optimal solution (and you seem to ask for an optimal solution in your comment) is unlikely to be practical...

python,numpy,statistics,mean,weighted

You actually have 2 different questions. How to make data discrete, and How to make a weighted average. It's usually better to ask 1 question at a time, but anyway. Given your specification: xmin = -100 xmax = 100 binsize = 20 First, let's import numpy and make some data:...

java,graph,nullpointerexception,weighted

You're shadowing the variable Latitude (and Longitude) double[] Latitude = new double[size]; should be Latitude = new double[size]; Note: Java naming conventions suggest that variables start with a lowercase letter, e.g. latitude and longitude...