variance

given several vectors: x1 = [3 4 6] x2 = [2 8 1] x3 = [5 5 4] x4 = [6 2 1] I wanna find weight w1, w2, w3 to each item, and get the weighted sum of each vector: yi = w1*i1 + w2*i2 + w3*i3 . for example, y1 = 3*w1 + 4*w2 + 6*w3 to make the variance of these values(y1, y2, y3, y4) to be minimized. notice: w1, w2, w3 should > 0, and w1 + w2 + w3 = 1 I don't know what kind of problems it should be... and how to solve it in python or matlab? You can start with building a loss function stating the variance and the constraints on w 's. The mean is m = (1/4)*(y1 + y2 + y3 + y4) . The variance is then

This question already has an answer here: Quickly remove zero variance variables from a data.frame 8 answers How to exclude from data matrix of nearly 1,000 variables the variables/columns with variance equal to 0 (zero) (i.e. all the cases/observations in the variable/column have the same value)? I can imagine calculate variances for each column and then manually write the numbers of columns to be excluded (or included as this seems to be easier to do in R). But sure there is a more elegant and time saving solution in R. Thank you in advance! We can use Filter Filter(var, df1) caret package

I haven't really used variance calculation that much, and I don't know quite what to expect. Actually I'm not too good with math at all. I have a an array of 1000000 random numeric values in the range 0-10000. The array could grow even larger, so I use 64 bit int for sum. I have tried to find code on how to calc variance, but I don't know if I get correct output. The mean is 4692 and median is 4533. I get variance 1483780.469308 using the following code: // size is the element count, in this case 1000000 // value_sum is __int64 double p2 = pow( (double)(value_sum - (value_sum/size)), (double)2

I wanted to create an observableCollection that is sortable so i started creating a class that inherit observable with some methods to sort it, then i wanted that class to persist the index into the childs, so i created an interface that expose an index property where i can write to, and i costrainted the T of my collection class to be of my Interface, then i wanted to be able from avery item to access the parentCollection and here the problems started because the type of the parent collection is generic ... i've tried many solutions, and i think covariance or invariance is the way, but i can

In a Python program, I need to generate normally-distributed random numbers with a specific, user-controlled variance. How can I do this? import math from random import gauss my_mean = 0 my_variance = 10 random_numbers = [gauss(my_mean, math.sqrt(my_variance)) for i in range(100)] This gets you 100 normally-distributed random numbers with mean 0 and variance 10. Use random.normalvariate (or random.gauss if you don't need thread-safety), and set the sigma argument to the square root of the variance. 来源： https://stackoverflow.com/questions/8815706/random-number-with-specific-variance-in-python

Suppose I want to add functionality like map to a Scala List , something along the lines of list mapmap f , which applies the function f to each element of list twice. (A more serious example might be implementing a parallel or distributed map, but I don't want to get distracted by details in that direction.) My first approach would be object MapMap { implicit def createFancyList[A](list: List[A]) = new Object { def mapmap(f: A => A): List[A] = { list map { a: A => f(f(a)) } } } } this now works great scala> import MapMap._ import MapMap._ scala> List(1,2,3) mapmap { _ + 1 } res1: List[Int] =

I can't find out how should I put up this ques so I used this method. I have a latitude-longitude dataset. The image posted below is what I want to produce.. This is my dataset: Latitude Longitude 21.06941667 71.07952778 21.06941667 71.07952778 21.06666667 71.08158333 21.07186111 71.08688889 21.08625 71.07083333 21.08719444 71.07286111 21.08580556 71.07686111 21.07894444 71.08225 .... I have used geom_path() to find the path. Now, As shown in fig. I have highlighted the variance with white color around the path which I want to do. This is how I calculated variance: var.latitude <- var(Data

If I have a list like this: results=[-14.82381293, -0.29423447, -13.56067979, -1.6288903, -0.31632439, 0.53459687, -1.34069996, -1.61042692, -4.03220519, -0.24332097] I want to calculate the variance of this list in Python which is the average of the squared differences from the mean. How can I go about this? Accessing the elements in the list to do the computations is confusing me for getting the square differences. You can use numpy's built-in function var : import numpy as np results = [-14.82381293, -0.29423447, -13.56067979, -1.6288903, -0.31632439, 0.53459687, -1.34069996, -1.61042692,