Welford - Online method of calculating variance and standard deviation
Go implementation Welford’s method for one-pass variance computation with D. H. D. West improved methods.
Highlights:
- Merging of several multiple sets of statistics
- Add weighted values
Abstract
A method of improved efficiency is given for updating the mean and variance of weighted sampled data when an additional data value is included in the set. Evidence is presented that the method is stable and at least as accurate as the best existing updating method.
Example Usage
0.5 variance := s.Variance() // ==> 0.3 stdev := s.StandardDeviation() // ==> 0.5477225575051661 variancp := s.VariancePpopulation() // ==> 0.25 stdevp := s.StandardDeviationPopulation() // ==> 0.5 n := s.NumDataValues()) // ==> 6 sts2 := welford.New() sts2.Add(3) sts1.Merge(sts2) // merge sts1 into sts2 sts2.Clear() // resets the state os sts2 } ">
package welford_example import "github.com/axiomhq/welford" func Example() { sts1 := welford.New() sts1.Add(1) sts1.Add(1) sts1.Add(1) sts1.Add(0) sts1.Add(0) sts1.Add(0) mean := s.Mean() // ==> 0.5 variance := s.Variance() // ==> 0.3 stdev := s.StandardDeviation() // ==> 0.5477225575051661 variancp := s.VariancePpopulation() // ==> 0.25 stdevp := s.StandardDeviationPopulation() // ==> 0.5 n := s.NumDataValues()) // ==> 6 sts2 := welford.New() sts2.Add(3) sts1.Merge(sts2) // merge sts1 into sts2 sts2.Clear() // resets the state os sts2 }
