forked from golang/hotime
106 lines
2.3 KiB
Go
106 lines
2.3 KiB
Go
package stats
|
|
|
|
import "math"
|
|
|
|
// _variance finds the variance for both population and sample data
|
|
func _variance(input Float64Data, sample int) (variance float64, err error) {
|
|
|
|
if input.Len() == 0 {
|
|
return math.NaN(), EmptyInput
|
|
}
|
|
|
|
// Sum the square of the mean subtracted from each number
|
|
m, _ := Mean(input)
|
|
|
|
for _, n := range input {
|
|
variance += (float64(n) - m) * (float64(n) - m)
|
|
}
|
|
|
|
// When getting the mean of the squared differences
|
|
// "sample" will allow us to know if it's a sample
|
|
// or population and wether to subtract by one or not
|
|
return variance / float64((input.Len() - (1 * sample))), nil
|
|
}
|
|
|
|
// Variance the amount of variation in the dataset
|
|
func Variance(input Float64Data) (sdev float64, err error) {
|
|
return PopulationVariance(input)
|
|
}
|
|
|
|
// PopulationVariance finds the amount of variance within a population
|
|
func PopulationVariance(input Float64Data) (pvar float64, err error) {
|
|
|
|
v, err := _variance(input, 0)
|
|
if err != nil {
|
|
return math.NaN(), err
|
|
}
|
|
|
|
return v, nil
|
|
}
|
|
|
|
// SampleVariance finds the amount of variance within a sample
|
|
func SampleVariance(input Float64Data) (svar float64, err error) {
|
|
|
|
v, err := _variance(input, 1)
|
|
if err != nil {
|
|
return math.NaN(), err
|
|
}
|
|
|
|
return v, nil
|
|
}
|
|
|
|
// Covariance is a measure of how much two sets of data change
|
|
func Covariance(data1, data2 Float64Data) (float64, error) {
|
|
|
|
l1 := data1.Len()
|
|
l2 := data2.Len()
|
|
|
|
if l1 == 0 || l2 == 0 {
|
|
return math.NaN(), EmptyInput
|
|
}
|
|
|
|
if l1 != l2 {
|
|
return math.NaN(), SizeErr
|
|
}
|
|
|
|
m1, _ := Mean(data1)
|
|
m2, _ := Mean(data2)
|
|
|
|
// Calculate sum of squares
|
|
var ss float64
|
|
for i := 0; i < l1; i++ {
|
|
delta1 := (data1.Get(i) - m1)
|
|
delta2 := (data2.Get(i) - m2)
|
|
ss += (delta1*delta2 - ss) / float64(i+1)
|
|
}
|
|
|
|
return ss * float64(l1) / float64(l1-1), nil
|
|
}
|
|
|
|
// CovariancePopulation computes covariance for entire population between two variables.
|
|
func CovariancePopulation(data1, data2 Float64Data) (float64, error) {
|
|
|
|
l1 := data1.Len()
|
|
l2 := data2.Len()
|
|
|
|
if l1 == 0 || l2 == 0 {
|
|
return math.NaN(), EmptyInput
|
|
}
|
|
|
|
if l1 != l2 {
|
|
return math.NaN(), SizeErr
|
|
}
|
|
|
|
m1, _ := Mean(data1)
|
|
m2, _ := Mean(data2)
|
|
|
|
var s float64
|
|
for i := 0; i < l1; i++ {
|
|
delta1 := (data1.Get(i) - m1)
|
|
delta2 := (data2.Get(i) - m2)
|
|
s += delta1 * delta2
|
|
}
|
|
|
|
return s / float64(l1), nil
|
|
}
|