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package stats
import (
"math"
)
// Correlation describes the degree of relationship between two sets of data
func Correlation(data1, data2 Float64Data) (float64, error) {
l1 := data1.Len()
l2 := data2.Len()
if l1 == 0 || l2 == 0 {
return math.NaN(), EmptyInputErr
}
if l1 != l2 {
return math.NaN(), SizeErr
}
sdev1, _ := StandardDeviationPopulation(data1)
sdev2, _ := StandardDeviationPopulation(data2)
if sdev1 == 0 || sdev2 == 0 {
return 0, nil
}
covp, _ := CovariancePopulation(data1, data2)
return covp / (sdev1 * sdev2), nil
}
// Pearson calculates the Pearson product-moment correlation coefficient between two variables
func Pearson(data1, data2 Float64Data) (float64, error) {
return Correlation(data1, data2)
}
// AutoCorrelation is the correlation of a signal with a delayed copy of itself as a function of delay
func AutoCorrelation(data Float64Data, lags int) (float64, error) {
if len(data) < 1 {
return 0, EmptyInputErr
}
mean, _ := Mean(data)
var result, q float64
for i := 0; i < lags; i++ {
v := (data[0] - mean) * (data[0] - mean)
for i := 1; i < len(data); i++ {
delta0 := data[i-1] - mean
delta1 := data[i] - mean
q += (delta0*delta1 - q) / float64(i+1)
v += (delta1*delta1 - v) / float64(i+1)
}
result = q / v
}
return result, nil
}
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