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