108 lines
2.2 KiB
Go
108 lines
2.2 KiB
Go
package quantile
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import (
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"math"
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"sort"
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"github.com/caio/go-tdigest"
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)
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type algorithm interface {
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Add(value float64) error
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Quantile(q float64) float64
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}
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func newTDigest(compression float64) (algorithm, error) {
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return tdigest.New(tdigest.Compression(compression))
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}
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type exactAlgorithmR7 struct {
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xs []float64
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sorted bool
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}
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func newExactR7(_ float64) (algorithm, error) {
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return &exactAlgorithmR7{xs: make([]float64, 0, 100), sorted: false}, nil
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}
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func (e *exactAlgorithmR7) Add(value float64) error {
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e.xs = append(e.xs, value)
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e.sorted = false
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return nil
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}
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func (e *exactAlgorithmR7) Quantile(q float64) float64 {
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size := len(e.xs)
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// No information
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if len(e.xs) == 0 {
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return math.NaN()
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}
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// Sort the array if necessary
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if !e.sorted {
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sort.Float64s(e.xs)
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e.sorted = true
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}
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// Get the quantile index and the fraction to the neighbor
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// Hyndman & Fan; Sample Quantiles in Statistical Packages; The American Statistician vol 50; pp 361-365; 1996 -- R7
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// Same as Excel and Numpy.
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n := q * (float64(size) - 1)
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i, gamma := math.Modf(n)
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j := int(i)
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if j < 0 {
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return e.xs[0]
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}
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if j >= size {
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return e.xs[size-1]
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}
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// Linear interpolation
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return e.xs[j] + gamma*(e.xs[j+1]-e.xs[j])
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}
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type exactAlgorithmR8 struct {
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xs []float64
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sorted bool
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}
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func newExactR8(_ float64) (algorithm, error) {
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return &exactAlgorithmR8{xs: make([]float64, 0, 100), sorted: false}, nil
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}
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func (e *exactAlgorithmR8) Add(value float64) error {
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e.xs = append(e.xs, value)
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e.sorted = false
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return nil
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}
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func (e *exactAlgorithmR8) Quantile(q float64) float64 {
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size := len(e.xs)
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// No information
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if size == 0 {
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return math.NaN()
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}
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// Sort the array if necessary
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if !e.sorted {
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sort.Float64s(e.xs)
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e.sorted = true
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}
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// Get the quantile index and the fraction to the neighbor
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// Hyndman & Fan; Sample Quantiles in Statistical Packages; The American Statistician vol 50; pp 361-365; 1996 -- R8
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n := q*(float64(size)+1.0/3.0) - (2.0 / 3.0) // Indices are zero-base here but one-based in the paper
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i, gamma := math.Modf(n)
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j := int(i)
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if j < 0 {
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return e.xs[0]
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}
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if j >= size {
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return e.xs[size-1]
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}
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// Linear interpolation
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return e.xs[j] + gamma*(e.xs[j+1]-e.xs[j])
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}
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