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Fixed spelling of Kullback-Leibler.

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btracey
2014-09-01 10:19:05 -07:00
parent d12dd51730
commit fae354edd8
2 changed files with 10 additions and 10 deletions
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8
stat.go
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@@ -328,12 +328,12 @@ func Histogram(count, dividers, x, weights []float64) []float64 {
return count return count
} }
// KulbeckLeibler computes the Kulbeck-Leibler distance between the // KullbackLeibler computes the Kullback-Leibler distance between the
// distributions p and q. The natural logarithm is used. // distributions p and q. The natural logarithm is used.
// sum_i(p_i * log(p_i / q_i)) // sum_i(p_i * log(p_i / q_i))
// Note that the Kulbeck-Leibler distance is not symmetric; // Note that the Kullback-Leibler distance is not symmetric;
// KulbeckLeibler(p,q) != KulbeckLeibler(q,p) // KullbackLeibler(p,q) != KullbackLeibler(q,p)
func KulbeckLeibler(p, q []float64) float64 { func KullbackLeibler(p, q []float64) float64 {
if len(p) != len(q) { if len(p) != len(q) {
panic("stat: slice length mismatch") panic("stat: slice length mismatch")
} }

12
stat_test.go
View File

@@ -320,24 +320,24 @@ the count field in order to avoid extra garbage`)
// Weighted Hist = [77 175 275 375 423 627 675 775 783 1067] // Weighted Hist = [77 175 275 375 423 627 675 775 783 1067]
} }
func ExampleKulbeckLiebler() { func ExampleKullbackLeibler() {
p := []float64{0.05, 0.1, 0.9, 0.05} p := []float64{0.05, 0.1, 0.9, 0.05}
q := []float64{0.2, 0.4, 0.25, 0.15} q := []float64{0.2, 0.4, 0.25, 0.15}
s := []float64{0, 0, 1, 0} s := []float64{0, 0, 1, 0}
klPQ := KulbeckLeibler(p, q) klPQ := KullbackLeibler(p, q)
klPS := KulbeckLeibler(p, s) klPS := KullbackLeibler(p, s)
klPP := KulbeckLeibler(p, p) klPP := KullbackLeibler(p, p)
fmt.Println("Kulbeck-Liebler is one measure of the difference between two distributions") fmt.Println("Kullback-Leibler is one measure of the difference between two distributions")
fmt.Printf("The K-L distance between p and q is %.4f\n", klPQ) fmt.Printf("The K-L distance between p and q is %.4f\n", klPQ)
fmt.Println("It is impossible for s and p to be the same distribution, because") fmt.Println("It is impossible for s and p to be the same distribution, because")
fmt.Println("the first bucket has zero probability in s and non-zero in p. Thus,") fmt.Println("the first bucket has zero probability in s and non-zero in p. Thus,")
fmt.Printf("the K-L distance between them is %.4f\n", klPS) fmt.Printf("the K-L distance between them is %.4f\n", klPS)
fmt.Printf("The K-L distance between identical distributions is %.4f\n", klPP) fmt.Printf("The K-L distance between identical distributions is %.4f\n", klPP)
// Kulbeck-Liebler is one measure of the difference between two distributions // Kullback-Leibler is one measure of the difference between two distributions
// The K-L distance between p and q is 0.8900 // The K-L distance between p and q is 0.8900
// It is impossible for s and p to be the same distribution, because // It is impossible for s and p to be the same distribution, because
// the first bucket has zero probability in s and non-zero in p. Thus, // the first bucket has zero probability in s and non-zero in p. Thus,