use Damerau-Levenshtein distance

Former-commit-id: 073f2be728690d9ef8662bb9960b91424694d12d [formerly 073f2be728690d9ef8662bb9960b91424694d12d [formerly f9f4e84e4d4b771b0eb9d7a9965bf8d7d246e91a [formerly c913a0221468ea66309d376cd7aa6e5ee94eefbe]]]
Former-commit-id: b65b58d2e287b62da016134632cd51281d4fafc3
Former-commit-id: a33c88925474e837abe5061fbd4a7930879f7230 [formerly 2905c2ed4801d650acd6f94261c553a33f2cfc80]
Former-commit-id: 5a892a6304228629f31bd7f10040f1b2150d5ddb

README.md

@@ -104,8 +104,8 @@ Key|Action
 <kbd>Space</kbd>|Alias to enter key
 <kbd>Ctrl</kbd>+<kbd>c</kbd>|Alias to quit
 <kbd>Ctrl</kbd>+<kbd>d</kbd>|Jump page down (vim style)
-<kbd>Ctrl</kbd>+<kbd>n</kbd>|Go to next page (vim style)
-<kbd>Ctrl</kbd>+<kbd>p</kbd>|Go to previous page (vim style)
+<kbd>Ctrl</kbd>+<kbd>n</kbd>|Go to next page
+<kbd>Ctrl</kbd>+<kbd>p</kbd>|Go to previous page
 <kbd>Ctrl</kbd>+<kbd>r</kbd>|Force refresh
 <kbd>Ctrl</kbd>+<kbd>u</kbd>|Jump page up (vim style)
 <kbd>Alt</kbd>+<kbd>↑</kbd>|Sort current column in ascending order

cointop/search.go

@@ -59,7 +59,7 @@ func (ct *Cointop) search(q string) error {
 			return nil
 		}
 		// store index with the smallest levenshtein
-		dist := levenshtein.Distance(name, q)
+		dist := levenshtein.DamerauLevenshteinDistance(name, q)
 		if min == -1 || dist <= min {
 			idx = i
 			min = dist

pkg/levenshtein/levenshtein.go

@@ -1,5 +1,10 @@
 package levenshtein
 
+import (
+	"math"
+	"strings"
+)
+
 // Distance Levenshtein distance
 // The Levenshtein distance between two strings is defined as the minimum
 // number of edits needed to transform one string into the other, with the
@@ -43,15 +48,51 @@ func Distance(str1, str2 string) int {
 	return column[lenS1]
 }
 
-func min(a, b, c int) int {
-	if a < b {
-		if a < c {
-			return a
+// DamerauLevenshteinDistance calculates the damerau-levenshtein distance between s1 and s2.
+// Reference: [Damerau-Levenshtein Distance](http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance)
+// Note that this calculation's result isn't normalized. (not between 0 and 1.)
+// and if s1 and s2 are exactly the same, the result is 0.
+func DamerauLevenshteinDistance(s1, s2 string) int {
+	if s1 == s2 {
+		return 0
+	}
+	s1Array := strings.Split(s1, "")
+	s2Array := strings.Split(s2, "")
+	lenS1Array := len(s1Array)
+	lenS2Array := len(s2Array)
+	m := make([][]int, lenS1Array+1)
+	var cost int
+	for i := range m {
+		m[i] = make([]int, lenS2Array+1)
+	}
+	for i := 0; i < lenS1Array+1; i++ {
+		for j := 0; j < lenS2Array+1; j++ {
+			if i == 0 {
+				m[i][j] = j
+			} else if j == 0 {
+				m[i][j] = i
+			} else {
+				cost = 0
+				if s1Array[i-1] != s2Array[j-1] {
+					cost = 1
+				}
+				m[i][j] = min(m[i-1][j]+1, m[i][j-1]+1, m[i-1][j-1]+cost)
+				if i > 1 && j > 1 && s1Array[i-1] == s2Array[j-2] && s1Array[i-2] == s2Array[j-1] {
+					m[i][j] = min(m[i][j], m[i-2][j-2]+cost)
+				}
+			}
 		}
-	} else {
-		if b < c {
-			return b
+	}
+	return m[lenS1Array][lenS2Array]
+}
+
+// min returns the minimum number of passed int slices.
+func min(is ...int) int {
+	min := int(math.MaxInt64)
+	for _, v := range is {
+		if min > v {
+			min = v
 		}
 	}
-	return c
+	return min
 }