Adds most of the toolsHEADmaster

1 files changed, 69 insertions, 0 deletions

diff --git a/v_windows/v/vlib/strings/similarity.v b/v_windows/v/vlib/strings/similarity.v
new file mode 100644
index 0000000..8d8de95
--- /dev/null
+++ b/v_windows/v/vlib/strings/similarity.v
@@ -0,0 +1,69 @@
+module strings
+
+// #-js
+// use levenshtein distance algorithm to calculate
+// the distance between between two strings (lower is closer)
+pub fn levenshtein_distance(a string, b string) int {
+	mut f := [0].repeat(b.len + 1)
+	for j in 0 .. f.len {
+		f[j] = j
+	}
+	for ca in a {
+		mut j := 1
+		mut fj1 := f[0]
+		f[0]++
+		for cb in b {
+			mut mn := if f[j] + 1 <= f[j - 1] + 1 { f[j] + 1 } else { f[j - 1] + 1 }
+			if cb != ca {
+				mn = if mn <= fj1 + 1 { mn } else { fj1 + 1 }
+			} else {
+				mn = if mn <= fj1 { mn } else { fj1 }
+			}
+			fj1 = f[j]
+			f[j] = mn
+			j++
+		}
+	}
+	return f[f.len - 1]
+}
+
+// use levenshtein distance algorithm to calculate
+// how similar two strings are as a percentage (higher is closer)
+pub fn levenshtein_distance_percentage(a string, b string) f32 {
+	d := levenshtein_distance(a, b)
+	l := if a.len >= b.len { a.len } else { b.len }
+	return (1.00 - f32(d) / f32(l)) * 100.00
+}
+
+// implementation of Sørensen–Dice coefficient.
+// find the similarity between two strings.
+// returns coefficient between 0.0 (not similar) and 1.0 (exact match).
+pub fn dice_coefficient(s1 string, s2 string) f32 {
+	if s1.len == 0 || s2.len == 0 {
+		return 0.0
+	}
+	if s1 == s2 {
+		return 1.0
+	}
+	if s1.len < 2 || s2.len < 2 {
+		return 0.0
+	}
+	a := if s1.len > s2.len { s1 } else { s2 }
+	b := if a == s1 { s2 } else { s1 }
+	mut first_bigrams := map[string]int{}
+	for i in 0 .. a.len - 1 {
+		bigram := a[i..i + 2]
+		q := if bigram in first_bigrams { first_bigrams[bigram] + 1 } else { 1 }
+		first_bigrams[bigram] = q
+	}
+	mut intersection_size := 0
+	for i in 0 .. b.len - 1 {
+		bigram := b[i..i + 2]
+		count := if bigram in first_bigrams { first_bigrams[bigram] } else { 0 }
+		if count > 0 {
+			first_bigrams[bigram] = count - 1
+			intersection_size++
+		}
+	}
+	return (2.0 * f32(intersection_size)) / (f32(a.len) + f32(b.len) - 2)
+}