New 'use similar' fixes.

pkg/analysis_services/lib/src/correction/levenshtein.dart

Index: pkg/analysis_services/lib/src/correction/levenshtein.dart
diff --git a/pkg/analysis_services/lib/src/correction/levenshtein.dart b/pkg/analysis_services/lib/src/correction/levenshtein.dart
new file mode 100644
index 0000000000000000000000000000000000000000..f25d07edbd459d016c620e7bd88d6d299b507eed
--- /dev/null
+++ b/pkg/analysis_services/lib/src/correction/levenshtein.dart
@@ -0,0 +1,46 @@
+library levenshtein;
+
+import 'dart:math';
+
+/// Levenshtein algorithm implementation based on:
+/// http://en.wikipedia.org/wiki/Levenshtein_distance#Iterative_with_two_matrix_rows
+///
+/// Implementation: https://github.com/conradkleinespel/levenshtein-dart
+int getLevenshteinDistance(String s, String t, {bool caseSensitive: true}) {
+  if (!caseSensitive) {
+    s = s.toLowerCase();
+    t = t.toLowerCase();
+  }
+
+  if (s == t) {
+    return 0;
+  }
+  if (s.length == 0) {
+    return t.length;
+  }
+  if (t.length == 0) {
+    return s.length;
+  }
+
+  List<int> v0 = new List<int>.filled(t.length + 1, 0);
+  List<int> v1 = new List<int>.filled(t.length + 1, 0);
+
+  for (int i = 0; i < t.length + 1; i < i++) {
+    v0[i] = i;
+  }
+
+  for (int i = 0; i < s.length; i++) {
+    v1[0] = i + 1;
+
+    for (int j = 0; j < t.length; j++) {
+      int cost = (s[i] == t[j]) ? 0 : 1;
+      v1[j + 1] = min(v1[j] + 1, min(v0[j + 1] + 1, v0[j] + cost));
+    }
+
+    for (int j = 0; j < t.length + 1; j++) {
+      v0[j] = v1[j];
+    }
+  }
+
+  return v1[t.length];
+}