summaryrefslogtreecommitdiff
path: root/app/utils/match-util.js
diff options
context:
space:
mode:
authorGeo Halkiadakis <gchalkiadakis@sklavenitis.co.gr>2024-04-17 18:32:13 +0300
committerGeo Halkiadakis <gchalkiadakis@sklavenitis.co.gr>2024-04-17 18:32:13 +0300
commitd9364679a51ff80db8e5948ab089d749da36a6b2 (patch)
treef847a03a017be8857923aaaae9f8dcf7177b2ba9 /app/utils/match-util.js
parent6248d2cf7a2214ac32628bc58248694108b4d8bf (diff)
downloadoseine-development.tar.gz
oseine-development.tar.bz2
oseine-development.zip
dockerize the appHEADmasterdevelopment
Diffstat (limited to 'app/utils/match-util.js')
-rw-r--r--app/utils/match-util.js154
1 files changed, 154 insertions, 0 deletions
diff --git a/app/utils/match-util.js b/app/utils/match-util.js
new file mode 100644
index 0000000..5e8c648
--- /dev/null
+++ b/app/utils/match-util.js
@@ -0,0 +1,154 @@
+/**
+ * match utility;
+ * includes fuzzy and partial match functions too;
+ * many of them return a match-rate
+ */
+
+
+// fuzzy match
+////////////////////////////////////////////////////////////////////////////////
+
+/** Ngram fuzzy match algorithm
+ * (simple and fast)
+ */
+const createNgram = (word, n) => { // Ngram creation
+ if (word.length <3) return word;
+ const vector = [];
+ for (let i = 0; i < word.length-n+1; ++i) {
+ vector.push(word.slice(i, i + n));
+ }
+ return vector;
+};
+
+/** similarity
+ * rates similarity between 2 words
+ * based on Ngram matches of N = n letters;
+ * implements a 2-dim check (all a-Ngrams vs all all b-Ngrams)
+ *
+ * @param {string} a : first word
+ * @param {string} b : second word
+ * @param {int} n : Ngram base
+ * @returns {float} : match percentage as a float in [0, 1]
+ */
+const similarity = (a, b, n) => { // Ngram match score
+ if (a.length > 0 && b.length > 0) {
+ const aNgram = createNgram(a, n);
+ const bNgram = createNgram(b, n);
+ let hits = 0;
+ for (let x = 0; x < aNgram.length; ++x) {
+ for (let y = 0; y < bNgram.length; ++y) {
+ if (aNgram[x] === bNgram[y]) {
+ hits += 1;
+ }
+ }
+ }
+ if (hits > 0) {
+ const union = aNgram.length + bNgram.length;
+ return (2.0 * hits) / union;
+ }
+ }
+ return 0;
+};
+
+/** resemblance
+ * is an alternative similarity rating;
+ * implements an 1-dim Ngram similarity check
+ * and it's much faster than similarity()
+ */
+const resemblance = (a, b, n) => {
+ if (a.length > n && b.length >= a.length) {
+ const aNgram = createNgram(a, n);
+ let hits = 0;
+ for (let i = 0; i < aNgram.length; ++i) {
+ if (b.includes(aNgram[i])) {
+ hits++;
+ }
+ }
+ if (hits > 0) {
+ // rate resemblance based on hits and length-similarity
+ return (hits / aNgram.length) * (a.length / b.length);
+ }
+ }
+ return 0;
+}
+
+
+// exact and partial match
+////////////////////////////////////////////////////////////////////////////////
+
+/** is_exact_match
+ * check if a searching string -> query (string/latin in kb-format)
+ * matches exactly an item of the array of synonyms -> chkArr (array of utf-8/strings)
+ *
+ * @param {string} query: searching string; string/latin in kb-format
+ * @param {array} chkArr: array of synonyms; (array of utf-8/strings)
+ * @return {boolean}: true|false
+ */
+function exact( query, chkArr ) {
+ found = false;
+ chkArr.forEach( w => { if (w == query) found = true });
+ return found;
+}
+
+function partial( query, chkArr ) {
+ found = false;
+ chkArr.forEach( w => { if (w.includes(query)) found = true });
+ return found;
+}
+
+/** is exact match + weight rating
+ * @returns {float} weight rates depth of array when a match is found
+ */
+function weighted_exact( query, chkArr ) {
+ let weight = 0; // closer to left/begin rating
+ let len = chkArr.length;
+ for(let i = 0; i < len ; i++) { // i ~ depth
+ if (chkArr[i] == query) {
+ // weights array depth
+ weight = (len - i + 1.0) / len;
+ break;
+ }
+ }
+ return weight;
+}
+
+
+/** is partial match + weight rating
+ *
+ * @param query (string): searching string; string/latin in kb-format
+ * @param chkArr (array): array of synonyms; (array of utf-8/strings)
+ * @returns {float} weight rates both match position and depth of match
+ *
+ * (*) optimization NOTE:
+ * Given the weight `W` and the depth `i`,
+ * the best weight for next `i` shall be: `(L - (i+1)) / L`
+ * To be imposibbe to have a better weight, should:
+ * W > (L - (i+1)) / L => ... => i > (L - L*W - 1)
+ */
+function weighted_partial( query, chkArr ) {
+ let rate = 0;
+ let weight = 0;
+ let len = chkArr.length;
+ for( let i = 0 ; i < len ; i++ ) {
+ let chk = chkArr[i].indexOf(query)
+ if (chk != -1) {
+ rate = (len - i) / (len + 2.0 * chk);
+ weight = rate > weight ? rate : weight;
+ }
+ if (i > (len - len * weight - 1)) {
+ break; // better rating is not possible (*)
+ }
+ }
+ return weight;
+}
+
+
+// exports
+module.exports = {
+ exact,
+ partial,
+ weighted_exact,
+ weighted_partial,
+ similarity,
+ resemblance
+}