From d9364679a51ff80db8e5948ab089d749da36a6b2 Mon Sep 17 00:00:00 2001
From: Geo Halkiadakis <gchalkiadakis@sklavenitis.co.gr>
Date: Wed, 17 Apr 2024 18:32:13 +0300
Subject: dockerize the app

---
 app/utils/match-util.js | 154 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 154 insertions(+)
 create mode 100644 app/utils/match-util.js

(limited to 'app/utils/match-util.js')

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
+}
-- 
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