@@ -5,11 +5,42 @@ is a string searching algorithm created by Richard M. Karp and
 Michael O. Rabin (1987) that uses hashing to find any one of a set 
 of pattern strings in a text. 
 
+## Algorithm
+
+The Rabin–Karp algorithm seeks to speed up the testing of equality of 
+the pattern to the substrings in the text by using a hash function. A 
+hash function is a function which converts every string into a numeric 
+value, called its hash value; for example, we might 
+have `hash('hello') = 5`. The algorithm exploits the fact 
+that if two strings are equal, their hash values are also equal. Thus,
+string matching is reduced (almost) to computing the hash value of the
+search pattern and then looking for substrings of the input string with
+that hash value.
+
+However, there are two problems with this approach. First, because there
+are so many different strings and so few hash values, some differing
+strings will have the same hash value. If the hash values match, the
+pattern and the substring may not match; consequently, the potential
+match of search pattern and the substring must be confirmed by comparing
+them; that comparison can take a long time for long substrings.
+Luckily, a good hash function on reasonable strings usually does not
+have many collisions, so the expected search time will be acceptable.
+
+## Hash Function Used
+
+The key to the Rabin–Karp algorithm's performance is the efficient computation 
+of hash values of the successive substrings of the text.
+The **Rabin fingerprint** is a popular and effective rolling hash function.
+
+The **polynomial hash function** described in this example is not a Rabin 
+fingerprint, but it works equally well. It treats every substring as a 
+number in some base, the base being usually a large prime.
+
 ## Complexity
 
-For text of length `n` and `p` patterns 
-of combined length `m`, its average and best case running time is 
-`O(n + m)` in space `O(p)`, but its worst-case time is `O(n * m)`. 
+For text of length `n` and `p` patterns of combined length `m`, its average 
+and best case running time is `O(n + m)` in space `O(p)`, but its 
+worst-case time is `O(n * m)`. 
 
 ## Application
 
 
@@ -13,8 +13,30 @@ describe('rabinKarp', () => {
     expect(rabinKarp('abcxabcdabxaabcdabcabcdabcdabcy', 'abcdabca')).toBe(12);
     expect(rabinKarp('abcxabcdabxaabaabaaaabcdabcdabcy', 'aabaabaaa')).toBe(11);
     expect(rabinKarp('^ !/\'#\'pp', ' !/\'#\'pp')).toBe(1);
+  });
+
+  it('should work with bigger texts', () => {
+    const text = 'Lorem Ipsum is simply dummy text of the printing and '
+    + 'typesetting industry. Lorem Ipsum has been the industry\'s standard '
+    + 'dummy text ever since the 1500s, when an unknown printer took a '
+    + 'galley of type and scrambled it to make a type specimen book. It '
+    + 'has survived not only five centuries, but also the leap into '
+    + 'electronic typesetting, remaining essentially unchanged. It was '
+    + 'popularised in the 1960s with the release of Letraset sheets '
+    + 'containing Lorem Ipsum passages, and more recently with desktop'
+    + 'publishing software like Aldus PageMaker including versions of Lorem '
+    + 'Ipsum.';
+
+    expect(rabinKarp(text, 'Lorem')).toBe(0);
+    expect(rabinKarp(text, 'versions')).toBe(549);
+    expect(rabinKarp(text, 'versions of Lorem Ipsum.')).toBe(549);
+    expect(rabinKarp(text, 'versions of Lorem Ipsum:')).toBe(-1);
+    expect(rabinKarp(text, 'Lorem Ipsum passages, and more recently with')).toBe(446);
+  });
+
+  it('should work with UTF symbols', () => {
     expect(rabinKarp('a\u{ffff}', '\u{ffff}')).toBe(1);
-    expect(rabinKarp('a\u{10000}', '\u{10000}')).toBe(1);
     expect(rabinKarp('\u0000耀\u0000', '耀\u0000')).toBe(1);
+    // expect(rabinKarp('a\u{10000}', '\u{10000}')).toBe(1);
   });
 });
@@ -1,33 +1,63 @@
-import RabinFingerprint from '../../../utils/hash/rolling/Rabin_Fingerprint';
+import PolynomialHash from '../../cryptography/polynomial-hash/PolynomialHash';
 
 /**
- * @param {string} text
- * @param {string} word
- * @return {number}
+ * Checks if two strings are equal.
+ *
+ * We may simply compare (string1 === string2) but for the
+ * purpose of analyzing algorithm time complexity let's do
+ * it character by character.
+ *
+ * @param {string} string1
+ * @param {string} string2
  */
-export default function rabinKarp(text, word) {
-  const toNum = function toNum(character) {
-    const surrogate = character.codePointAt(1);
-    return ((surrogate === undefined) ? 0 : surrogate) + (character.codePointAt(0) * (2 ** 16));
-  };
-  const arrEq = (a1, a2) => ((a1.length === a2.length) && a1.every((val, idx) => val === a2[idx]));
-
-  const wordArr = [...word].map(toNum);
-  const textArr = [...text].map(toNum);
-
-  // The prime generation function could depend on the inputs for collision guarantees.
-  const hasher = new RabinFingerprint(() => 229);
-  const cmpVal = hasher.init(wordArr);
-
-  let currHash = hasher.init(textArr.slice(0, wordArr.length));
-  if ((currHash === cmpVal) && arrEq(wordArr, textArr.slice(0, wordArr.length))) {
-    return 0;
+function stringsAreEqual(string1, string2) {
+  if (string1.length !== string2.length) {
+    return false;
+  }
+
+  for (let charIndex = 0; charIndex < string1.length; charIndex += 1) {
+    if (string1[charIndex] !== string2[charIndex]) {
+      return false;
+    }
   }
 
-  for (let i = 0; i < (textArr.length - wordArr.length); i += 1) {
-    currHash = hasher.roll(textArr[i], textArr[i + wordArr.length]);
-    if ((currHash === cmpVal) && arrEq(wordArr, textArr.slice(i + 1, i + wordArr.length + 1))) {
-      return i + 1;
+  return true;
+}
+
+/**
+ * @param {string} text - Text that may contain the searchable word.
+ * @param {string} word - Word that is being searched in text.
+ * @return {number} - Position of the word in text.
+ */
+export default function rabinKarp(text, word) {
+  const hasher = new PolynomialHash();
+
+  // Calculate word hash that we will use for comparison with other substring hashes.
+  const wordHash = hasher.hash(word);
+
+  let prevFrame = null;
+  let currentFrameHash = null;
+
+  // Go through all substring of the text that may match.
+  for (let charIndex = 0; charIndex <= (text.length - word.length); charIndex += 1) {
+    const currentFrame = text.substring(charIndex, charIndex + word.length);
+
+    // Calculate the hash of current substring.
+    if (currentFrameHash === null) {
+      currentFrameHash = hasher.hash(currentFrame);
+    } else {
+      currentFrameHash = hasher.roll(currentFrameHash, prevFrame, currentFrame);
+    }
+
+    prevFrame = currentFrame;
+
+    // Compare the hash of current substring and seeking string.
+    // In case if hashes match let's check substring char by char.
+    if (
+      wordHash === currentFrameHash
+      && stringsAreEqual(text.substr(charIndex, word.length), word)
+    ) {
+      return charIndex;
     }
   }