Updated readme

README.md

@@ -18,7 +18,7 @@ of document IDs.
 This allows us to quickly find documents containing a given word.
 
 More specifically, for each term we save a postings list as follows: 
-$$\text{n}\;|\;(\text{id}_i, f_i, [p_0, \dots, p_m]), \dots$$
+$$\text{n}\\;|\\;(\text{id}_i, f_i, [p_0, \dots, p_m]), \dots$$
 
 Where $n$ is the number of documents the term appears in, id is the 
 doc id, $f$ is the frequency, and $p_j$ are the positions where 
@@ -34,8 +34,8 @@ The vocabulary is written on disk using prefix compression.
 The idea is to sort terms and then write them as "matching prefix length", and suffix.
 
 Here is an example with three words: 
-$$\text{watermelon}\;\text{waterfall}\;\text{waterfront}$$
-$$0\;\text{watermelon}\;5\;\text{fall}\;6\;\text{ront}$$
+$$\text{watermelon}\\;\text{waterfall}\\;\text{waterfront}$$
+$$0\\;\text{watermelon}\\;5\\;\text{fall}\\;6\\;\text{ront}$$
 
 Spelling correction is used before answering queries. Given a 
 word $w$, we use a trigram index to find terms in the vocabulary 
@@ -44,8 +44,8 @@ We then select the one with the lowest [Levenshtein Distance](https://en.wikiped
 
 $$
 \text{lev}(a, b) = \begin{cases}
-    |a| & \text{if}\;|b| = 0, \\
-    |b| & \text{if}\;|a| = 0, \\
+    |a| & \text{if}\\;|b| = 0, \\
+    |b| & \text{if}\\;|a| = 0, \\
     1 + \text{min} \begin{cases}
         \text{lev}(\text{tail}(a), b) \\
         \text{lev}(a, \text{tail}(b)) \\
@@ -57,11 +57,11 @@ $$
 ### Query processing
 
 You can query the index with boolean or free test queries. In the first case you can use the usual boolean operators to compose a query, such as: 
-$$\text{gun}\;\text{AND}\;\text{control}$$
+$$\text{gun}\\;\text{AND}\\;\text{control}$$
 
 In the second case, you just enter a phrase and receive a ranked collection of documents matching the query, ordered by [BM25 score](https://en.wikipedia.org/wiki/Okapi_BM25). 
 
-$$\text{BM25}(D, Q) = \sum_{i = 1}^{n} \; \text{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \Big (1 - b + b \cdot \frac{|D|}{\text{avgdl}} \Big )}$$
+$$\text{BM25}(D, Q) = \sum_{i = 1}^{n} \\; \text{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \Big (1 - b + b \cdot \frac{|D|}{\text{avgdl}} \Big )}$$
 
 $$\text{IDF}(q_i) = \ln \Bigg ( \frac{N - n(q_i) + 0.5}{n(q_i) + 0.5} + 1 \Bigg )$$