*
* A little about how it works, and probability theory:
*
- * When given an identifier (which we will denote I), we're essentially
+ * When given an identifier (which we will denote I), we're essentially
* just trying to choose the most likely correction for that identifier.
* (the actual "correction" can very well be the identifier itself).
* There is actually no way to know for sure that certian identifers
* out of all possible corrections that maximizes the probability of C
* for the original identifer I.
*
- * Thankfully there exists some theroies for probalistic interpretations
+ * Thankfully there exists some theroies for probalistic interpretations
* of data. Since we're operating on two distictive intepretations, the
* transposition from I to C. We need something that can express how much
* degree of I should rationally change to become C. this is called the
* AC P(I|C) P(C) / P(I)
*
* However since P(I) is the same for every possibility of I, we can
- * complete ignore it giving just:
+ * completley ignore it giving just:
* AC P(I|C) P(C)
*
* This greatly helps visualize how the parts of the expression are performed
* enumerates all feasible values of C, to determine the one that
* gives the greatest probability score.
*
- * In reality the requirement for a more complex expression involving
+ * In reality the requirement for a more complex expression involving
* two seperate models is considerably a waste. But one must recognize
* that P(C|I) is already conflating two factors. It's just much simpler
* to seperate the two models and deal with them explicitaly. To properly
*
* A little information on additional algorithms used:
*
- * Initially when I implemented this corrector, it was very slow.
+ * Initially when I implemented this corrector, it was very slow.
* Need I remind you this is essentially a brute force attack on strings,
* and since every transformation requires dynamic memory allocations,
* you can easily imagine where most of the runtime conflated. Yes
* shock to me. A forward allocator (or as some call it a bump-point
* allocator, or just a memory pool) was implemented. To combat this.
*
- * But of course even other factors were making it slow. Initially
+ * But of course even other factors were making it slow. Initially
* this used a hashtable. And hashtables have a good constant lookup
* time complexity. But the problem wasn't in the hashtable, it was
* in the hashing (despite having one of the fastest hash functions
*
* Future Work (If we really need it)
*
- * Currently we can only distinguishes one source of error in the
+ * Currently we can only distinguishes one source of error in the
* language model we use. This could become an issue for identifiers
* that have close colliding rates, e.g colate->coat yields collate.
*
- * Currently the error model has been fairly trivial, the smaller the
+ * Currently the error model has been fairly trivial, the smaller the
* edit distance the smaller the error. This usually causes some un-
* expected problems. e.g reciet->recite yields recipt. For QuakeC
* this could become a problem when lots of identifiers are involved.
*
- * Our control mechanisim could use a limit, i.e limit the number of
+ * Our control mechanisim could use a limit, i.e limit the number of
* sets of edits for distance X. This would also increase execution
* speed considerably.
- *
*/