Hash Tables cse.unt/~Nielsen/1040/ ../~sweany/CSCE1040F13/Home.html

1. Hash Tableshttp://www.cse.unt.edu/~Nielsen/1040/../~sweany/CSCE1040F13/Home.html Meetings 7-8 — Sep 23+, 2013 CSCE 1040: Computer Science II Rodney Nielsen

2. Schedule • Today: Hash Tables & Recursion • Lab 3 – This week – Thu, Sept 26 (F270) • Recursion • Lab 4 – Fri, Sept 27 – Thu, Oct 3 • Hash tables plus… • Next week: Pointers and Strings

3. Polish Notation • (5 − 6) * 7 • * - 5 6 7 • 5 – 6 * 7 • - 5 * 6 7

4. Polish Notation double parsePolish() { // TODO Add error handling char *val; scanf("%s", val); if (strlen(val) == 1) { switch (val[0]) { case '+': return parsePolish() + parsePolish(); case '-': return parsePolish() - parsePolish(); … default: … } } return strtod(val, NULL); // TODO Add error handling }

5. Polish Notation double parsePolish() { char *val; scanf("%s", val); if (strlen(val) == 1) { switch (val[0]) { case '+': return parsePolish() + parsePolish(); … default: if ((val[0] >= '0') && (val[0] <= '9')) return strtod(val, NULL); exit(1); } } return strtod(val, NULL); // TODO Add error handling }

6. Polish Notation double parsePolish() { char *val; scanf("%s", val); if (strlen(val) == 1) { switch (val[0]) { case '+': return parsePolish() + parsePolish(); case '-': return parsePolish() - parsePolish(); case '*': return parsePolish() * parsePolish(); case '/': return parsePolish() * parsePolish(); default: if ((val[0] >= '0') && (val[0] <= '9')) return strtod(val, NULL); exit(1); } } return strtod(val, NULL); // TODO Add error handling }

7. Hash Tables • Why do we need to store the key?

8. Hash Tables #include "stdio.h” Entry insert(____ key, ____ value); main() { … }

9. Hash Tables ____ insert(____ key, ____ value) { // compute the key’s hash code // convert (compress) the hash code to compute index // insert the new entry into the table at index }

10. Hash Tables ____ get(____ key, ____ value) { // compute the key’s hash code // convert (compress) the hash code to compute index // search chain at index to find key // return value if found, else null }

11. Hash Tables ____ remove(____ key) { // compute the key’s hash code // convert (compress) the hash code to compute index // search chain at index to find key // if found, remove entry and return value // else return null }

12. Hash Tables What if the key already exists in the hash table? ____ add(____ key, ____ value) { // compute the key’s hash code // convert (compress) the hash code to compute index // search chain at index to find key // if found, replace value… // else add the new entry to the end of the chain }

13. Hash Tables Load factor of a hash table: n / N n = the number of entries in the table N = the length of the array holding the entries If: load factor is low, and the hash code and compression are good, and you don’t allow duplicate keys,  then entry lists are very short and it takes very little time to find an entry (basically constant time – that is the same amount of time regardless of the size of n)

14. Hash Tables • Ideally, though not realistically, you want to map each key to a different index.

15. Search and Rescue