Download presentation
Presentation is loading. Please wait.
Published byAleesha Dickerson Modified over 9 years ago
1
Chapter 16
2
Data structures that take control of organizing elements Elements not in fixed positions Advantage – better performance Adding Removing Finding
3
Unordered collection of distinct elements Fundamental operations Adding an element Removing an element Containment testing (is something there) Listing – arbitrary order Duplicates Aren’t permitted Attempt to add – ignored Attempt to remove non existent data - ignored
5
Hash tables Trees Uses set interface > Hash SetTree Set
6
Create a Hash Set of Strings Set names = new HashSet (); Adding and deleting names.add(“Romeo”); names.remove(“Juliet”); Test to see if element is in set if (names.contains(“Juliet”);
7
Iterator iter=names.iterator(); while (iter.hasNext()) { String name = iter.next(); do something with the string ……… }
8
for (String name: names) { do something with names { Iterating through elements Does not visit in order inserted Set implementation rearranges for quick location Cannot add in iterator position Create a print method Should be usable by has or tree
9
Data type Associates keys and values A function from one set(key) to another set(value) Every key has unique value A value may have multiple keys Two kinds of implementation Hash Map Tree Map Both implement Map Interface
10
Map favoriteColors = new HashMap<String, Color?(); Romeo Adam Eve Juliet Keys Values Green Red Pink
11
Add an association favorite.put(“Juliet”,Color.PINK); Change a value favorite.put(“Juliet”,Color.RED); Return the value Color julietFavoriteColor = favorite.get(“Juliet”); If you ask for a value with no key return a null Remove key and value favorite.remove(“Juliet”);
12
Find all keys and values in map Set keySet = m.keySet(); For(String key: KeySet) { Color value=m.get(key); System.out.println(Key + “->” + value); }
13
Find elements in a data structure quickly No linear search through all elements Can implement sets and maps Utilizes hash codes Function that computes an integer value from an object Integer value = has code Object class has a hashCode method Int h = x.hashCode(); Collision – 2 objects with same hash code Good has functions minimizes collision Hash Code – index into hash table
14
Simplest form Array index to hash table In array insert each object @ location of its hash code If anything already in array – object is present
15
Eve Jim Joe [70068] [74478] [74656]
16
Problem with this idea Not possible to allocate an array that is large enough to hold all possible integer index positions Must reduce hash function to fit the array size int h = x.hashCode(); if (h<0) h = -h; position = h.%buckets.length; This approach increases likely hood of collisions
17
Create a hash table that is implemented as an array of buckets. Buckets are a sequence of nodes that hold elements with the same hash code. [65] [66] [67] [68] [69] [70] [71] [72] Harry Nina Susannah Sue Larry Adam Tony KatherineJuliet
18
Compute hash code Reduce to table size using modulo Produces index into hash table int h = x.hashCode(); if (h<0) h = -h; position = h.%buckets.length; Iterate through the elements of the bucket at position h Check for match
19
Depends on number of collisions or buckets Few buckets – quick O(1) Many buckets – much slower
20
From String int h = 0; for (int i=0; I < s.length(); i++) h = h+s.charAt(i); What is the problem with this? Same hash code for permutations of same letters (eat tea)
21
final int HASH_MULTIPLIER = 31; Int h=0; for(int i=0;i<s.length();i++) h = HASH_MULTIPLIER*h+s.charAt(i); eat 31*(31*’e’ +’a’) + ‘t’ = 100184 31*(31*101+97) + 116 tea 31*(31*’t’ +’e’) + ‘a’ = 114704 31*(31*116+101) + 97
22
Example Coin class public class Coin { final int HASH_MULTIPLIER = 29; public int hashCode() { int h1 = name.hashCode(); int h2 – new Double(value.hashCode(); int h = HASH_MULTIPLIER*h1+h2; return h; } In a hash map only the keys are hashed.
Similar presentations
© 2024 SlidePlayer.com. Inc.
All rights reserved.