1 / 29

Chapter 9: Maps, Dictionaries, Hashing

Chapter 9: Maps, Dictionaries, Hashing. 0. . Nancy Amato Parasol Lab, Dept. CSE, Texas A&M University Acknowledgement: These slides are adapted from slides provided with Data Structures and Algorithms in C++, Goodrich, Tamassia and Mount (Wiley 2004). 1. 025-612-0001. 2. 981-101-0002.

meredith-horne
Download Presentation

Chapter 9: Maps, Dictionaries, Hashing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 9: Maps, Dictionaries, Hashing 0  Nancy Amato Parasol Lab, Dept. CSE, Texas A&M University Acknowledgement: These slides are adapted from slides provided with Data Structures and Algorithms in C++, Goodrich, Tamassia and Mount (Wiley 2004) 1 025-612-0001 2 981-101-0002 3  4 451-229-0004

  2. Dictionaries Outline and Reading • Map ADT (§9.1) • Dictionary ADT (§9.5) • Ordered Maps (§9.3) • Hash Tables (§9.2)

  3. Dictionaries The map ADT models a searchable collection of key-element items The main operations of a map are searching, inserting, and deleting items Multiple items with the same key are not allowed Applications: address book credit card authorization mapping host names (e.g., cs16.net) to internet addresses (e.g., 128.148.34.101) Map ADT methods: find(k): if M has an entry with key k, return an iterator p referring to this element, else, return special end iterator. put(k, v): if M has no entry with key k, then add entry (k, v) to M, otherwise replace the value of the entry with v; return iterator to the inserted/modified entry erase(k) or erase(p): remove from M entry with key k or iterator p; An error occurs if there is no such element. size(), empty() Map ADT

  4. Dictionaries A direct address table is a map in which The keys are in the range {0,1,2,…,N} Stored in an array of size N - T[0,N] Item with key k stored in T[k] Performance: insertItem, find, and removeElementall take O(1) time Space - requires space O(N), independent of n, the number of items stored in the map The direct address table is not space efficient unless the range of the keys is close to the number of elements to be stored in the map, I.e., unless n is close to N. Map - Direct Address Table

  5. Dictionaries The dictionary ADT models a searchable collection of key-element items The main difference from a map is that multiple items with the same key are allowed Any data structure that supports a dictionary also supports a map Applications: Dictionary which has multiple definitions for the same word Dictionary ADT methods: find(k): if the dictionary has an entry with key k, returns an iterator p to an arbitrary elt. findAll(k): Return iterators (b,e) s.t. that all entries with key k are between them. insert(k, v): insert entry (k, v) into D, return iterator to it. erase(k), erase(p): remove arbitrary entry with key k or entry referenced by p. Error occurs if there is no such entry Begin(), end(): return iterator to first or just beyond last entry of D size(), isEmpty() Dictionary ADT

  6. Dictionaries A log file is a dictionary implemented by means of an unsorted sequence We store the items of the dictionary in a sequence (based on a doubly-linked lists or a circular array), in arbitrary order Performance: insert takes O(1) time since we can insert the new item at the beginning or at the end of the sequence find and erasetake O(n) time since in the worst case (the item is not found) we traverse the entire sequence to look for an item with the given key Space - can be O(n), where n is the number of elements in the dictionary The log file is effective only for dictionaries of small size or for dictionaries on which insertions are the most common operations, while searches and removals are rarely performed (e.g., historical record of logins to a workstation) Dictionary - Log File (unordered sequence implementation)

  7. Vectors Map/Dictionary implementations • n - #elements in map/Dictionary

  8. An Ordered Map/Dictionary supports the usual map/dictionary operations, but also maintains an order relation for the keys. Naturally supports Look-Up Tables - store dictionary in a vector by non-decreasing order of the keys Binary Search Ordered Map/Dictionary ADT: In addition to the generic map/dictionary ADT, supports the following functions: closestBefore(k): return the position of an item with the largest key less than or equal to k closestAfter(k): return the position of an item with the smallest key greater than or equal to k Ordered Map/Dictionary ADT

  9. Dictionaries A lookup table is a dictionary implemented by means of a sorted sequence We store the items of the dictionary in an array-based sequence, sorted by key We use an external comparator for the keys Performance: find takes O(log n) time, using binary search insertItem takes O(n) time since in the worst case we have to shift n/2 items to make room for the new item removeElementtake O(n) time since in the worst case we have to shift n/2 items to compact the items after the removal The lookup table is effective only for dictionaries of small size or for dictionaries on which searches are the most common operations, while insertions and removals are rarely performed (e.g., credit card authorizations) Lookup Table

  10. Dictionaries Example of Ordered Map: Binary Search • Binary search performs operation find(k) on a dictionary implemented by means of an array-based sequence, sorted by key • similar to the high-low game • at each step, the number of candidate items is halved • terminates after a logarithmic number of steps • Example: find(7) 0 1 3 4 5 7 8 9 11 14 16 18 19 m h l 0 1 3 4 5 7 8 9 11 14 16 18 19 m h l 0 1 3 4 5 7 8 9 11 14 16 18 19 m h l 0 1 3 4 5 7 8 9 11 14 16 18 19 l=m =h

  11. Vectors Map/Dictionary implementations • n - #elements in map/Dictionary

  12. Dictionaries Hashing Hash table (an array) of size N, H[0,N] Hash function h that maps keys to indices in H Issues Hash functions - need method to transform key to an index in H that will have nice properties. Collisions - some keys will map to the same index of H (otherwise we have a Direct Address Table). Several methods to resolve the collisions Chaining - put elts that hash to same location in a linked list Open addressing - if a collision occurs, have a method to select another location in the table. Probe sequences Hash Tables

  13. Dictionaries A hash function hmaps keys of a given type to integers in a fixed interval [0, N- 1] Example:h(x) =x mod Nis a hash function for integer keys The integer h(x) is called the hash value of key x A hash table for a given key type consists of Hash function h Array (called table) of size N When implementing a dictionary with a hash table, the goal is to store item (k, o) at index i=h(k) Hash Functions and Hash Tables

  14. Dictionaries 0  1 025-612-0001 2 981-101-0002 3  4 451-229-0004 … 9997  9998 200-751-9998 9999  Example • We design a hash table for a dictionary storing items (SSN, Name), where SSN (social security number) is a nine-digit positive integer • Our hash table uses an array of sizeN= 10,000 and the hash functionh(x) = last four digits of x

  15. Dictionaries Collisions occur when different elements are mapped to the same cell collisions must be resolved Chaining (store in list outside the table) Open addressing (store in another cell in the table) Collisions • Example with Division Method • h(k) = k mod N • If N=10, then • h(k)=0 for k=0,10,20, … • h(k)= 1 for k=1, 11, 21, etc • …

  16. Dictionaries Collisions occur when different elements are mapped to the same cell Chaining: let each cell in the table point to a linked list of elements that map there Chaining is simple, but requires additional memory outside the table 0  1 025-612-0001 2  3  4 451-229-0004 981-101-0004 Collision Resolution with Chaining

  17. Dictionaries Exercise: chaining • Assume you have a hash table H with N=9 slots (H[0,8]) and let the hash function be h(k)=k mod N. • Demonstrate (by picture) the insertion of the following keys into a hash table with collisions resolved by chaining. • 5, 28, 19, 15, 20, 33, 12, 17, 10

  18. Dictionaries Open addressing: the colliding item is placed in a different cell of the table Linear probing handles collisions by placing the colliding item in the next (circularly) available table cell. So the i-th cell checked is: H(k,i) = (h(k)+i)mod N Each table cell inspected is referred to as a “probe” Colliding items lump together, causing future collisions to cause a longer sequence of probes Example: h(x) = x mod13 Insert keys 18, 41, 22, 44, 59, 32, 31, 73, in this order Collision Resolution in Open Addressing - Linear Probing 0 1 2 3 4 5 6 7 8 9 10 11 12 41 18 44 59 32 22 31 73 0 1 2 3 4 5 6 7 8 9 10 11 12

  19. Dictionaries Consider a hash table A that uses linear probing find(k) We start at cell h(k) We probe consecutive locations until one of the following occurs An item with key k is found, or An empty cell is found, or N cells have been unsuccessfully probed Search with Linear Probing Algorithmfind(k) i h(k) p0 repeat c A[i] if c= returnPosition(null) else if c.key () =k returnPosition(c) else i(i+1)mod N p p+1 untilp=N returnPosition(null)

  20. Dictionaries To handle insertions and deletions, we introduce a special object, called AVAILABLE, which replaces deleted elements removeElement(k) We search for an item with key k If such an item (k, o) is found, we replace it with the special item AVAILABLE and we return the position of this item Else, we return a null position insertItem(k, o) We throw an exception if the table is full We start at cell h(k) We probe consecutive cells until one of the following occurs A cell i is found that is either empty or stores AVAILABLE, or N cells have been unsuccessfully probed We store item (k, o) in cell i Updates with Linear Probing

  21. Dictionaries Exercise: Linear Probing • Assume you have a hash table H with N=11 slots (H[0,10]) and let the hash function be h(k)=k mod N. • Demonstrate (by picture) the insertion of the following keys into a hash table with collisions resolved by linear probing. • 10, 22, 31, 4, 15, 28, 17, 88, 59

  22. Dictionaries Double hashing uses a secondary hash function h2(k) and handles collisions by placing an item in the first available cell of the seriesh(k,i) =(h1(k)+ih2(k)) mod Nfor i= 0, 1, … , N - 1 The secondary hash function h2(k) cannot have zero values The table size N must be a prime to allow probing of all the cells Common choice of compression map for the secondary hash function: h2(k) =q-k mod q where q<N q is a prime The possible values for h2(k) are1, 2, … , q Open Addressing: Double Hashing

  23. Dictionaries Example of Double Hashing • Consider a hash table storing integer keys that handles collision with double hashing • N= 13 • h1(k) = k mod13 • h2(k) =7 - k mod7 • Insert keys 18, 41, 22, 44, 59, 32, 31, 73, in this order 0 1 2 3 4 5 6 7 8 9 10 11 12 31 41 18 32 59 73 22 44 0 1 2 3 4 5 6 7 8 9 10 11 12

  24. Dictionaries Exercise: Double Hashing • Assume you have a hash table H with N=11 slots (H[0,10]) and let the hash functions for double hashing be • h(k,i)=(h1(k) + ih2(k))mod N • h1(k)=k mod N • h2(k)=1 + (k mod (N-1)) • Demonstrate (by picture) the insertion of the following keys into H • 10, 22, 31, 4, 15, 28, 17, 88, 59

  25. Dictionaries In the worst case, searches, insertions and removals on a hash table take O(n) time The worst case occurs when all the keys inserted into the dictionary collide The load factor a=n/N affects the performance of a hash table Assuming that the hash values are like random numbers, it can be shown that the expected number of probes for an insertion with open addressing is1/ (1 -a) The expected running time of all the dictionary ADT operations in a hash table is O(1) In practice, hashing is very fast provided the load factor is not close to 100% Applications of hash tables: small databases compilers browser caches Performance of Hashing

  26. Dictionaries Uniform Hashing Assumption • The probe sequence of a key k is the sequence of slots that will be probed when looking for k • In open addressing, the probe sequence is h(k,0), h(k,1), h(k,2), h(k,3), … • Uniform Hashing Assumption: Each key is equally likely to have any one of the N! permutations of {0,1, 2, …, N-1} as is probe sequence • Note: Linear probing and double hashing are far from achieving Uniform Hashing • Linear probing: N distinct probe sequences • Double Hashing: N2 distinct probe sequences

  27. Dictionaries Performance of Uniform Hashing • Theorem: Assuming uniform hashing and an open-address hash table with load factor a = n/N < 1, the expected number of probes in an unsuccessful search is at most 1/(1-a). • Exercise: compute the expected number of probes in an unsuccessful search in an open address hash table with a = ½ , a=3/4, and a = 99/100.

  28. Dictionaries A family of hash functions is universal if, for any 0<i,j<M-1, Pr(h(j)=h(i)) < 1/N. Choose p as a prime between M and 2M. Randomly select 0<a<p and 0<b<p, and define h(k)=(ak+b mod p) mod N Theorem: The set of all functions, h, as defined here, is universal. Universal Hashing

  29. Vectors Maps/Dictionaries • n = #elements in map/dictionary, • N=#possible keys (it could be N>>n) or size of hash table

More Related