hash function
... for set, values accessed in increasing order (based on operator< for TYPE) for map, values accessed in increasing order (based on operator< for KEY_TYPE) ...
... for set, values accessed in increasing order (based on operator< for TYPE) for map, values accessed in increasing order (based on operator< for KEY_TYPE) ...
A Comparison of Dictionary Implementations
... because it ’mixes’ the data of its input, so that the output for similar inputs appears totally unrelated. When two members of K, say k1 and k2 have the same hash value, i.e. h(k1 ) = h(k2 ), then we say there is a hash collision, and this collision must be resolved. There are two main methods of co ...
... because it ’mixes’ the data of its input, so that the output for similar inputs appears totally unrelated. When two members of K, say k1 and k2 have the same hash value, i.e. h(k1 ) = h(k2 ), then we say there is a hash collision, and this collision must be resolved. There are two main methods of co ...
Data Structures Part 2
... ● Hash tables (hashmaps) an efficient data structure for storing dynamic data. ● Hash tables are good for doing a quick search on things. ● commonly implemented as an array of linked lists (hash tables with chaining). ● Each data item is associated with a key that determines its location. ...
... ● Hash tables (hashmaps) an efficient data structure for storing dynamic data. ● Hash tables are good for doing a quick search on things. ● commonly implemented as an array of linked lists (hash tables with chaining). ● Each data item is associated with a key that determines its location. ...
Data Structures So Far
... remove(e): Remove from P and return entry e. replaceKey(e,k): Replace with k and return the old key; an error condition occurs if k is invalid (that is, k cannot be compared with other keys). replaceValue(e,x): Replace with x and return the old ...
... remove(e): Remove from P and return entry e. replaceKey(e,k): Replace with k and return the old key; an error condition occurs if k is invalid (that is, k cannot be compared with other keys). replaceValue(e,x): Replace with x and return the old ...
k-wise Independent Hashing and Applications
... in [l] give a pairwise independent hash family: we can associate a hash function h to every choice of random bits and set h(a) = xa for all a ∈ [m]. Recalling our construction of 2-wise independent distributions, we can construct a pairwise-independent hash family as H = {ax + b : a, b ∈ F} for a fi ...
... in [l] give a pairwise independent hash family: we can associate a hash function h to every choice of random bits and set h(a) = xa for all a ∈ [m]. Recalling our construction of 2-wise independent distributions, we can construct a pairwise-independent hash family as H = {ax + b : a, b ∈ F} for a fi ...
Slides
... Universal Hashing • Choose a hash funcLon at random from a large parameterized family of hash funcLons (e.g., h(x) = ax + b, where a and b are chosen at random) • With high probability, it wi ...
... Universal Hashing • Choose a hash funcLon at random from a large parameterized family of hash funcLons (e.g., h(x) = ax + b, where a and b are chosen at random) • With high probability, it wi ...
Hash Tables The Search Problem
... In the cuckoo hashing scheme, we use two lookup tables, T0 and T1, each of size N, where N is greater than n, the number of items in the map, by at least a constant factor, say, N ≥ 2n. We use a hash function, h0, for T0, and a different hash function, h1, for T1. For any key, k, there are only two ...
... In the cuckoo hashing scheme, we use two lookup tables, T0 and T1, each of size N, where N is greater than n, the number of items in the map, by at least a constant factor, say, N ≥ 2n. We use a hash function, h0, for T0, and a different hash function, h1, for T1. For any key, k, there are only two ...
pptx - Chair of Software Engineering
... The main way to access them is in that order (Bonus condition) No hardwired size limit Use an array if: Each item can be identified by and integer index The main way to access items is through that index Hardwired size limit (at least for long spans of execution) ...
... The main way to access them is in that order (Bonus condition) No hardwired size limit Use an array if: Each item can be identified by and integer index The main way to access items is through that index Hardwired size limit (at least for long spans of execution) ...
Sets, Maps and Hash tables
... – Must map all objects of type T to the interval [0; N-1] – Should map objects as uniformly as possible to the interval [0; N-1] ...
... – Must map all objects of type T to the interval [0; N-1] – Should map objects as uniformly as possible to the interval [0; N-1] ...
hash 2 (x)
... • So the average length of list is λ • Search Time = Time to evaluate hash function + the time to traverse the list • Unsuccessful search= λ nodes are examined • Successful search=1 + ½* (N-1)/M (the node searched + half the expected # of other nodes) =1+1/2 *λ • Observation: Table size is not impor ...
... • So the average length of list is λ • Search Time = Time to evaluate hash function + the time to traverse the list • Unsuccessful search= λ nodes are examined • Successful search=1 + ½* (N-1)/M (the node searched + half the expected # of other nodes) =1+1/2 *λ • Observation: Table size is not impor ...
Data Structures Lecture 14 Name:__________________
... b) Similarly, binary search of a sorted array (or AVL tree) always uses a fixed search strategy for any given target value. For example, binary search always compares the target value with the middle element of the remaining portion of the array needing to be searched. If n is the number of items be ...
... b) Similarly, binary search of a sorted array (or AVL tree) always uses a fixed search strategy for any given target value. For example, binary search always compares the target value with the middle element of the remaining portion of the array needing to be searched. If n is the number of items be ...
lecture10
... • Notice that one issue with all the hash functions is that the actual content of the key set matters • The elements in K (the keys that are used) are quite possibly a restricted subset of U, not just a random collection › variable names, words in the English ...
... • Notice that one issue with all the hash functions is that the actual content of the key set matters • The elements in K (the keys that are used) are quite possibly a restricted subset of U, not just a random collection › variable names, words in the English ...
Data Structures 1
... How to Hash Strings? Simple hash schemes don’t distribute the keys evenly enough • Number of characters, mod BUCKET_COUNT • Sum the numeric codes of all characters, mod BUCKET_COUNT ...
... How to Hash Strings? Simple hash schemes don’t distribute the keys evenly enough • Number of characters, mod BUCKET_COUNT • Sum the numeric codes of all characters, mod BUCKET_COUNT ...
Array Algorithms
... 1. Search the array for the smallest element 2. Swap the first element with the smallest element 3. The first element is holding the correct value, so no longer consider it in the sort 4. Repeat previous steps for rest of array until the array is completely sorted. ...
... 1. Search the array for the smallest element 2. Swap the first element with the smallest element 3. The first element is holding the correct value, so no longer consider it in the sort 4. Repeat previous steps for rest of array until the array is completely sorted. ...
Midterm Solutions
... I. Design a priority queue implementation that performs insert, max, and delete-max in ∼ 31 lg N compares per operation, where N is the number of comparable keys in the data structure. This would violate the ∼ N lg N lower bound for sorting because you can sort an array by inserting N keys into a ma ...
... I. Design a priority queue implementation that performs insert, max, and delete-max in ∼ 31 lg N compares per operation, where N is the number of comparable keys in the data structure. This would violate the ∼ N lg N lower bound for sorting because you can sort an array by inserting N keys into a ma ...
CPSC 335 - University of Calgary
... keys. Also, the simple operations tries use during lookup, such as array indexing using a character, are fast on real machines. Tries can require less space when they contain a large number of short strings, because the keys are not stored explicitly and nodes are shared between keys with common ini ...
... keys. Also, the simple operations tries use during lookup, such as array indexing using a character, are fast on real machines. Tries can require less space when they contain a large number of short strings, because the keys are not stored explicitly and nodes are shared between keys with common ini ...
pptx - Chair of Software Engineering
... The main way to access them is in that order (Bonus condition) No hardwired size limit Use an array if: Each item can be identified by and integer index The main way to access items is through that index Hardwired size limit (at least for long spans of execution) ...
... The main way to access them is in that order (Bonus condition) No hardwired size limit Use an array if: Each item can be identified by and integer index The main way to access items is through that index Hardwired size limit (at least for long spans of execution) ...
previous-qns-and-answers
... 2. If key k is in node x and x is an internal node, there are three cases to consider: (a) If the child y that precedes k in node x has at least t keys (more than the minimum), then find the predecessor key k0 in the subtree rooted at y. Recursively delete k0 and replace k with k0 in x (b) Symmetri ...
... 2. If key k is in node x and x is an internal node, there are three cases to consider: (a) If the child y that precedes k in node x has at least t keys (more than the minimum), then find the predecessor key k0 in the subtree rooted at y. Recursively delete k0 and replace k with k0 in x (b) Symmetri ...
Bloom filter
A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not, thus a Bloom filter has a 100% recall rate. In other words, a query returns either ""possibly in set"" or ""definitely not in set"". Elements can be added to the set, but not removed (though this can be addressed with a ""counting"" filter). The more elements that are added to the set, the larger the probability of false positives.Bloom proposed the technique for applications where the amount of source data would require an impractically large amount of memory if ""conventional"" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses. For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses, an 85–15 form of the Pareto principle (Bloom (1970)).More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set (Bonomi et al. (2006)).