Compsci 201 Priority Queues & Autocomplete

Total Page:16

File Type:pdf, Size:1020Kb

Compsci 201 Priority Queues & Autocomplete Compsci 201 Priority Queues & Autocomplete Owen Astrachan Jeff Forbes November 15, 2017 11/15/17 Compsci 201, Fall 2017, PQ + Compare 1 U is for … • URL and URI • Uniform Resource (Locator and Identifier) • Usenet • p2p original source of FAQ, Flame, Spam • Unix • Before there was Linux, … • User Interface, UI, UX • User is the heart and soul 11/15/17 Compsci 201, Fall 2017, PQ + Compare 2 Plan for the Day • Where are we? Where are we going? What’s left? • Review of PQs+Heaps: implementation and API • PQ API is a key to Autocomplete • Software Design and Software Testing • Autcomplete, algorithms, trade-offs • Testing, debugging, understanding 11/15/17 Compsci 201, Fall 2017, PQ + Compare 3 Work [todate | todo] • APTQuiz2: Median 30, Mean 24.6 • APTQuiz1: Median 30, Mean 25.5 • Midterm 1: Median 78%, Mean 75% • Midterm 2: Median 87%, Mean 84% • Assignments: 5 of 6 out • APT: one more set, one more quiz 11/15/17 Compsci 201, Fall 2017, PQ + Compare 4 One path, two paths 11/15/17 Compsci 201, Fall 2017, PQ + Compare 5 Heap Review • Used to implement priority queues efficiently • Binary tree implemented in array: indexes! • Heap shape and heap property, 2*k and 2*k+1 • Minimal element: O(1) peek and O(log N) poll • Change definition of min: max-heap! • How to compare elements? • Comparable or Comparator! 11/15/17 Compsci 201, Fall 2017, PQ + Compare 6 A Sore by any other name … • String implements Comparable<String> • We know there’s a .compareTo method • What if we want to change how strings compared, e.g., length or lowercase or … • Sometimes we don’t have access to class, or sub-classing not a good idea • Some classes are final, no sub-classing! • Some classes not designed for sub-classing 11/15/17 Compsci 201, Fall 2017, PQ + Compare 7 Comparator or Raptor Coma? • Must implement .compare(T a, T b) • Different than .compareTo, similar as well • Return < 0 when a < b • Return == 0 when a == b • Return > 0 when a > b • You write code to determine what this means! 11/15/17 Compsci 201, Fall 2017, PQ + Compare 8 Something Old, Something New • Create class that implements Comparator<T> • Write the .compare(T a, T b) method • Create Comparator object, use it • Java 8: New tricks: Create classes “anonymously” by calling Comparator.comparing • Pass method names as parameters https://coursework.cs.duke.edu/201fall17/sortall/blob/master/src/PersonSorter.java 11/15/17 Compsci 201, Fall 2017, PQ + Compare 9 PersonSorter.java static class Person implements Comparable<Person> { String first; String last; public String getLast(){ return last; } // more here https://coursework.cs.duke.edu/201fall17/sortall/blob/master/src/PersonSorter.java • Changing how people are compared (run it) Comparator<Person> comp = Comparator.comparing(Person::getFirst) .thenComparing(Person::getLast); Collections.sort(list,comp); 11/15/17 Compsci 201, Fall 2017, PQ + Compare 10 WOTO http://bit.ly/201fall17-nov15-compare • What is Comparator.comparing? • Creating Comparator at runtime 11/15/17 Compsci 201, Fall 2017, PQ + Compare 11 It's time for Autocomplete 11/15/17 Compsci 201, Fall 2017, PQ + Compare 12 What is Autocomplete? • 40,000 queries/second, thousands of computers, 0.2 seconds to answer query 11/15/17 Compsci 201, Fall 2017, PQ + Compare 13 Geolocating Heaven… 11/15/17 Compsci 201, Fall 2017, PQ + Compare 14 Data Structure for Autocomplete • We'd like the "best" or "top" matching Terms • Each Term is a (word, weight) pair • If we sort by weight, we get the best easily! • Priority Queues help • Find "best" element • Comparator! 11/15/17 Compsci 201, Fall 2017, PQ + Compare 15 Tradeoffs in Autocomplete • Bruteforce: look at every (word, weight) pair • Find heaviest/best ones after looking at all N • Binary Search: search efficiently for prefixes • Find these candidates, choose best from M • Trie Search: search really efficiently for prefixes • Search tree-like structure, choose best from M 11/15/17 Compsci 201, Fall 2017, PQ + Compare 16 Overview of Approaches • Have N total terms, want k best matches from M • N is millions; k is 10’s; M is hundreds… • We want k "heav…" prefix matches, from M of N • We can organize the N elements to find M • ITRW we'd do lots/different organizing • Bruteforce: search for M matching terms, sort M • O(N) to search, O(M log M) to sort, get top k 11/15/17 Compsci 201, Fall 2017, PQ + Compare 17 How to get organized https://www.youtube.com/watch?v=1ve57l3c19g 11/15/17 Compsci 201, Fall 2017, PQ + Compare 18 Bruteforce made smarter • Suppose we insert all N elements into a priority queue ordered by weight -- limited to M elements • PQ contains M elements, minimum first • Default PQ in Java, can specify comparator • Example: M = 4, add 10,70,30,40,20,60,50 • After adding 20, drop using pq.remove() • After adding 60 drop using pq.remove() • Contains largest M elements seen so far 11/15/17 Compsci 201, Fall 2017, PQ + Compare 19 Quantifying Improvements • PQ changes O(N + M log M)to O(N log M) • Find M, then sort versus PQ insertion. Doesn't take constant factors into account • Still typically faster when N > M https://coursework.cs.duke.edu/201fall17/sortall/blob/master/src/TopMSorting.java • Where are heaviest/best when sorting? • Last M: Collections.sort(...) • First M: .sort(...,comp.reversed()); 11/15/17 Compsci 201, Fall 2017, PQ + Compare 20 Sort N take top k • Why use reversed comparator? Alternative? • If we used comp, greatest/top k at end • Could still use subList to return these! https://coursework.cs.duke.edu/201fall17/sortall/blob/master/src/TopMSorting.java public static List<String> sortTopM(List<String> list, int mSize, Comparator<String> comp){ List<String> copy = new ArrayList<>(list); Collections.sort(copy,comp.reversed()); return copy.subList(0, mSize); } 11/15/17 Compsci 201, Fall 2017, PQ + Compare 21 Use limited-PQ for top k https://coursework.cs.duke.edu/201fall17/sortall/blob/master/src/TopMSorting.java • Remove/poll min when pq size exceeds • Why do we remove smallest seen so far? • Why do we use LinkedList and addFirst? public static List<String> pqTopM(List<String> list, int mSize, Comparator<String> comp) { PriorityQueue<String> pq = new PriorityQueue<>(comp); for(String s : list) { pq.add(s); if (pq.size() > mSize) pq.remove(); } LinkedList<String> ret = new LinkedList<>(); while (pq.size() > 0) ret.addFirst(pq.remove()); return ret; } 11/15/17 Compsci 201, Fall 2017, PQ + Compare 22 No organization in BruteForce • Always search through all N (word, weight) terms • Even to find the best 10, or the best 100 of M • New Query? Search again, no improvement • We can organize data to facilitate prefix search! • Binary search through a sorted list • Trie data structure to help with prefixes • Both better in theory, in practice? It depends 11/15/17 Compsci 201, Fall 2017, PQ + Compare 23 Binary Search in Autocomplete • Given "beenie" and prefix of 3, find M matches • Find first "bee.." and last "bee.." • Sort these M elements by weight! Done • O(log N) to find first and last O(M log M) to sort 11/15/17 Compsci 201, Fall 2017, PQ + Compare 24 BinarySearch (Autocomplete) • Sort all N elements: cost O(N log N) • Find M prefixes (weight,word) pairs O(log N) using binary search for firstIndex and lastIndex (adjacent) • Take the top k of these • More queries? Sort once! Sorting cost amortized • Carefully code binary search to find first/last • Top k after sorting: O(M log M) • Use limited PQ?: O(M log k) 11/15/17 Compsci 201, Fall 2017, PQ + Compare 25 Summary of Two Approaches • Bruteforce: O(N + M log M) or O(N log M) • Binary search: O(log N + M log M)* • Requires initial sort of O(N log N), only once! • We are willing to sort once, recoup $$ over time • Which is better? • What if we do LOTS of queries? Q? • Comparing QN to Qlog N 11/15/17 Compsci 201, Fall 2017, PQ + Compare 26 One compare, cut list in half! binary search 11/15/17 Compsci 201, Fall 2017, PQ + Compare 27 Finding the firstIndex • Use Collections.binarySearch • Code below doesn't check index < 0 • Why is this O(N) in worst case? public static int firstIndex(String[] values, String target, Comparator<String> comp) { List<String> list = Arrays.asList(values); int index = Collections.binarySearch(list,target,comp); while (0 <= index && comp.compare(list.get(index),target) == 0) { index -= 1; } return index+1; } 11/15/17 Compsci 201, Fall 2017, PQ + Compare 28 Start with code and change, … • Do not use this reference to achieve O(log N) http://stackoverflow.com/questions/6676360/first -occurrence-in-a-binary-search • One idea: find standard code and mess with it until it works 11/15/17 Compsci 201, Fall 2017, PQ + Compare 29 How to develop loops • David Gries: The Science of Programming • Edsger Dijkstra: The Discipline of Programming 11/15/17 Compsci 201, Fall 2017, PQ + Compare 30 Reasoning about code https://coursework.cs.duke.edu/201fall17/sortall/blob/master/src/Looper.java A. Runs Forever B. Exhausts Memory and stops C. Prints~ (2 billion), D. Prints~ (-2 billion) public class Looper { public static void main(String[] args){ int x = 0; while (x < x + 1) { x = x + 1; } System.out.println("value of x = "+x); } } 11/15/17 Compsci 201, Fall 2017, PQ + Compare 31 Reasoning with Logic • While loop test is a boolean expression • Negation must be true when loop exits, why? • Other boolean expressions aid loop development • Loop invariant: true when loop test checked • Use invariant and loop guard to develop loop • Reason semi-formally about loops 11/15/17 Compsci 201, Fall 2017, PQ + Compare 32 Better than late-night coding? • Proving code correct?
Recommended publications
  • An Alternative to Fibonacci Heaps with Worst Case Rather Than Amortized Time Bounds∗
    Relaxed Fibonacci heaps: An alternative to Fibonacci heaps with worst case rather than amortized time bounds¤ Chandrasekhar Boyapati C. Pandu Rangan Department of Computer Science and Engineering Indian Institute of Technology, Madras 600036, India Email: [email protected] November 1995 Abstract We present a new data structure called relaxed Fibonacci heaps for implementing priority queues on a RAM. Relaxed Fibonacci heaps support the operations find minimum, insert, decrease key and meld, each in O(1) worst case time and delete and delete min in O(log n) worst case time. Introduction The implementation of priority queues is a classical problem in data structures. Priority queues find applications in a variety of network problems like single source shortest paths, all pairs shortest paths, minimum spanning tree, weighted bipartite matching etc. [1] [2] [3] [4] [5] In the amortized sense, the best performance is achieved by the well known Fibonacci heaps. They support delete and delete min in amortized O(log n) time and find min, insert, decrease key and meld in amortized constant time. Fast meldable priority queues described in [1] achieve all the above time bounds in worst case rather than amortized time, except for the decrease key operation which takes O(log n) worst case time. On the other hand, relaxed heaps described in [2] achieve in the worst case all the time bounds of the Fi- bonacci heaps except for the meld operation, which takes O(log n) worst case ¤Please see Errata at the end of the paper. 1 time. The problem that was posed in [1] was to consider if it is possible to support both decrease key and meld simultaneously in constant worst case time.
    [Show full text]
  • Priority Queues and Binary Heaps Chapter 6.5
    Priority Queues and Binary Heaps Chapter 6.5 1 Some animals are more equal than others • A queue is a FIFO data structure • the first element in is the first element out • which of course means the last one in is the last one out • But sometimes we want to sort of have a queue but we want to order items according to some characteristic the item has. 107 - Trees 2 Priorities • We call the ordering characteristic the priority. • When we pull something from this “queue” we always get the element with the best priority (sometimes best means lowest). • It is really common in Operating Systems to use priority to schedule when something happens. e.g. • the most important process should run before a process which isn’t so important • data off disk should be retrieved for more important processes first 107 - Trees 3 Priority Queue • A priority queue always produces the element with the best priority when queried. • You can do this in many ways • keep the list sorted • or search the list for the minimum value (if like the textbook - and Unix actually - you take the smallest value to be the best) • You should be able to estimate the Big O values for implementations like this. e.g. O(n) for choosing the minimum value of an unsorted list. • There is a clever data structure which allows all operations on a priority queue to be done in O(log n). 107 - Trees 4 Binary Heap Actually binary min heap • Shape property - a complete binary tree - all levels except the last full.
    [Show full text]
  • Assignment 3: Kdtree ______Due June 4, 11:59 PM
    CS106L Handout #04 Spring 2014 May 15, 2014 Assignment 3: KDTree _________________________________________________________________________________________________________ Due June 4, 11:59 PM Over the past seven weeks, we've explored a wide array of STL container classes. You've seen the linear vector and deque, along with the associative map and set. One property common to all these containers is that they are exact. An element is either in a set or it isn't. A value either ap- pears at a particular position in a vector or it does not. For most applications, this is exactly what we want. However, in some cases we may be interested not in the question “is X in this container,” but rather “what value in the container is X most similar to?” Queries of this sort often arise in data mining, machine learning, and computational geometry. In this assignment, you will implement a special data structure called a kd-tree (short for “k-dimensional tree”) that efficiently supports this operation. At a high level, a kd-tree is a generalization of a binary search tree that stores points in k-dimen- sional space. That is, you could use a kd-tree to store a collection of points in the Cartesian plane, in three-dimensional space, etc. You could also use a kd-tree to store biometric data, for example, by representing the data as an ordered tuple, perhaps (height, weight, blood pressure, cholesterol). However, a kd-tree cannot be used to store collections of other data types, such as strings. Also note that while it's possible to build a kd-tree to hold data of any dimension, all of the data stored in a kd-tree must have the same dimension.
    [Show full text]
  • Rethinking Host Network Stack Architecture Using a Dataflow Modeling Approach
    DISS.ETH NO. 23474 Rethinking host network stack architecture using a dataflow modeling approach A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by Pravin Shinde Master of Science, Vrije Universiteit, Amsterdam born on 15.10.1982 citizen of Republic of India accepted on the recommendation of Prof. Dr. Timothy Roscoe, examiner Prof. Dr. Gustavo Alonso, co-examiner Dr. Kornilios Kourtis, co-examiner Dr. Andrew Moore, co-examiner 2016 Abstract As the gap between the speed of networks and processor cores increases, the software alone will not be able to handle all incoming data without additional assistance from the hardware. The network interface controllers (NICs) evolve and add supporting features which could help the system increase its scalability with respect to incoming packets, provide Quality of Service (QoS) guarantees and reduce the CPU load. However, modern operating systems are ill suited to both efficiently exploit and effectively manage the hardware resources of state-of-the-art NICs. The main problem is the layered architecture of the network stack and the rigid interfaces. This dissertation argues that in order to effectively use the diverse and complex NIC hardware features, we need (i) a hardware agnostic representation of the packet processing capabilities of the NICs, and (ii) a flexible interface to share this information with different layers of the network stack. This work presents the Dataflow graph based model to capture both the hardware capabilities for packet processing of the NIC and the state of the network stack, in order to enable automated reasoning about the NIC features in a hardware-agnostic way.
    [Show full text]
  • Priorityqueue
    CSE 373 Java Collection Framework, Part 2: Priority Queue, Map slides created by Marty Stepp http://www.cs.washington.edu/373/ © University of Washington, all rights reserved. 1 Priority queue ADT • priority queue : a collection of ordered elements that provides fast access to the minimum (or maximum) element usually implemented using a tree structure called a heap • priority queue operations: add adds in order; O(log N) worst peek returns minimum value; O(1) always remove removes/returns minimum value; O(log N) worst isEmpty , clear , size , iterator O(1) always 2 Java's PriorityQueue class public class PriorityQueue< E> implements Queue< E> Method/Constructor Description Runtime PriorityQueue< E>() constructs new empty queue O(1) add( E value) adds value in sorted order O(log N ) clear() removes all elements O(1) iterator() returns iterator over elements O(1) peek() returns minimum element O(1) remove() removes/returns min element O(log N ) Queue<String> pq = new PriorityQueue <String>(); pq.add("Stuart"); pq.add("Marty"); ... 3 Priority queue ordering • For a priority queue to work, elements must have an ordering in Java, this means implementing the Comparable interface • many existing types (Integer, String, etc.) already implement this • if you store objects of your own types in a PQ, you must implement it TreeSet and TreeMap also require Comparable types public class Foo implements Comparable<Foo> { … public int compareTo(Foo other) { // Return > 0 if this object is > other // Return < 0 if this object is < other // Return 0 if this object == other } } 4 The Map ADT • map : Holds a set of unique keys and a collection of values , where each key is associated with one value.
    [Show full text]
  • Programmatic Testing of the Standard Template Library Containers
    Programmatic Testing of the Standard Template Library Containers y z Jason McDonald Daniel Ho man Paul Stro op er May 11, 1998 Abstract In 1968, McIlroy prop osed a software industry based on reusable comp onents, serv- ing roughly the same role that chips do in the hardware industry. After 30 years, McIlroy's vision is b ecoming a reality. In particular, the C++ Standard Template Library STL is an ANSI standard and is b eing shipp ed with C++ compilers. While considerable attention has b een given to techniques for developing comp onents, little is known ab out testing these comp onents. This pap er describ es an STL conformance test suite currently under development. Test suites for all of the STL containers have b een written, demonstrating the feasi- bility of thorough and highly automated testing of industrial comp onent libraries. We describ e a ordable test suites that provide go o d co de and b oundary value coverage, including the thousands of cases that naturally o ccur from combinations of b oundary values. We showhowtwo simple oracles can provide fully automated output checking for all the containers. We re ne the traditional categories of black-b ox and white-b ox testing to sp eci cation-based, implementation-based and implementation-dep endent testing, and showhow these three categories highlight the key cost/thoroughness trade- o s. 1 Intro duction Our testing fo cuses on container classes |those providing sets, queues, trees, etc.|rather than on graphical user interface classes. Our approach is based on programmatic testing where the number of inputs is typically very large and b oth the input generation and output checking are under program control.
    [Show full text]
  • Readings Findmin Problem Priority Queue
    Readings • Chapter 6 Priority Queues & Binary Heaps › Section 6.1-6.4 CSE 373 Data Structures Winter 2007 Binary Heaps 2 FindMin Problem Priority Queue ADT • Quickly find the smallest (or highest priority) • Priority Queue can efficiently do: item in a set ›FindMin() • Applications: • Returns minimum value but does not delete it › Operating system needs to schedule jobs according to priority instead of FIFO › DeleteMin( ) › Event simulation (bank customers arriving and • Returns minimum value and deletes it departing, ordered according to when the event › Insert (k) happened) • In GT Insert (k,x) where k is the key and x the value. In › Find student with highest grade, employee with all algorithms the important part is the key, a highest salary etc. “comparable” item. We’ll skip the value. › Find “most important” customer waiting in line › size() and isEmpty() Binary Heaps 3 Binary Heaps 4 List implementation of a Priority BST implementation of a Priority Queue Queue • What if we use unsorted lists: • Worst case (degenerate tree) › FindMin and DeleteMin are O(n) › FindMin, DeleteMin and Insert (k) are all O(n) • In fact you have to go through the whole list • Best case (completely balanced BST) › Insert(k) is O(1) › FindMin, DeleteMin and Insert (k) are all O(logn) • What if we used sorted lists • Balanced BSTs › FindMin and DeleteMin are O(1) › FindMin, DeleteMin and Insert (k) are all O(logn) • Be careful if we want both Min and Max (circular array or doubly linked list) › Insert(k) is O(n) Binary Heaps 5 Binary Heaps 6 1 Better than a speeding BST Binary Heaps • A binary heap is a binary tree (NOT a BST) that • Can we do better than Balanced Binary is: Search Trees? › Complete: the tree is completely filled except • Very limited requirements: Insert, possibly the bottom level, which is filled from left to FindMin, DeleteMin.
    [Show full text]
  • Chapter C4: Heaps and Binomial / Fibonacci Heaps
    Chapter C4: Heaps and Binomial / Fibonacci Heaps C4.1 Heaps A heap is the standard implementation of a priority queue. A min-heap stores values in nodes of a tree such that heap-order property: for each node, its value is smaller than or equal to its children’s So the minimum is on top. (A max-heap can be defined similarly.) The standard heap uses a binary tree. Specifically, it uses a complete binary tree, which we define here to be a binary tree where each level except the last is full, and in the last level nodes are added left to right. Here is an example: 7 24 19 25 56 68 40 29 31 58 Now, any binary tree can be stored in an array using level numbering. The root is in cell 0, cells 1 and cells 2 are for its children, cells 3 through 6 are for its grandchildren, and so on. Note that this means a nice formula: if a node is in cell x,thenitschildren are in 2x+1 and 2x+2. Such storage can be (very) wasteful for a general binary tree; but the definition of complete binary tree means the standard heap is stored in consecutive cells of the array. C4.2 Heap Operations The idea for insertion is to: Add as last leaf, then bubble up value until heap-order property re-established. Insert(v) add v as next leaf while v<parent(v) { swapElements(v,parent(v)) v=parent(v) } One uses a “hole” to reduce data movement. Here is an example of inserting the value 12: 7 7 24 19 12 19 ) 25 56 68 40 25 24 68 40 29 31 58 12 29 31 58 56 The idea for extractMin is to: Replace with value from last leaf, delete last leaf, and bubble down value until heap-order property re-established.
    [Show full text]
  • Nearest Neighbor Searching and Priority Queues
    Nearest neighbor searching and priority queues Nearest Neighbor Search • Given: a set P of n points in Rd • Goal: a data structure, which given a query point q, finds the nearest neighbor p of q in P or the k nearest neighbors p q Variants of nearest neighbor • Near neighbor (range search): find one/all points in P within distance r from q • Spatial join: given two sets P,Q, find all pairs p in P, q in Q, such that p is within distance r from q • Approximate near neighbor: find one/all points p’ in P, whose distance to q is at most (1+ε) times the distance from q to its nearest neighbor Solutions Depends on the value of d: • low d: graphics, GIS, etc. • high d: – similarity search in databases (text, images etc) – finding pairs of similar objects (e.g., copyright violation detection) Nearest neighbor search in documents • How could we represent documents so that we can define a reasonable distance between two documents? • Vector of word frequency occurrences – Probably want to get rid of useless words that occur in all documents – Probably need to worry about synonyms and other details from language – But basically, we get a VERY long vector • And maybe we ignore the frequencies and just idenEfy with a “1” the words that occur in some document. Nearest neighbor search in documents • One reasonable measure of distance between two documents is just a count of words they share – this is just the point wise product of the two vectors when we ignore counts.
    [Show full text]
  • Rethinking Host Network Stack Architecture Using a Dataflow Modeling Approach
    Research Collection Doctoral Thesis Rethinking host network stack architecture using a dataflow modeling approach Author(s): Shinde, Pravin Publication Date: 2016 Permanent Link: https://doi.org/10.3929/ethz-a-010657596 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library DISS.ETH NO. 23474 Rethinking host network stack architecture using a dataflow modeling approach A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by Pravin Shinde Master of Science, Vrije Universiteit, Amsterdam born on 15.10.1982 citizen of Republic of India accepted on the recommendation of Prof. Dr. Timothy Roscoe, examiner Prof. Dr. Gustavo Alonso, co-examiner Dr. Kornilios Kourtis, co-examiner Dr. Andrew Moore, co-examiner 2016 Abstract As the gap between the speed of networks and processor cores increases, the software alone will not be able to handle all incoming data without additional assistance from the hardware. The network interface controllers (NICs) evolve and add supporting features which could help the system increase its scalability with respect to incoming packets, provide Quality of Service (QoS) guarantees and reduce the CPU load. However, modern operating systems are ill suited to both efficiently exploit and effectively manage the hardware resources of state-of-the-art NICs. The main problem is the layered architecture of the network stack and the rigid interfaces. This dissertation argues that in order to effectively use the diverse and complex NIC hardware features, we need (i) a hardware agnostic representation of the packet processing capabilities of the NICs, and (ii) a flexible interface to share this information with different layers of the network stack.
    [Show full text]
  • Jt-Polys-Cours-11.Pdf
    Notes de cours Standard Template Library ' $ ' $ Ecole Nationale d’Ing´enieurs de Brest Table des mati`eres L’approche STL ................................... 3 Les it´erateurs .................................... 15 Les classes de fonctions .......................... 42 Programmation par objets Les conteneurs ................................... 66 Le langage C++ Les adaptateurs ................................. 134 — Standard Template Library — Les algorithmes g´en´eriques ...................... 145 Index ........................................... 316 J. Tisseau R´ef´erences ...................................... 342 – 1996/1997 – enib c jt ........ 1/344 enib c jt ........ 2/344 & % & % Standard Template Library L’approche STL ' $ ' $ L’approche STL Biblioth`eque de composants C++ g´en´eriques L’approche STL Fonctions : algorithmes g´en´eriques 1. Une biblioth`eque de composants C++ g´en´eriques sort, binary search, reverse, for each, accumulate,... 2. Du particulier au g´en´erique Conteneurs : collections homog`enes d’objets 3. Des indices aux it´erateurs, via les pointeurs vector, list, set, map, multimap,... 4. De la g´en´ericit´edes donn´ees `ala g´en´ericit´edes structures It´erateurs : sorte de pointeurs pour inspecter un conteneur input iterator, random access iterator, ostream iterator,... Objets fonction : encapsulation de fonctions dans des objets plus, times, less equal, logical or,... Adaptateurs : modificateurs d’interfaces de composants stack, queue, priority queue,... enib c jt ........ 3/344 enib c jt ........ 4/344 & % & % L’approche STL L’approche STL ' $ ' $ Du particulier . au g´en´erique int template <class T> max(int x, int y) { return x < y? y : x; } const T& max(const T& x, const T& y) { return x < y? y : x; } int template <class T, class Compare> max(int x, int y, int (*compare)(int,int)) { const T& return compare(x,y)? y : x; max(const T& x, const T& y, Compare compare) { } return compare(x, y)? y : x; } enib c jt .......
    [Show full text]
  • IBM Spectrum Scale 5.1.0: Concepts, Planning, and Installation Guide Summary of Changes
    IBM Spectrum Scale Version 5.1.0 Concepts, Planning, and Installation Guide IBM SC28-3161-02 Note Before using this information and the product it supports, read the information in “Notices” on page 551. This edition applies to version 5 release 1 modification 0 of the following products, and to all subsequent releases and modifications until otherwise indicated in new editions: • IBM Spectrum Scale Data Management Edition ordered through Passport Advantage® (product number 5737-F34) • IBM Spectrum Scale Data Access Edition ordered through Passport Advantage (product number 5737-I39) • IBM Spectrum Scale Erasure Code Edition ordered through Passport Advantage (product number 5737-J34) • IBM Spectrum Scale Data Management Edition ordered through AAS (product numbers 5641-DM1, DM3, DM5) • IBM Spectrum Scale Data Access Edition ordered through AAS (product numbers 5641-DA1, DA3, DA5) • IBM Spectrum Scale Data Management Edition for IBM® ESS (product number 5765-DME) • IBM Spectrum Scale Data Access Edition for IBM ESS (product number 5765-DAE) Significant changes or additions to the text and illustrations are indicated by a vertical line (|) to the left of the change. IBM welcomes your comments; see the topic “How to send your comments” on page xxxii. When you send information to IBM, you grant IBM a nonexclusive right to use or distribute the information in any way it believes appropriate without incurring any obligation to you. © Copyright International Business Machines Corporation 2015, 2021. US Government Users Restricted Rights – Use,
    [Show full text]