| ROBOT |
ロボスタ |
レース場のレーサーに移動が困難な障がい者が遠隔から握手と花束贈呈の体験へ 力触覚技術「リアルハプティクス」を活用 |
https://robotstart.info/2023/08/30/remote-handshake-to-racer.html
|
レース場のレーサーに移動が困難な障がい者が遠隔から握手と花束贈呈の体験へ力触覚技術「リアルハプティクス」を活用シェアツイートはてブモーションリブは、トヨタ・モビリティ基金が主催する「もっといいモビリティ社会」をつくるアイデアコンテスト「MobilityforALL」に採択。 |
2023-08-30 03:40:36 |
| ROBOT |
ロボスタ |
オルツが電通デジタル代表の瀧本氏のクローン(デジタルヒューマン)を「CLONEdev」で制作 全社員向けのイベントで公開 |
https://robotstart.info/2023/08/30/pai-clonedev-dentsu.html
|
|
2023-08-30 03:12:44 |
| IT |
ITmedia 総合記事一覧 |
[ITmedia News] 「PayPayポイントアップ店」開始 「月曜は＋1％」など加盟店が独自にポイント加算 |
https://www.itmedia.co.jp/news/articles/2308/30/news129.html
|
itmedia |
2023-08-30 12:42:00 |
| IT |
ITmedia 総合記事一覧 |
[ITmedia News] 夏休みの自由研究、ChatGPTにやらせてみた 結構悪くないが意味がない |
https://www.itmedia.co.jp/news/articles/2308/30/news127.html
|
chatgpt |
2023-08-30 12:30:00 |
| IT |
ITmedia 総合記事一覧 |
[ITmedia News] NEC、生成AIで「議事録作成30分→5分」に サイバー攻撃訓練メールやFAQ作成にも活用 |
https://www.itmedia.co.jp/news/articles/2308/30/news125.html
|
itmedianewsnec |
2023-08-30 12:26:00 |
| TECH |
Techable（テッカブル） |
人間×AIでパワポスライドを作るノウハウを伝授する1冊！短時間でハイクオリティに仕上げるには？ |
https://techable.jp/archives/218378
|
chatgpt |
2023-08-30 03:00:59 |
| TECH |
Techable（テッカブル） |
不動産買取再販業者向け「不動産仕入自動提案AIサービス」開始。交渉金額目安・割安度など配信 |
https://techable.jp/archives/218499
|
株式会社 |
2023-08-30 03:00:43 |
| Ruby |
Railsタグが付けられた新着投稿 - Qiita |
RailsにBootstrapを導入 |
https://qiita.com/ryuuya0921/items/bcc2acbc8beb5d0c7fb1
|
gemfilegembootstrapgtgem |
2023-08-30 12:32:24 |
| 海外TECH |
DEV Community |
Data structures and algorithms for multi-language programmers: C++, Swift, Python, Java, C#, JavaSCript. |
https://dev.to/khaledhosseini/data-structures-and-algorithms-for-multi-language-programmers-c-swift-python-java-c-javascript-alp
|
Data structures and algorithms for multi language programmers C Swift Python Java C JavaSCript Within the realm of programming languages predefined data structures are a common inclusion albeit with variations in names and implementation details across different languages Although these discrepancies exist the fundamental principles underlying these structures remain consistent In this article we embark on an immersive exploration of data structures delving into their internal implementations across a range of programming languages To aid comprehension we enhance the learning experience with concise animations offering an intuitive grasp of these essential concepts Although the title specifically mentions Multi language programmers it is evident that mono language programmers can also derive significant benefits from this article This article marks the beginning of a series where we delve into a range of essential topics The series will cover the following main subjects Linear data structuresNon linear data structuresAlgorithms Memory ⚬ Physical layer ⚬ Virtual layer Location Arrangement Algorithms ⚬ Fundamental operations ⚬ Fundamental Algorithms Sorting algorithms Searching algorithms ⚬ Algorithm design techniques Data structures ⚬ Contiguous Memory data structures ⚬ Discontiguous Memory data structures ⚬ Combination of CM and DCM ⚬ Linear data structures Array DynamicArray RingBuffer LinkedList DoubleLinkedList CircularLinkedList CircularDoubleLinkedList Stack Stack via DynamicArray Stack via LinkedList Stack via deque Queue Queue via DoubleLinkedList Queue via RingBuffer Queue via Double Stack Deque as Queue Deque Deque via DoubleLinkedList Deque via Array PriorityQueue PriorityQueue via DynamicArray PriorityQueue via LinkedList PriorityQueue via Deque PriorityQueue via BinaryHeap Associative collections UnorderedMap or HashTable OrderedMap via HashTable and LinkedList OrderedMap via HashTable and DynamicArray SortedMap via Self Balancing Tree Set UnorderedSet OrderedSet via HashTable and LinkedList SortedSet via Self Balancing Tree standard library data structures ⚬ C ⚬ Swift ⚬ CSharp ⚬ Python ⚬ Java Non Linear data structures AlgorithmsSuppose you have a collection of data blocks denoted as A B C D E and so on Your objective is to solve a problem by employing an algorithm that processes this data and produces a result Irrespective of the specific problem or algorithm you opt for there are certain steps that need to be followed Allocate some space memory to your data Arrange your data blocks in the allocated space and create a logical relationship Implicit or explicit among them Specify a Data Structure Doing Some operations on the data blocks Algorithm These operations may include Read data blocksWrite data blocksData structures and algorithms are closely intertwined concepts Certain algorithms exhibit better efficiency when used with specific data structures and conversely certain data structures offer advantageous arrangements that enhance the efficiency of particular algorithms To efficiently solve problems it is crucial to design both efficient algorithms and appropriate data structures Designing these efficient solutions necessitates a solid understanding of the fundamentals and analysis techniques involved Memory Physical layerThe physical layer of a computer system is responsible for the actual storage and retrieval of data in electronic or magnetic form Memory in the physical layer is organized hierarchically with different types and levels of memory Types of Memory in the Physical Layer RegistersCache MemoryMain Memory Random Access Memory RAM Secondary memories HDD SSD Physical memory is invisible to programs in virtual memory systems and as a programmer you re not required to reason about it Memory Virtual layerWhere and how memory is being allocated in virtual layer LocationStack Fast allocation Faster access Moving just an integer pointer allocates de allocates memory Heap Slow Allocation Slower access Search heap Sync heap for other threads Allocate memory ArrangementContiguous Bulk allocation in continuous memory block faster access Discontiguous Dynamic allocation in separated memory blocks slower access AlgorithmsAt the core of all algorithms are the fundamental operations that involve accessing and potentially mutating the data blocks regardless of how we arrange our data blocks in memory and what are the logical connections between them At this level all algorithms can be reduced to one or some of the following operations Fundamental operationsreadaccessDataBySequence Either forward or backward getIndexingInformation getStartIndex getEndIndex getNextIndex forIndex getPreviousIndex forIndex accessDataAtRandomIndex For Random access time complexity should be of order O accessDataAtFront accessDataAtBack writeinsertDataAtRandomIndex insertDataAtFront insertDataAtBack removeDataAtRandomIndex removeDataAtFront removeDataAtBack updateDataAtRandomIndex updateDataAtFront updateDataAtBack For example Linear search algorithm uses accessDataBySequence and compare each item with a specified value to find the answer while Binary search algorithm needs accessDataAtRandomIndex operation A note on Random Access In the context of data structures random access refers to the ability to instantly access a specific location With Array for instance if you select a random index the Array data structure can immediately provide you with the address of that index However if you attempt to access a random index in a LinkedList the data structure cannot instantaneously provide the address Instead it must iterate from the beginning starting from head until it reaches the desired index Consequently LinkedLists are considered to have a time complexity of O n Upper bound for random access operation Most algorithms require O random access and languages such as Java have introduced a marker interface with no methods called RandomAccess This interface serves as a reminder that certain algorithms rely on random access To ensure that these algorithms perform efficiently with your data structure it is necessary to make it compatible with random access The Swift equivalent is a marker protocol RandomAccessCollection Fundamental AlgorithmsFundamental operations form the building blocks upon which algorithms are constructed Conversely certain algorithms play fundamental rules for other algorithms Take for instance the impact of input data order on the time efficiency of algorithms Sorting the data beforehand can greatly simplify our lives as it has a significant positive effect on the efficiency of numerous algorithms Sorting can be accomplished through two methods The first method involves utilizing a sorting algorithm to arrange an unsorted collection The second method involves utilizing specific data structures such as binary search trees that facilitate the sorting of data through amortization Sorting algorithmsAll sort algorithms need getIndexingInformation accessDataAtRandomIndex operations Also items must be comparable unless for non comparison algorithms In place sorting algorithms They need updateDataAtRandomIndex operation Bubble sortSelection sorInsertion sortHeap sortQuick sortNot In Place Sorting Algorithms Merge sortRadix sort non comparison Bucket sort non comparison Searching algorithmsLinear search needs accessDataBySequence Binary search needs accessDataAtRandomIndex with O Algorithm design techniquesDivide and conquerRecursionRandomized algorithms Input MUST be RANDOM Dynamic programmingGreedy algorithmsIn a next article I will return to the algorithms Data structuresEach data structure has the following characteristics Virtual layer Memory management at the virtual layer Logical connection between data blocks either implicit or explicit Implicit In an Array data blocks have no direct connection but implicitly they are arranged in a specific order contiguously in memory Explicit In LinkedList the blocks may not be stored contiguously in memory but each node has the connection information to some other nodes Rules for applying basic operations Provides basic read and write operations with a space time complexity The space time complexities for data structures for basic operations can easily be analyzed using the following concepts Contiguous Memory data structures and Discontiguous Memory data structures Contiguous Memory data structuresInit with fixed size size stays fixed Address of each block can be calculated via start k blocksize Random access time complexity is O Bulk memory allocationSame size memory blocks Same type Base data Structure example Array Discontiguous Memory data structuresThis arrangement is a special kind of Graph We can represent graphs using it Each block contains the address of next block Time complexity for random access operation is O n Dynamic memory allocationMemory block sizes can be different Different types Base data structure example LinkedList Combination of CM and DCMA contiguous memory array of pointers to contiguous memory or discontiguous memory collection of objects Time complexity for random access operations is O via array of pointers but accessing objects in non continuous memory have a little overhead Bulk memory allocation for address pointer array dynamic memory allocation for objects Objects can have different memory sizes different types Base data structure example An array of referenced objects in most programming languages Linear data structuresBy employing one or a combination of the aforementioned concepts basic data structures can be implemented serving as the foundation for more intricate data structures Additionally the space and time complexities as well as memory costs can be readily analyzed by leveraging the complexities and costs associated with these fundamental concepts ArrayIn Programming languages Arrays are built in types Array of pointers or array of reference types acts like Combination of CM and DCM For primitive types or value types like Int enum struct in C Swift the behavior is like Contiguous Memory data structures Basic operations time complexity Same as Contiguous Memory data structuresGood accessAtRandomIndex insertAtBack removeAtBack operations Bulk memory allocation fast Contiguous memory Fast access If used with primitive types Value types no dynamic memory allocation cost Not good insertAtFront insertAtMiddle removeAtFront removeAtMiddle Operations Fixed size Programming Languages implementations CPP Array size is compile time constant Swift Arrays in swift are dynamic Python Python array size is compile time constant Java array size is compile time constant C Array size is compile time constant JavaScript Arrays in Javascript are dynamic DynamicArraySimilar to array but can grow at runtime DynamicArray of pointers or DynamicArray of reference types acts like Combination of CM and DCM For primitive types or value types like Int enum struct in C Swift the behavior is like Contiguous Memory data structures Steps for resizing allocate new array with new sizecopy the old array values to the new arraydelete the old arrayBasic operations time complexity Same as Contiguous Memory data structuresGood accessAtRandomIndex insertAtBack removeAtBack operations Bulk memory allocation fast If used with primitive types Value types no dynamic memory allocation cost Not good insertAtFront insertAtMiddle removeAtFront removeAtMiddle Operations New memory allocations and copy cost when capacity is full Has unused memory allocation based on growth strategy For example in Swift programming language each time an array capacity is full it double the capacity of the array Programming Languages implementations CPP Vector Swift contiguousarray and array are dynamic When capacity is full the size gets doubled Python list is a dynamic array of pointers to other objects The behavior is always like Combination of CM and DCM UserList is a wrapper class that allows you to create your own list like objects by inheriting from UserList and implementing certain methods It provides a convenient way to create custom list like classes without directly subclassing the built in list class Java ArrayList and Vector are dynamic and the difference is that vector is thread safe C ArrayList and List are dynamic arrays The difference is that ArrayList is non generic and can store elements of any while List lt T gt is a generic class that provides type safe collections JavaScript When it comes to Javascript things are a little bit different Array is dynamic and you can add multiple types to it As Array is an Object and Objects in javascript are HashTables you can access indices of array using string of indices too Depending on the type of the values the behavior of Javascript array is different In V Engine when Array only contains a single primitive type like integer float it ll be backed by a C array of that type and the behavior is like Contiguous Memory data structures When Array contains more than one of primitive types the array will be backed by a C array of the bigger one and the behavior is the same as above If the array contains only objects or a mixture of numbers and objects it ll backed by an array of pointers primitive types will be boxed inside objects The behavior is like Combination of CM and DCM When you have a sparse array WHY If it is not too spare it ll still be backed by an array with empty array indices replaced with a hole value If an array is very sparse it ll no longer be backed by an array in memory Instead it will be backed by a dictionary hashtable The key is typically stored as a string representation of the index and the value is the element itself RingBufferA ring buffer is a specialized data structure implemented using an array It is a static sized buffer where read and write operations occur through two distinct pointers that iterate through the array in a circular manner Basic operations time complexity Same as Array with the following improvement insertAtFront is O removeAtFront is O Good accessAtRandomIndex insert operation Bulk memory allocation fast If used with primitive types Value types no dynamic memory allocation cost As it is fixed size we can map it to virtual memory layer memory page to make it super fast Not good Fixed size Write operations may fail if the frequency of writes exceeds the frequency of reads Programming Languages implementations CPP Has no built in implementation for LinkedList Here is an implementation Swift Has no built in implementation for LinkedList Here is an implementation Python Has no built in implementation for LinkedList Here is an implementation Java Has no built in implementation for LinkedList Here is an implementation C Has no built in implementation for LinkedList Here is an implementation JavaScript Has no built in implementation for LinkedList Here is an implementation LinkedListBasic operations time complexity Same as Discontiguous Memory data structures with one improvement insertAtBack becomes O because we keep track of tail removeAtBack stays O n because we have to iterate from head to index n to remove n Good insertAtFront removeAtFront insertAtBack operations Not good accessAtRandomIndex removeAtBack insertAtMiddle removeAtMiddle Operations Dynamic memory allocation slow Programming Languages implementations CPP forward list Swift Has no built in implementation for LinkedList An implementation can be found here Python Has no built in implementation for LinkedList An implementation can be found here Java LinkedList is DoubleLinkedList C LinkedList is a DoubleLinkedList JavaScript Has no built in implementation for LinkedList an implementation can be found here DoubleLinkedListBasic operations time complexity Same as Discontiguous Memory data structures with two improvements insertAtBack becomes O removeAtBack becomes O Now we have access to n from n and we can remove the pointer to n from n Good insertAtFront removeAtFront insertAtBack removeAtBack operations Not good accessAtRandomIndex insertAtMiddle Operations Dynamic memory allocation slow High overhead of extra storage for the forward and back reference Programming Languages implementations CPP list is doubly linkedList Swift Has no built in implementation for DoubleLinkedList An implementation can be found here Python Has no built in implementation for DoubleLinkedList an implementation can be found here Java LinkedList is DoubleLinkedList C LinkedList is DoubleLinkedList JavaScript Has no built in implementation for DoubleLinkedList An implementation can be found here CircularLinkedListBasic operations time complexity Same as LinkedList with some more capabilities We can traverse to a previous nodeWe can traverse in loop CircularDoubleLinkedListBasic operations time complexity Same as DoubleLinkedList with some more capabilities We can traverse to a previous nodeWe can traverse in loop in both direction StackStack is a Last In First Out LIFO data structure Any data structure that is Good at insert remove from one of the ends can be used as a container for Stack Based on this stacks can be implemented using DynamicArray Good at add remove from the back LinkedList Good at add remove from front DoubleLinkedList Good at add remove from both front and back and Deque Each implementation inherits Good and Not Good of the container data structure Stack via DynamicArrayBasic operations time complexity Same as DynamicArray Methods push insertAtBack on array container pop removeAtBack on array container Good push and pop are O operations Bulk memory allocation for pointers If used with primitive types value types no dynamic memory allocation cost Not good New memory allocations and copy cost when internal array capacity is full Has unused memory allocation based on growth strategy of the pointer array Programming Languages implementations CPP Stack In CPP vector deque and list DoubleLinkedList can be used as container for Stack Swift Has no Stack in standard library an implementation can be found here Python Has no built in Stack in standard library but list can be used as stack in python An implementation can be found here Java Stack is implemented with dynamic array C Stack is implemented with dynamic array as the container JavaScript has no built in stack data structure an implementation can be found here Stack via LinkedListBasic operations time complexity Same as LinkedList We use Head of LinkedList to insert remove Methods push insertAtFront on LinkedList container pop removeAtFront on LinkedList container Good push and pop are O operations Not good accessAtRandomIndex is O n Dynamic memory allocation slow Stack via dequeDeque data structure can be implemented using Deque via DoubleLinkedList or Deque via Array The Deque can serve as a container for a Stack due to its behavior C default container for Stack is deque QueueQueue data structure is First In First Out Every data structure that is Good at addAtFront and removeAtBack or vice versa can be used as a container for Queue data structure DoubleLinkedList Good at add remove at both ends can be used as the containers for Queue data structure Also RingBuffer can be used for fixed size queues DynamicArray is not a good container for queue data structure because of O n for insert operation We can amortize this complexity using Queue via Double Stack Stack via DynamicArray Another approach is storing contents in multiple smaller arrays allocating additional arrays at the beginning or end as needed Indexing is implemented by keeping a dynamic array or a LinkedList containing pointers to each of the smaller arrays In this case the cost of inserting reduced from O n to the O small array length This approach is used for deque Queue via DoubleLinkedListBasic operations time complexity DoubleLinkedListMethods enqueue insertAtFront on DoubleLinkedList container dequeue removeAtBack on DoubleLinkedList container Good enqueue and dequeue are O operations Not good accessAtRandomIndex operation Extra memory for forward backward pointers Dynamic memory allocation slow Programming Languages implementations CPP queue in cpp can has deque or list DoubleLinkedList as the container the default container is deque Swift Has no built in implementation for Queue An implementation can be found here Python Has no built in implementation for Queue but list can be used as queue in python An implementation can be found here Java LinkedList and ArrayDeque have implemented Queue interface C Queue in c uses circular buffer array JavaScript an implementation can be found here Queue via RingBufferBasic operations time complexity RingBufferMethods enqueue insertAtRandomIndex on Array container dequeue accessAtRandomIndex on Array container Good enqueue and dequeue are O operations If used for primitive types value types No dynamic allocation Not good Fixed size enqueue may fail Programming Languages implementations C Queue in c uses circular buffer array Queue via Double StackIf we use DynamicArray as container for our queue the dequeue time complexity would be O n Adding items to start of an array is an O n operation But we can amortize this complexity to O using two stacks LeftStack for enqueue and the RightStack for dequeue Each time the LeftStack is empty copy the RightStack contents to the LeftStack This operation guarantees First In First Out for the queue Basic operations time complexity Similar to Stack via DynamicArray Methods enqueue insertAtBack on left Array container the enqueue stack dequeue removeAtBack on right Array container the dequeue stack Good enqueue and dequeue are O operations If used for primitive types value types No dynamic allocation Not good New memory allocations and copy cost when capacity is full Has unused memory allocation based on growth strategy Deque as QueueDeque Double Ended Queue can be used as Queue DequeDeque Double Ended Queue are a type of Queue that enqueue and dequeue can happen at both ends Every data structure that is Good at insert remove from both ends can be used as a container for Deque data structure The only data structure that fullfil this requirement is DoubleLinkedList Array is not a good data structure for implementing Deque data structure directly However we can use some tricks to use Array as a container for Deque data structure See Deque via Array Deque via DoubleLinkedListImplementing a Deque via DoubleLinkedList is straightforward as this data structure has O for insertAtFront removeAtFront and insertAtBack removeAtBack operations Methods pushBack insertAtBack of the DoubleLinkedList container pushFront insertAtFront of the DoubleLinkedList container popBack removeAtBack of the DoubleLinkedList container popFront removeAtFront of the DoubleLinkedList container Good Easy implementationNot Good Random access operation Dynamic memory allocation slow High overhead of extra storage for the forward and back references Programming Languages implementations Python deque uses DoubleLinkedList internally Deque via ArrayAs it was the case for Queue data structure Array cannot be used as a container for Deque data structure directly because insertAtFront removeAtFront operations are not O for Arrays We can use one of the following techniques to use Array as a container Using a special RingBuffer Using an Array and allocating deque contents from the center of the underlying array and resizing the underlying array when either end is reached Storing contents in multiple smaller arrays allocating additional arrays at the beginning or end as needed Indexing is implemented by keeping a dynamic array containing pointers to each of the smaller arrays In this case the cost of resizing the array in step is eliminated but different small arrays are not allocated contiguously in memory Good Random Access operationNot GoodMore complex implementationNeed for array resize when filledProgramming Languages implementations CPP Deque uses approach of above mentioned tricks to use Array as container for Deque In this approach data is stored in smaller arrays and these arrays are linked using a doubleLinkedList or another array Swift Has no built in implementation for LinkedList An implementation can be found here Python deque uses DoubleLinkedList internally Java ArrayDeque is implemented using technique of above mentioned tricks Circular buffer C Deque is implemented using technique of above mentioned tricks Circular buffer JavaScript An implementation can be found here PriorityQueuePriorityQueue is the same as Queue with one difference The dequeue operation is not for the first item that has been inserted Instead the dequeue item is selected based on a priority criteria and the item may be at the front the middle or the end of the collection Any data structure that is Good at inserting at one of the ends can be used as a container for PriorityQueue As finding the item to be dequeued includes a searching phase for linear data structures as the container for PriorityQueue the time complexity of dequeue operation is O n In case of Heap data structure as the container the time complexity reduces to O log n due to internal structure of the Heap PriorityQueue via DynamicArrayMethods enqueue insertAtBack on Array container dequeue iterate and then removeAtMiddle on Array container Time complexity is O n Good enqueue is O operation If used for primitive types value types No dynamic allocation Not good dequeue operation is O n New memory allocations and copy cost when capacity is full Has unused memory allocation based on growth strategy Programming Languages implementations CPP priority queue is using deque as a container by default vector also can be used PriorityQueue via LinkedListMethods enqueue insertAtFront on LinkedList container dequeue iterate and then removeAtMiddle on LinkedList container Time complexity is O n Good enqueue is O operation Not good dequeue operation is O n Dynamic memory allocation slow PriorityQueue via DequeDeque data structure can be implemented using either Deque via DoubleLinkedList or Deque via Array and PriorityQueue can use it as a container PriorityQueue via BinaryHeapMethods enqueue insert on BinaryHeap container dequeue delete on BinaryHeap container Good dequeue is O log n operation Not good enqueue is O log n operation In PriorityQueue via DynamicArray and PriorityQueue via LinkedList this operation is O Programming Languages implementations Java PriorityQueue uses binary heap as internal data structure C PriorityQueue uses binary heap as internal data structure Associative collectionsAn associative collection is an abstract data type that stores a collection of key value pairs ensuring that each possible key appears at most once in the collection However there is no standardized naming convention for these types of data structures leading to varying terminology across different programming languages which can cause confusion Some alternative names for associative collections include associative array map symbol table or dictionary See here UnorderedMap or HashTableOther name is HashTable The main idea behind a Hashtable is to use a hashing function to map keys to specific buckets or slots in an array Each bucket can store one or more key value pairs Hash functions can occasionally generate the same index for different keys resulting in a collision To handle collisions efficiently Hashtable data structures employ various strategies Each bucket in the array is a LinkedList of key value pairs Open addressingResizing the Array For most data structures a linear search is an O n or O log n operation HashTable is a data structure with an amortized O time complexity for searching Length of arrays in a HashTable is a prime number Good O for search operation Not Good Collection has no order No Random access If LinkedList used for collision handling Worst case for search can be O n All nodes collide Average case is not O Programming Languages implementations CPP unordered map is an unordered collection created using HashTable Another version is unordered multimap that allows for repetitive keys in the unordered map version the keys are unique Swift Dictionary is an unordered collection created using HashTable The keys are unique Python dict is an unordered map created using HashTable Also Counter is a dictionary specific to counting of values the key is the item you put in the dictionary and the value is a counter on each insert if the value exists is added to the count UserDict is a wrapper class that allows you to create your own dictionary like objects by inheriting from UserDict and implementing certain methods It provides a convenient way to create custom dictionary like classes without directly subclassing the built in dict class mappingproxy object provides read only access to the original dictionary s data Java HashTable is unordered thread safe HashMap is unordered map created using HashTable C Dictionary is an unordered map created using HashTable ListDictionary uses a combination of array for keys and LinkedList for values Operations are all O n and it MUST be used for small collections Less than items JavaScript Map is an unordered map OrderedMap via HashTable and LinkedListA collection of key value pairs While the order of the insertion is preserved the collection is not sorted Good Order of the insertion is preserved Unlike SortedMap the keys are not sorted accessDataBySequence is possible Not Good No random access with O because of LinkedList High overhead of extra storage for the forward and back reference Programming Languages implementations Python OrderedDic is implemented using a combination of a doubly linked list and a dictionary Java LinkedHashMap In Java the LinkedHashMap class uses a combination of a hash table and a doubly linked list as its internal data structure to provide the functionality of a hash map with predictable iteration order OrderedMap via HashTable and DynamicArrayA collection of key value pairs While the order of the insertion is preserved the collection is not sorted Good Order of the insertion is preserved Unlike SortedMap the keys are not sorted accessDataBySequence is possible accessDataAtRandomIndex is O Not Good insert is O n because of array remove is O n because of array Programming Languages implementations C OrderedDictionary uses a combination of a HashTable and an ArrayList SortedMap via Self Balancing TreeA collection of key value pairs which is sorted by the key Good Search is O log n keys are sorted Not Good Random access is not O Suitable for small number of data Programming Languages implementations CPP map uses Red Black Tree for implementation Another version is multimap which allows duplicate keys In the map version keys are unique Swift Swift has no built in Ordered map using tree data structure You can sort the keys of a dictionary to a collection and iterate that collection Python Swift has no built in Ordered map using tree data structure Java TreeMap is implemented using a Red Black Tree as its internal data structure C SortedDictionary is implemented internally using a self balancing binary search tree called a Red Black Tree SortedList uses two separate arrays one for the keys and the second for the values As the array for the keys is sorted when a new item is inserted the index is found via binary search The time complexity for inserting is O n Binary search is O log n and the items re arrangement is O n JavaScript an implementation can be found here Set UnorderedSetIt is almost exactly like UnorderedMap or HashTable with the distinction that the node has only a key and no value exists In Java it is implemented using HashTable and the values for the nodes are set to a fixed value Good O for search operation Not Good Collection has no order No Random access If LinkedList used for collision handling Worst case for search can be O n Average case is not O Programming Languages implementations CPP unordered set is an unordered collection created using HashTable Another version is unordered multiset that allows for duplicate keys in the unordered set version the keys are unique Swift Set is an unordered collection created using HashTable The keys are unique Python Set is an unordered set created using HashTable frozenset is an immutable set Java HashSet is an unordered set created using HashTable C HashSet is an unordered set created using HashTable JavaScript Set is an unordered set OrderedSet via HashTable and LinkedListIt is almost exactly like OrderedMap via HashTable and LinkedList with the distinction that the node has only a key and no value exists In Java it is implemented using HashTable and the values for the nodes are set to a fixed value Good Order of the insertion is preserved Unlike SortedSet the keys are not sorted Not Good No random access with O because of LinkedList Programming Languages implementations Java LinkedHashSet In Java the LinkedHashSet class uses a combination of a hash table and a doubly linked list as its internal data structure to provide the functionality of a hash set with predictable iteration order SortedSet via Self Balancing TreeGood Search is O log n keys are sorted Not Good Random access is not O Suitable for small number of data Programming Languages implementations CPP set uses Red Black Tree for implementation Another version is multiset which allows duplicate keys In the Set version keys are unique Swift Swift has no built in Ordered set You can sort the keys of a set to a collection and iterate that collection Python Python has no built in Ordered set Java TreeSet is implemented using a Red Black Tree as its internal data structure C SortedSet is implemented internally using a self balancing binary search tree called a Red Black Tree JavaScript An implementation can be found here standard library data structures C SwiftSwift source code for collections can be found here CSharpDotnet source code for collections can be found here PythonSource code for python built in types can be found here Collection module source code is located here JavaJava collections source code is located here Non Linear data structuresComing soon AlgorithmsComing soon |
2023-08-30 03:05:59 |
| 医療系 |
医療介護 CBnews |
全世代型社会保障改革で登場した「複合型サービス」-地域共生社会での介護事業のカタチ（8） |
https://www.cbnews.jp/news/entry/20230830093649
|
介護事業 |
2023-08-30 12:25:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
バリュー投資家が狙う中国株 景気低迷で割安に - WSJ発 |
https://diamond.jp/articles/-/328444
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景気 |
2023-08-30 12:02:00 |
| ビジネス |
東洋経済オンライン |
ジャニー喜多川氏｢性嗜好異常｣の特徴と犯罪性 ｢パラフィリア症｣と呼ばれる疾患で治療が必要だった | 災害･事件･裁判 | 東洋経済オンライン |
https://toyokeizai.net/articles/-/698162?utm_source=rss&utm_medium=http&utm_campaign=link_back
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再犯防止 |
2023-08-30 12:01:00 |
| マーケティング |
MarkeZine |
ヤマハ発動機とBASE FOODがファンコミュニティ運営の意義を問う 9/22無料ウェビナー |
http://markezine.jp/article/detail/43294
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basefood |
2023-08-30 12:30:00 |
| マーケティング |
MarkeZine |
今や単なるECカートシステムにあらず！Webプラットフォームに進化したShopifyの最新技術とは |
http://markezine.jp/article/detail/43302
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最新技術 |
2023-08-30 12:15:00 |
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