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In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop. All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
Go's foreach loop can be used to loop over an array, slice, string, map, or channel. Using the two-value form gets the index/key (first element) and the value (second element): for index , value := range someCollection { // Do something to index and value }
The naive algorithm for finding the lexicographically minimal rotation of a string is to iterate through successive rotations while keeping track of the most lexicographically minimal rotation encountered. If the string is of length n, this algorithm runs in O(n 2) time in the worst case.
The ordered sequential types are lists (dynamic arrays), tuples, and strings. All sequences are indexed positionally (0 through length - 1) and all but strings can contain any type of object, including multiple types in the same sequence. Both strings and tuples are immutable, making them perfect candidates for dictionary keys (see below).
The length of a string can also be stored explicitly, for example by prefixing the string with the length as a byte value. This convention is used in many Pascal dialects; as a consequence, some people call such a string a Pascal string or P-string. Storing the string length as byte limits the maximum string length to 255.
An example of a Python generator returning an iterator for the Fibonacci numbers using Python's yield statement follows: def fibonacci ( limit ): a , b = 0 , 1 for _ in range ( limit ): yield a a , b = b , a + b for number in fibonacci ( 100 ): # The generator constructs an iterator print ( number )
doc2vec, generates distributed representations of variable-length pieces of texts, such as sentences, paragraphs, or entire documents. [ 14 ] [ 15 ] doc2vec has been implemented in the C , Python and Java / Scala tools (see below), with the Java and Python versions also supporting inference of document embeddings on new, unseen documents.
Function CRC32 Input: data: Bytes // Array of bytes Output: crc32: UInt32 // 32-bit unsigned CRC-32 value // Initialize CRC-32 to starting value crc32 ← 0xFFFFFFFF for each byte in data do nLookupIndex ← (crc32 xor byte) and 0xFF crc32 ← (crc32 shr 8) xor CRCTable[nLookupIndex] // CRCTable is an array of 256 32-bit constants