Civil Rights
Movements, leaders, victories and the continuing fight for equality.
q2K BHP
Black History Portal
THE BHP WIRE —
HIDDEN TRUTHS
What's New!
Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Katherine Johnson’s mathematical calculations helped guide some of America’s most important early space missions while she confronted the racial and gender barriers faced by Black women in twentieth-century America.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
In mathematics and computer science, a string metric (also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings for approximate string matching or comparison and in fuzzy string searching. A requirement for a string metric (e.g. in contrast to string matching) is fulfillment of the triangle inequality. For example, the strings "Sam" and "Samuel" can be considered to be close.[1] A string metric provides a number indicating an algorithm-specific indication of distance.
The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance).[2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token, grammatical and character-based methods of statistical comparisons.
String metrics are used heavily in information integration and are currently used in areas including fraud detection, fingerprint analysis, plagiarism detection, ontology merging, DNA analysis, RNA analysis, image analysis, evidence-based machine learning, database data deduplication, data mining, incremental search, data integration, malware detection,[3] and semantic knowledge integration.
There also exist functions which measure a dissimilarity between strings, but do not necessarily fulfill the triangle inequality, and as such are not metrics in the mathematical sense. An example of such function is the Jaro–Winkler distance.
| Name | Description | Example |
|---|---|---|
| Hamming distance | Only for strings of the same length. Number of changed characters. | "karolin" and "kathrin" is 3. |
| Levenshtein distance and Damerau–Levenshtein distance | Generalization of Hamming distance that allows for different length strings, and (with Damerau) for transpositions | kitten and sitting have a distance of 3.
|
| Jaro–Winkler distance | JaroWinklerDist("MARTHA","MARHTA") =
| |
| Most frequent k characters | MostFreqKeySimilarity('research', 'seeking', 2) = 2 |
{{cite journal}}: Cite journal requires |journal= (help)
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In mathematics and computer science, a string metric (also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings for approximate string matching or comparison and in fuzzy string searching. A requirement for a string metric (e.g. in contrast to string matching) is fulfillment of the triangle inequality. For example, the strings "Sam" and "Samuel" can be considered to be close. A string metric provides a number indicating an algorithm-specific indication of distance. The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token, grammatical and character-based methods of statistical comparisons. String metrics are used heavily in information integration and are currently used in areas including fraud detection, fingerprint analysis, plagiarism detection, ontology merging, DNA analysis, RNA analysis, image analysis, evidence-based machine learning, database data deduplication, data mining, incremental search, data integration, malware detection, and semantic knowledge integration.
A chemical formula is any of several kinds of notation for expressing the composition and structure of a chemical compound. Chemical formulas use element symbols for the atoms of each chemical element in a compound. There are several conventional types of chemical formula that present different levels of detail. An empirical formula is a type of chemical formula that shows the proportions of each chemical element by writing a subscripted whole number for each element symbol, such as C2H5 for butane, which indicates that butane contains carbon and hydrogen atoms in a 2:5 ratio. Different compounds can share the same empirical formula, such as CH2O for most carbohydrates. A molecular formula is a type of chemical formula that shows the number of atoms of each element contained in a molecule of a compound by writing the number as a subscript for each element symbol, such as C4H10 for butane. There are conventions for the order of the elements, such as the Hill system, to assist readability and to make sorted lists that are readily searched. Molecular formulas for classes of compounds and for polymers of variable length make use of variables, such as in CnH2n+2 for the linear alkanes. A structural formula is a type of chemical formula that shows the pattern of chemical bonds between the atoms, either as explicit lines between the element symbols, or as implied bonds in a notation that makes the bonding pattern clear. Full structural formulas (or "displayed formulas") show all atoms and bonds explicitly, as in the formula for butane pictured. A skeletal formula is a compact graphical notation in which element symbols for carbon (C) and hydrogen (H) are not shown and are implied by the pattern of bond lines. A condensed formula is a type of structural formula that is written as a text string, such as CH3CH2CH2CH3 for butane. Double (and higher-order) bonds are usually shown explicitly, and single bonds are sometimes shown for clarity, as in CH3−CH=CH2 for propylene. Parentheses are used to enclose groups as needed to show the structure, as in CH3CH2(OH)CH2OH for propylene glycol. Complex branched structures can be depicted by using nested parentheses, but cyclic structures, except for small pre-defined groups like phenyl (C6H5), cannot be represented as condensed formulas. Ionic compounds are depicted by sequentially writing the condensed formula for each strongly-bonded group of atoms, as in (NH4)2SO4 for ammonium sulfate, or as [NH4]+2[SO4]2+ to show the electric charges of groups. Condensed formulas distinguish structural isomers, which are compounds having the same molecular formula but different connectivity. To distinguish stereoisomers, and to depict compounds that are too complex to represent as condensed formulas, graphical structural formulas such as skeletal formulas or perspective drawings are used. The term line formula is ambiguous. Depending on context, it is used to mean a condensed formula, because it is written as a line of text, or to mean a displayed formula that shows all bonds as lines. A crystal-chemical formula expresses the composition and some structural information for crystalline solids. In addition to describing compounds, chemical formulas are used to describe chemical groups (such as the methyl group, −CH3) classes of compounds (such as amines, R−NH2), and polymers (such as polyethylene, (CH2CH2)n). Formula types are sometimes intermixed, as in the use of a condensed formula to compactly denote a substituent in a skeletal formula. Chemical formulas use element symbols and graphic symbols, in contrast to a chemical names, which use words. Chemical formulas are intended to be read by people, in contrast to text-string notations such as the SMILES and InChI, which specify chemical structures and can be manipulated by text editors but are intended to be generated and read mainly by computers.
SNOBOL (StriNg Oriented and symBOlic Language) is a series of programming languages developed between 1962 and 1967 at AT&T Bell Laboratories by David J. Farber, Ralph Griswold and Ivan P. Polonsky, culminating in SNOBOL4. It was one of a number of text-string-oriented languages developed during the 1950s and 1960s; others included COMIT and TRAC. Despite the similar name, it is entirely unlike COBOL. SNOBOL4 stands apart from most programming languages of its era by having patterns as a first-class data type, a data type whose values can be manipulated in all ways permitted to any other data type in the programming language, and by providing operators for pattern concatenation and alternation. SNOBOL4 patterns are a type of object and admit various manipulations, much like later object-oriented languages such as JavaScript whose patterns are known as regular expressions. In addition SNOBOL4 strings generated during execution can be treated as programs and either interpreted or compiled and executed (as in the eval function of other languages). SNOBOL4 was quite widely taught in larger U.S. universities in the late 1960s and early 1970s and was widely used in the 1970s and 1980s as a text manipulation language in the humanities. In the 1980s and 1990s, its use faded as newer languages such as AWK and Perl made string manipulation by means of regular expressions fashionable. SNOBOL4 patterns include a way to express BNF grammars, which are equivalent to context-free grammars and more powerful than regular expressions. The "regular expressions" in current versions of AWK and Perl are in fact extensions of regular expressions in the traditional sense, but regular expressions, unlike SNOBOL4 patterns, are not recursive, which gives a distinct computational advantage to SNOBOL4 patterns. (Recursive expressions did appear in Perl 5.10, though, released in December 2007.) The later SL5 (1977) and Icon (1978) languages were designed by Griswold to combine the backtracking of SNOBOL4 pattern matching with more standard ALGOL-like structuring.
In computer science, the Knuth–Morris–Pratt algorithm (or KMP algorithm) is a string-searching algorithm that searches for occurrences of a "word" W within a main "text string" S by employing the observation that when a mismatch occurs, the word itself embodies sufficient information to determine where the next match could begin, thus bypassing re-examination of previously matched characters. The algorithm was conceived by James H. Morris and independently discovered by Donald Knuth "a few weeks later" from automata theory. Morris and Vaughan Pratt published a technical report in 1970. The three also published the algorithm jointly in 1977. Independently, in 1969, Matiyasevich discovered a similar algorithm, coded by a two-dimensional Turing machine, while studying a string-pattern-matching recognition problem over a binary alphabet. This was the first linear-time algorithm for string matching.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →The Greenwood District.