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There are two main types of variable-expanding algorithms for variable interpolation: [3] Replace and expand placeholders: creating a new string from the original one, by find–replace operations. Find variable reference (placeholder), replace it by its variable value. This algorithm offers no cache strategy.
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
In computer character encodings, there is a normal general-purpose space (Unicode character U+0020) whose width will vary according to the design of the typeface. Typical values range from 1/5 em to 1/3 em (in digital typography an em is equal to the nominal size of the font, so for a 10-point font the space will probably be between 2 and 3.3 ...
In general, there are two types of string datatypes: fixed-length strings, which have a fixed maximum length to be determined at compile time and which use the same amount of memory whether this maximum is needed or not, and variable-length strings, whose length is not arbitrarily fixed and which can use varying amounts of memory depending on ...
The term range space has multiple meanings in mathematics: In linear algebra , it refers to the column space of a matrix, the set of all possible linear combinations of its column vectors. In computational geometry , it refers to a hypergraph , a pair (X, R) where each r in R is a subset of X.
The state space or phase space is the geometric space in which the axes are the state variables. The system state can be represented as a vector , the state vector . If the dynamical system is linear, time-invariant, and finite-dimensional, then the differential and algebraic equations may be written in matrix form.
An alternative view can show compression algorithms implicitly map strings into implicit feature space vectors, and compression-based similarity measures compute similarity within these feature spaces. For each compressor C(.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to the vector norm ||~x||.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.