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String functions common to many languages are listed below, including the different names used. The below list of common functions aims to help programmers find the equivalent function in a language. Note, string concatenation and regular expressions are handled in separate pages. Statements in guillemets (« … ») are optional.
In Python, functions are first-class objects that can be created and passed around dynamically. Python's limited support for anonymous functions is the lambda construct. An example is the anonymous function which squares its input, called with the argument of 5:
Beyond syntactic requirements of C/C++, implicit concatenation is a form of syntactic sugar, making it simpler to split string literals across several lines, avoiding the need for line continuation (via backslashes) and allowing one to add comments to parts of strings. For example, in Python, one can comment a regular expression in this way: [21]
Binary splitting method; David V. Chudnovsky & Gregory V. Chudnovsky. Computer algebra in the service of mathematical physics and number theory. In Computers and Mathematics (Stanford, CA, 1986), pp. 09–232, Dekker, New York, 1990. Bruno Haible, Thomas Papanikolaou. Fast multiprecision evaluation of series of rational numbers.
Some languages do not offer string interpolation, instead using concatenation, simple formatting functions, or template libraries. String interpolation is common in many programming languages which make heavy use of string representations of data, such as Apache Groovy, Julia, Kotlin, Perl, PHP, Python, Ruby, Scala, Swift, Tcl and most Unix shells.
JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Template:String split is a convenience wrapper for the split function in Module:String2. The split function splits text at boundaries specified by separator and returns the chunk for the index idx (starting at 1). It can use positional parameters or named parameters (but these should not be mixed):
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]