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The S-parameter for a 1-port network is given by a simple 1 × 1 matrix of the form () where n is the allocated port number. To comply with the S-parameter definition of linearity, this would normally be a passive load of some type.
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
Syntax highlighting and indent style are often used to aid programmers in recognizing elements of source code. This Python code uses color-coded highlighting. In computer science, the syntax of a computer language is the rules that define the combinations of symbols that are considered to be correctly structured statements or expressions in ...
In computer programming, two notions of parameter are commonly used, and are referred to as parameters and arguments—or more formally as a formal parameter and an actual parameter. For example, in the definition of a function such as y = f(x) = x + 2, x is the formal parameter (the parameter) of the defined function.
An output parameter, also known as an out parameter or return parameter, is a parameter used for output, rather than the more usual use for input. Using call by reference parameters, or call by value parameters where the value is a reference, as output parameters is an idiom in some languages, notably C and C++, [ b ] while other languages have ...
With named parameters, it is usually possible to provide the arguments in any order, since the parameter name attached to each argument identifies its purpose. This reduces the connascence between parts of the program. A few languages support named parameters but still require the arguments to be provided in a specific order.
In a programming language, an evaluation strategy is a set of rules for evaluating expressions. [1] The term is often used to refer to the more specific notion of a parameter-passing strategy [2] that defines the kind of value that is passed to the function for each parameter (the binding strategy) [3] and whether to evaluate the parameters of a function call, and if so in what order (the ...
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.