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Both these rules can be overridden with a global or nonlocal (in Python 3) declaration prior to use, which allows accessing global variables even if there is a masking nonlocal variable, and assigning to global or nonlocal variables. As a simple example, a function resolves a variable to the global scope: >>>
A snippet of Python code with keywords highlighted in bold yellow font. The syntax of the Python programming language is the set of rules that defines how a Python program will be written and interpreted (by both the runtime system and by human readers). The Python language has many similarities to Perl, C, and Java. However, there are some ...
The off-side rule describes syntax of a computer programming language that defines the bounds of a code block via indentation. [ 1 ] [ 2 ] The term was coined by Peter Landin , possibly as a pun on the offside law in association football .
Off-side rule languages: Boo, Cobra, CoffeeScript, F#, Haskell (in do-notation when braces are omitted), LiveScript, occam, Python, Nemerle (Optional; the user may use white-space sensitive syntax instead of the curly-brace syntax if they so desire), Nim, Scala (Optional, as in Nemerle)
Mathematical induction is an inference rule used in formal proofs, and is the foundation of most correctness proofs for computer programs. [ 3 ] Despite its name, mathematical induction differs fundamentally from inductive reasoning as used in philosophy , in which the examination of many cases results in a probable conclusion.
Scott's rule is a method to select the number of bins in a histogram. [1] Scott's rule is widely employed in data analysis software including R, [2] Python [3] and Microsoft Excel where it is the default bin selection method. [4]
In matrix form, Oja's rule can be written = [() ()] (),and the Gram-Schmidt algorithm is = [() ()] (),where w(t) is any matrix, in this case representing synaptic weights, Q = η x x T is the autocorrelation matrix, simply the outer product of inputs, diag is the function that diagonalizes a matrix, and lower is the function that sets all matrix elements on or above the diagonal equal to 0.
The following Python code with the SymPy library will allow for calculation of the values of and to 20 digits of precision: from sympy import * def lag_weights_roots ( n ): x = Symbol ( "x" ) roots = Poly ( laguerre ( n , x )) . all_roots () x_i = [ rt . evalf ( 20 ) for rt in roots ] w_i = [( rt / (( n + 1 ) * laguerre ( n + 1 , rt )) ** 2 ...