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Simple file verification (SFV) is a file format for storing CRC32 checksums of files to verify the integrity of files. SFV is used to verify that a file has not been corrupted, but it does not otherwise verify the file's authenticity.
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:
This is a list of hash functions, including cyclic redundancy checks, checksum functions, and cryptographic hash functions. This list is incomplete ; you can help by adding missing items . ( February 2024 )
In computing, a minimal reproducible example (abbreviated MRE) [1] is a collection of source code and other data files which allow a bug or problem to be demonstrated and reproduced. The important feature of a minimal reproducible example is that it is as small and as simple as possible, such that it is just sufficient to demonstrate the ...
A built-in function, or builtin function, or intrinsic function, is a function for which the compiler generates code at compile time or provides in a way other than for other functions. [23] A built-in function does not need to be defined like other functions since it is built in to the programming language. [24]
HumanEval: Programming problems where the solution is always a python function, often just a few lines long. [27] The benchmark scores are of the following kinds: pass@n: The model is given attempts to solve each problem. If any attempt is correct, the model earns a point.
In Python, functions are first-class objects, just like strings, numbers, lists etc. This feature eliminates the need to write a function object in many cases. Any object with a __call__() method can be called using function-call syntax. An example is this accumulator class (based on Paul Graham's study on programming language syntax and ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...