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Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
When the program requires the sine of a value, it can use the lookup table to retrieve the closest sine value from a memory address, and may also interpolate to the sine of the desired value, instead of calculating by mathematical formula. Lookup tables can thus used by mathematics coprocessors in computer systems.
Since 7 October 2024, Python 3.13 is the latest stable release, and it and, for few more months, 3.12 are the only releases with active support including for bug fixes (as opposed to just for security) and Python 3.9, [55] is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.8 reaching end-of-life.
The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot; substitution: coat → cost
A parsing expression is a kind of pattern that each string may either match or not match.In case of a match, there is a unique prefix of the string (which may be the whole string, the empty string, or something in between) which has been consumed by the parsing expression; this prefix is what one would usually think of as having matched the expression.
The match-to-sample task has been shown to be an effective tool to understand the impact of sleep deprivation on short-term memory. One research study [9] compared performance on a traditional sequential test battery with that on a synthetic work task requiring subjects to work concurrently on several tasks, testing subjects every three hours during 64 hrs of sleep deprivation.
[1] [2] [3] Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, [4] but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. [5]
The projection function transforms the 3-D point and strips away the component of depth. Without knowing the depth of the component an inverse projection function can only return a set of possible 3D points, that form a line emanating from the nodal point of the camera lens and passing through the projected 2-D point. We can express the inverse ...