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Pages in category "Articles with example Python (programming language) code" The following 200 pages are in this category, out of approximately 201 total. This list may not reflect recent changes. (previous page)
On fetal heart tracing (a linear recording of the fetal heart rate) this would usually look like moderate to severe variable decelerations. [6] In overt cord prolapse, the cord can be seen or felt on the vulva or vagina. [1] The main issue with cord prolapse is that, once the cord is prolapsed, it is prone to compression by the foetus and the womb.
Monty Python references appear frequently in Python code and culture; [190] for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo and bar. [190] [191] The official Python documentation also contains various references to Monty Python routines.
The tslearn Python library implements DTW in the time-series context. The cuTWED CUDA Python library implements a state of the art improved Time Warp Edit Distance using only linear memory with phenomenal speedups. DynamicAxisWarping.jl Is a Julia implementation of DTW and related algorithms such as FastDTW, SoftDTW, GeneralDTW and DTW barycenters.
When variable decelerations are associated with uterine contractions, their onset, depth, and duration commonly vary with successive uterine contractions. [citation needed] Prolonged deceleration: a decrease in FHR from baseline of at least 15 bpm, lasting at least 2 minutes but less than 10 minutes. A deceleration of at least 10 minutes is a ...
Nuchal cord, when the umbilical cord is (tightly) around the neck of the fetus [2]; Entanglement of the cord [2]; Knot in the cord [2]; Cord prolapse, where the umbilical cord exits the birth canal before the baby, which can cause cord compression.
A generalization of the Viterbi algorithm, termed the max-sum algorithm (or max-product algorithm) can be used to find the most likely assignment of all or some subset of latent variables in a large number of graphical models, e.g. Bayesian networks, Markov random fields and conditional random fields.
Fourier–Motzkin elimination, also known as the FME method, is a mathematical algorithm for eliminating variables from a system of linear inequalities. It can output real solutions. The algorithm is named after Joseph Fourier [ 1 ] who proposed the method in 1826 and Theodore Motzkin who re-discovered it in 1936.