Search results
Results from the WOW.Com Content Network
Structural synthesis of programs (SSP) is a special form of (automatic) program synthesis that is based on propositional calculus.More precisely, it uses intuitionistic logic for describing the structure of a program in such a detail that the program can be automatically composed from pieces like subroutines or even computer commands.
The DSSP algorithm is the standard method for assigning secondary structure to the amino acids of a protein, given the atomic-resolution coordinates of the protein. The abbreviation is only mentioned once in the 1983 paper describing this algorithm, [2] where it is the name of the Pascal program that implements the algorithm Define Secondary Structure of Proteins.
SSP can also be regarded as an optimization problem: find a subset whose sum is at most T, and subject to that, as close as possible to T. It is NP-hard, but there are several algorithms that can solve it reasonably quickly in practice. SSP is a special case of the knapsack problem and of the multiple subset sum problem.
A Byte of Python: Author: Swaroop C H: Software used: DocBook XSL Stylesheets with Apache FOP: Conversion program: Apache FOP Version 1.1: Encrypted: no: Page size: 595.275 x 841.889 pts (A4) Version of PDF format: 1.4
python-sgp4 A Python Implementation of the sgp4 model with automatic downloading of TLE Elements from NORAD database. PHP5 based on Gpredict; Java: SDP4 and predict4java; C++, FORTRAN, Pascal, and MATLAB. go-satellite GoLang implementation of SGP4 model and helper utilities.
It is beyond the scope of this Wikipedia Page to explain the intricacies of operating HEC-RAS. For those interested in learning more, the HEC-RAS user’s manual is an excellent learning tool and the program is free to the public. The first two figures below are the upstream and downstream water surface profiles modeled by HEC-RAS.
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 ...
The method is useful for calculating the local minimum of a continuous but complex function, especially one without an underlying mathematical definition, because it is not necessary to take derivatives. The basic algorithm is simple; the complexity is in the linear searches along the search vectors, which can be achieved via Brent's method.