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math/hoc-9.2 (Score: 9.841064E-5)
High Order Calculator
Hoc, the High Order Calculator, is an interpreted language for floating-point calculations. Its most basic use is as a powerful and convenient calculator, interactively evaluating expressions such as 1+2*sin(0.7). But hoc is no ordinary calculator: It also lets you assign values to variables, define your own functions, and use loops, conditionals, and everything else you'd expect in a programming language. Hoc was developed by Brian Kernighan and Rob Pike, and introduced in their 1984 book The Unix Programming Environment. This version has been extended and improved by Nadav Y. Har'El.
math/octave-forge-base-1.4 (Score: 9.841064E-5)
Octave-forge baseport for all packages
The octave-forge package is the result of The GNU Octave Repositry project, which is intended to be a central location for custom scripts, functions and extensions for GNU Octave. contains the source for all the functions plus build and install scripts. This baseport provides the basic directory structure, and installs a script "load-octave-pkg", that synchronizes the FreeBSD ports structure to the octave packaging system. Another purpose of the script "load-octave-pkg" is to attempt to correct any errors created by the octave packaging system.
math/octave-forge-doctest-0.4.1 (Score: 9.841064E-5)
Octave-forge package doctest
The octave-forge package is the result of The GNU Octave Repositry project, which is intended to be a central location for custom scripts, functions and extensions for GNU Octave. contains the source for all the functions plus build and install scripts. This is doctest. The Octave-Forge Doctest package finds specially-formatted blocks of example code within documentation files. It then executes the code and confirms the output is correct. This can be useful as part of a testing framework or simply to ensure that documentation stays up-to-date during software development.
math/Chart-Math-Axis-1.06 (Score: 9.841064E-5)
Implements an algorithm to find good values for chart axis
Chart::Math::Axis implements in a generic way an algorithm for finding a set of ideal values for an axis. That is, for any given set of data, what should the top and bottom of the axis scale be, and what should the interval between the ticks be. The terms top and bottom are used throughout this module, as it's primary use is for determining the Y axis. For calculating the X axis, you should think of 'top' as 'right', and 'bottom' as 'left'.
math/Math-Random-ISAAC-XS-1.004 (Score: 9.841064E-5)
C implementation of the ISAAC PRNG Algorithm
As with other Pseudo-Random Number Generator (PRNG) algorithms like the Mersenne Twister (see Math::Random::MT), this algorithm is designed to take some seed information and produce seemingly random results as output. However, ISAAC (Indirection, Shift, Accumulate, Add, and Count) has different goals than these commonly used algorithms. In particular, it's really fast - on average, it requires only 18.75 machine cycles to generate a 32-bit value. This makes it suitable for applications where a significant amount of random data needs to be produced quickly, such solving using the Monte Carlo method or for games.
math/Math-Random-ISAAC-1.004 (Score: 9.841064E-5)
Perl interface to the ISAAC PRNG Algorithm
As with other Pseudo-Random Number Generator (PRNG) algorithms like the Mersenne Twister (see Math::Random::MT), this algorithm is designed to take some seed information and produce seemingly random results as output. However, ISAAC (Indirection, Shift, Accumulate, Add, and Count) has different goals than these commonly used algorithms. In particular, it's really fast - on average, it requires only 18.75 machine cycles to generate a 32-bit value. This makes it suitable for applications where a significant amount of random data needs to be produced quickly, such solving using the Monte Carlo method or for games.
math/carve-1.4.0 (Score: 9.841064E-5)
Fast, robust constructive solid geometry library
Carve is a C++ library designed to perform boolean operations between two arbitrary polygonal meshes. The standard union and intersection operations are supported, as are symmetric and asymmetric difference. It is also possible to implement custom operations using Carve, allowing results to be formed from any combination of inputs. Carve supports a variety of inputs, including both closed and open surfaces, faces with arbitrary edge counts and datasets with multiple disjoint, embedded or touching surfaces. Carve can also interpolate arbitrary values across faces, meaning that CSG operations need not discard colour, texture coordinates or other data.
math/Statistics-Contingency-0.09 (Score: 9.841064E-5)
Calculate precision, recall, F1, accuracy, etc
The "Statistics::Contingency" class helps you calculate several useful statistical measures based on 2x2 "contingency tables". I use these measures to help judge the results of automatic text categorization experiments, but they are useful in other situations as well. The general usage flow is to tally a whole bunch of results in the "Statistics::Contingency" object, then query that object to obtain the measures you are interested in. When all results have been collected, you can get a report on accuracy, precision, recall, F1, and so on, with both macro-averaging and micro-averaging over categories.
math/ParMetis-4.0 (Score: 9.841064E-5)
Package for parallel (mpi) unstructured graph partitioning
ParMETIS is an MPI-based parallel library that implements a variety of algorithms for partitioning unstructured graphs and for computing fill-reducing orderings of sparse matrices. ParMETIS extends the functionality provided by METIS and includes routines that are especially suited for parallel AMR computations and large scale numerical simulations. The algorithms implemented in ParMETIS are based on the parallel multilevel k-way graph-partitioning algorithms described in [KK95d], [KK96], [KK97], and the adaptive repartitioning algorithms described in [SKK97a], [SKK97b], [SK+98], and [SKK98].
math/apgl-0.8.1 (Score: 9.841064E-5)
Fast python graph library with some machine learning features
Another Python Graph Library is a simple, fast and easy to use graph library with some machine learning features. The main features are as follows: * Directed, undirected and multigraphs designed under a hierarchical class structure * Sparse and Dense graph structures using numpy and scipy for fast linear algebra computations * Many operations on graphs such as subgraphs, search, Floyd-Warshall, Dijkstras algorithm * Erdos-Renyi, Small-World and Albert-Barabasi random graphs * Write to Pajek, and simple CSV files * Some machine learning features - data preprocessing, kernels, PCA, KCCA, wrappers for LibSVM, and some mlpy learning algorithms * Unit tested using the Python unittest framework