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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.

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.

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'.

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.

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.

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.

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

Paraphrasing the website: Gato - the Graph Animation Toolbox - is software [toolkit] which visualizes algorithms on graphs. - Graphs are mathematical objects consisting of vertices, and edges connecting pairs of vertices. - Algorithms might find a shortest path - the fastest route - or a minimal spanning tree or solve one of other interesting problems on graphs: maximal-flow, weighted and non-weighted matching and min-cost flow. - Visualisation means linking cause - the statements of an algorithm - immediately to an effect - changes to the graph the algorithm has as its input - by terms of blinking, changing colors and other visual effects.

The fundamental package needed for scientific computing with Python is called NumPy. This package contains: * a powerful N-dimensional array object * sophisticated (broadcasting) functions * basic linear algebra functions * basic Fourier transforms * sophisticated random number capabilities * tools for integrating Fortran code. NumPy derives from the old Numeric code base and can be used as a replacement for Numeric. It also adds the features introduced by numarray and can also be used to replace numarray. Note: Development for Numeric has ceased, and users should transisition to NumPy as quickly as possible.

This is a Ruby library for mathematical (algebraic) computations. Our purpose is to express mathematical objects naturally in Ruby. Though it does not operate fast, we can see the algorithm of the mathematical processing not in a black box but in scripts. Things Ruby/Algebra offers include the following: - One-variate polynomial o Fundamental operations (addition, multiplication, quotient/remainder, ...) o Factorization - Multi-variate polynomial o Fundamental operations (addition, multiplication, ...) o Creating Groebner-basis, quotient/remainder by Groebner-basis. - Algebraic systems o Creating quotient fields o Creating residue class fields o Operating matrices