Showing 6 open source projects for "number prediction algorithm"

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  • 1
    UMAP.jl

    UMAP.jl

    Uniform Manifold Approximation and Projection (UMAP) implementation

    A pure Julia implementation of the Uniform Manifold Approximation and Projection dimension reduction algorithm. The umap function takes two arguments, X (a column-major matrix of shape (n_features, n_samples)), n_components (the number of dimensions in the output embedding), and various keyword arguments.
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  • 2
    Roots.jl

    Roots.jl

    Root finding functions for Julia

    ...For functions where a bracketing interval is known (one where f(a) and f(b) have alternate signs), a bracketing method, like Bisection, can be specified. The default is Bisection, for most floating point number types, employed in a manner exploiting floating point storage conventions. For other number types (e.g. BigFloat), an algorithm of Alefeld, Potra, and Shi is used by default. These default methods are guaranteed to converge. Other bracketing methods are available.
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  • 3
    Go Recipes

    Go Recipes

    Collection of handy tools for Go projects

    ...This helps to identify code areas with high and low coverage. Useful when you have a large project with lots of files and packages. This 2D image-hash of your project should be more representative than a single number. For each module, the node representing the greatest version (i.e., the version chosen by Go's minimal version selection algorithm) is colored green. Other nodes, which aren't in the final build list, are colored grey — by the official Go team. Use to find unexpected dependencies or visualize the project. Works best for a small number of packages, for large projects use grep to narrow down subgraph. ...
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  • 4
    Bayesian Optimization

    Bayesian Optimization

    Python implementation of global optimization with gaussian processes

    This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic...
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    Smart Business Texting that Generates Pipeline

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  • 5
    Cubature.jl

    Cubature.jl

    One- and multi-dimensional adaptive integration routines for Julia

    This module provides one- and multi-dimensional adaptive integration routines for the Julia language, including support for vector-valued integrands and facilitation of parallel evaluation of integrands, based on the Cubature Package by Steven G. Johnson. Adaptive integration works by evaluating the integrand at more and more points until the integrand converges to a specified tolerance (with the error estimated by comparing integral estimates with different numbers of points). The Cubature...
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  • 6
    Visualization of Protein-Ligand Graphs

    Visualization of Protein-Ligand Graphs

    Compute protein graphs. Moved to https://github.com/MolBIFFM/PTGLtools

    NOTE: Project moved to https://github.com/MolBIFFM/PTGLtools. The Visualization of Protein-Ligand Graphs (VPLG) software package computes and visualizes protein graphs. It works on the super-secondary structure level and uses the atom coordinates from PDB files and the SSE assignments of the DSSP algorithm. VPLG is command line software. If you do not like typing commands, try our PTGL web server: http://ptgl.uni-frankfurt.de/
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