Showing 9 open source projects for "construct"

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  • 1
    AbstractGPs.jl

    AbstractGPs.jl

    Abstract types and methods for Gaussian Processes

    AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs. You may want to go through the main API design documentation.
    Downloads: 3 This Week
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  • 2
    PolyChaos.jl

    PolyChaos.jl

    Julia package to construct orthogonal polynomials

    PolyChaos is a collection of numerical routines for orthogonal polynomials written in the Julia programming language. Starting from some non-negative weight (aka an absolutely continuous nonnegative measure).
    Downloads: 2 This Week
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  • 3
    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.
    Downloads: 3 This Week
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  • 4
    ComponentArrays.jl

    ComponentArrays.jl

    Arrays with arbitrarily nested named components

    The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main...
    Downloads: 6 This Week
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  • 5
    DynamicQuantities.jl

    DynamicQuantities.jl

    Lightweight + fast physical quantities in Julia

    DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods.
    Downloads: 3 This Week
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  • 6
    FunSQL.jl

    FunSQL.jl

    Julia library for compositional construction of SQL queries

    ...FunSQL allows you to build queries incrementally from small independent fragments. This approach is particularly useful for building applications that programmatically construct SQL queries.
    Downloads: 3 This Week
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  • 7
    RuntimeGeneratedFunctions.jl

    RuntimeGeneratedFunctions.jl

    Functions generated at runtime without world-age issues or overhead

    ...This is normally transparent to the user, but if the RuntimeGeneratedFunction is evaluated during module precompilation, the cache module must be explicitly set to the module currently being precompiled. This is relevant for helper functions in some modules that construct a RuntimeGeneratedFunction on behalf of the user.
    Downloads: 2 This Week
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  • 8
    Rotations.jl

    Rotations.jl

    Julia implementations for different rotation parameterizations

    ...While the RotMatrix type is a dense representation of a 3×3 matrix, we also have sparse (or computed, rather) representations such as quaternions, angle-axis parameterizations, and Euler angles. All rotation types support one(R) to construct the identity rotation for the desired parameterization.
    Downloads: 6 This Week
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  • 9
    Strategems

    Strategems

    Quantitative systematic trading strategy development and backtesting

    ...Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward syntax would seem to make it a natural fit as a language for systematic strategy research and development. While this package remains early in development, with time the hope is to be able to rapidly implement a trading idea, construct a historical backtest, analyze its results, optimize over a given parameter set, and visualize all of this with great detail.
    Downloads: 0 This Week
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