7 个仓库
Merging multiple arrays through concatenation, stacking, or coordinate grid creation.
Distinct from Array Manipulations: Focuses on combining multiple arrays into one, distinct from modifying the contents of a single array.
Explore 7 awesome GitHub repositories matching scientific & mathematical computing · Array Combinations. Refine with filters or upvote what's useful.
This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque
Implements merging of multiple tensors through concatenation, stacking, and other array combination methods.
This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p
Provides capabilities to join multiple arrays along specified axes or split arrays into sub-arrays.
LearnPython is a programming tutorial consisting of a collection of practical code examples used to demonstrate Python language features and programming patterns. It serves as a comprehensive learning resource that implements core language concepts through functional code. The project provides specialized guides and samples covering several key domains. These include asynchronous network programming with event loops and coroutines, data visualization using numerical datasets for 2D and 3D plots, and web scraping for fetching content and automating login flows. It also features instructions on
Shows how to combine arrays through concatenation and stacking to manage data layouts.
collect.js is a dependency-free JavaScript library that provides a fluent, chainable interface for manipulating arrays and objects. It mirrors the Laravel Collection API, offering a consistent set of methods for data transformation across JavaScript and Laravel backend environments. The library stores collection data as plain arrays internally and supports fluent method chaining, where each method returns a new collection instance. The library distinguishes itself by closely replicating the Laravel Collection API in JavaScript, mapping each PHP method to an equivalent JavaScript implementatio
Merges a collection of arrays into a single flat collection.
ndarray 是一个 Rust 的多维数组库,用作线性代数框架和科学计算工具。它提供了创建和操作 n 维数组的核心基础设施,既充当并行数组处理器,也充当数值数据分析工具包。 该库的独特之处在于提供高效的切片和内存视图,允许在不复制的情况下共享数据。它利用优化的后端数学库进行高速矩阵乘法,并将繁重的数学迭代分布到多个 CPU 线程上以加速处理。 该项目涵盖了广泛的数学运算,包括逐元素算术、基于轴的数据聚合和点积计算。它还包括用于数组操作的全面实用程序,如重塑、展平、堆叠和坐标网格生成,以及对随机数组生成和序列化的支持。
Joins arrays together by stacking them along new axes or concatenating them along existing ones.
more-itertools 是 Python itertools 模块的扩展库。它作为一个用于操作可迭代对象的工具包,提供了广泛的数据转换、组合生成和迭代器状态管理例程。 该库以高级状态管理和复杂的序列生成为特色。它提供了查看未来元素、在序列内搜索,以及从可能包含重复元素的集合中生成唯一排列、组合和集合划分的功能。 其更广泛的功能涵盖了数据处理任务,如递归展平、分组、填充和数据流重塑。它还包括用于流合并、局部邻域分析的窗口化以及线程安全的迭代同步的工具。 该项目还提供了用于数值序列处理的专门例程,包括矩阵乘法、离散线性卷积和傅里叶变换。
Generates partial products to explore combinations across multiple collections without producing every possible pair.
xtensor is a C++ multidimensional array library for numerical computing that provides N-dimensional containers with an interface mirroring the NumPy API. It utilizes a lazy evaluation expression engine to defer numerical computations until assignment, which minimizes memory allocations and intermediate copies. The library features a foreign memory array adaptor that allows it to wrap external buffers, such as NumPy arrays, to perform numerical operations in-place without duplicating data. It further optimizes performance through lazy broadcasting and a system that manages the lifetime of temp
Provides methods to merge multiple arrays via concatenation, stacking, or the creation of coordinate grids.