dpilger26/NumCpp

Features

• Multi-Dimensional Arrays - Implements a comprehensive framework for creating and manipulating multi-dimensional arrays for scientific computing.
• NumPy-Compatible Implementations - Replicates the multi-dimensional array operations and numerical functions of the Python NumPy ecosystem in C++.
• Strided - Utilizes strided memory mapping to allow efficient array reshaping and slicing without copying data.
• Generic Type Safety - Leverages C++ templates to provide generic, type-safe multi-dimensional arrays for any numerical data type.
• Array Broadcasting - Provides array broadcasting to virtually expand smaller arrays for element-wise arithmetic operations.
• Array Geometry Management - Provides logic for controlling array geometry through reshaping, stride modifications, and size adjustments.
• Array Manipulations - Implements high-performance functions for modifying array contents, including efficient slicing, reshaping, and broadcasting.
• Array Slicing - Implements powerful array slicing to extract sub-sections based on boundaries or predicates.
• Dimensional Reductions - Implements dimensional reduction operations to aggregate multi-dimensional data along specific axes.
• Element-wise Array Operations - Implements high-performance element-wise operations that apply mathematical functions across every array member.
• Linear Algebra Routines - Provides linear algebra routines to solve algebraic equations and compute matrix norms or determinants.
• Matrix Numerical Computations - Provides algorithms for the representation and manipulation of multi-dimensional arrays using linear algebra.
• Array Initialization - Offers programmatic creation of arrays using linear spacing, identity matrices, and fixed values.
• Numerical Array Operations - Performs complex mathematical calculations, indexing, and reshaping on multi-dimensional arrays for scientific computing.
• Numerical Libraries - Offers optimized algorithms for matrix operations and mathematical calculations on large datasets.
• Contiguous Memory Buffers - Uses contiguous memory buffers to store multi-dimensional data, ensuring high cache locality and performance.
• Array Reorientation - Enables data reorientation through diagonal extraction, triangular matrix creation, and axis flipping.
• Array Binary Serialization - Enables fast loading and saving of arrays by writing raw memory contents and shape metadata directly to disk.
• Data Import and Export - Enables the import and export of numerical arrays using binary dumps and delimited text formats.
• Logical Array Operations - Provides element-wise logical operations to evaluate conditions and filter multi-dimensional data.
• Array Concatenations - Ships functions to join multiple arrays via stacking or appending into a single structure.
• Array Reductions - Accumulates values along specified axes to compute sums, products, and means for data aggregation.
• Random Distribution Generation - Produces arrays populated with random values from normal and integer distributions for simulation and testing.
• Array Statistical Aggregations - Calculates summary statistics like minimums, maximums, and unique values along specified axes.
• Scientific Computing - Provides a computational framework for performing complex mathematical modeling and large-scale scientific data analysis.
• Statistical Analysis Libraries - Provides comprehensive toolsets for calculating descriptive statistics and correlations across multi-dimensional datasets.
• Scalar Mathematical Functions - Implements absolute value, trigonometric, logarithmic, and power functions for numeric types across arrays.
• Array Equality Comparison - Provides utilities for element-wise equality checks and identifying divergent indices between numerical datasets.
• Mathematical Libraries - C++ implementation of NumPy.
• Mathematics - Template-based C++ implementation of NumPy.
• Mathematics and Statistics - C++ implementation of NumPy-style functionality.

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