NumCpp
casadi
| NumCpp | casadi | |
|---|---|---|
| 4 | 4 | |
| 3,641 | 1,815 | |
| 1.6% | 3.5% | |
| 2.5 | 9.8 | |
| 9 days ago | 3 days ago | |
| C++ | C++ | |
| MIT License | GNU Lesser General Public License v3.0 only |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NumCpp
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Machine Learning using C++ vs Python
Yeah, as someone who writes C++ daily for their ML related job, I concur that the cost of executing a convolutions dwarves the overhead of calling from Python. So as much as I like C++ over Python (because static compilation to find little typos or type mismatches ahead of time is much nicer than exploding 5 minutes later into my batched vision recognition problem 😠), generally for small problems, Python is a nice quick and dirty approach. I do have my eye though on this little C++ numpy clone.
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Can i use numpy with c or c++ ?
Despite being written in C itself, the primary external API is for Python, and though it is possible to call via C, it's quite ungainly (several ref-counted Py_* calls and structs). It's probably easier to just consume a library that targets C++ directly like xtensor (https://xtensor.readthedocs.io/en/latest/numpy.html) or NumCpp (https://github.com/dpilger26/NumCpp).
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trouble with linspace functions
I am trying to feed 2 different 3 column 1 row arrays into a linspace function using the NumCPP package, but i'm getting errors such as:
- Read python pickle files in C++
casadi
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pyomo VS casadi - a user suggested alternative
2 projects | 5 Sep 2023
Interface for several solvers and integrators.
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(Direct) Collocation in (Time) Optimal Control
Howdy! Collocation methods can be... tricky. For NMPC control of vehicles, success has been had using direct multiple shooting. Also easier to implement and more intuitive. In fact, this example from the GH is pretty instructive: https://github.com/casadi/casadi/blob/master/docs/examples/python/race_car.py
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Are there any optimization libraries/packages that use automatic differentiation?
JuMP.jl (Julia) or casADi (Python) are good choices.
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Should I switch over completely to Julia from Python for numerical analysis/computing?
Python is not mature in this area. If you ask Google what Simulink for Python is, you get responses that point to dead libraries that were never feature complete and slow. The absolute closest is CASADI which is nice for some things but doesn't even have a true causal modeling interface and is mostly abandoned by the developers (they put a patch in every now and then, but just look at the commit graph), and it's slow compared to the Julia tools, so much so that PyBAMM is interfacing with ModelingToolkit.jl in Julia for a performance boost. Python is not the place to be for causal/acausal modeling or controls. Anyone who is saying "Python is mature" here is saying it in the abstract and not in the context of your actual question. Yes, Python has web development frameworks. No it does not have good libraries for tons of areas (control, acausal modeling, pharmacometrics, etc.).
What are some alternatives?
eigen
ceres-solver - A large scale non-linear optimization library
RxCpp - Reactive Extensions for C++
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.
jsbsim - An open source flight dynamics & control software library
examples - Example data structures and algorithms
symforce - Fast symbolic computation, code generation, and nonlinear optimization for robotics
Tiger - C++ Matrix -- High performance and accurate (e.g. edge cases) matrix math library with expression template arithmetic operators
wyvern - Automatic conversion of call by value into call by need in the LLVM IR.
tuninglib - A C++ Class and Template Library for Performance Critical Applications
Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.