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Python API Modules for Machine Learning and Arduino
Published in Amartya Mukherjee, Nilanjan Dey, Smart Computing with Open Source Platforms, 2019
Amartya Mukherjee, Nilanjan Dey
Python library has a good range of API support for artificial intelligence and hardware interfacing. The machine learning application is one of the most popular packages that is widely implemented using python scikit learning tools. In this chapter, our main objective is to emphasize different useful packages that are related to machine learning, Arduino, Data Science, and other technologies. We are here trying to give the insights of those packages with some useful examples. One of the helpful entities in this context is PyPI, the Python Package Index, which is a software repository for Python programming.
Tutorial: The LuxPy Python Toolbox for Lighting and Color Science
Published in LEUKOS, 2020
Note that Anaconda from Continuum Analytics is a free and open-source distribution of Python for scientific computing that eases package management and deployment (it contains a large set packages specifically aimed at scientific computation). Enthought Canopy is another option. Once installed, activate the virtual environment (this step must be done each time you want to use any of the packages installed in the environment) by typing:Install pip—installer for packages listed on the Python Package Index (pypi.python.org)—to the py36 conda virtual environment to ensure that any packages installed with pip will be installed to the py36 environment and not globally:The LuxPy package can now be installed by typing:
Optimization of economic production scheduling under sales and operations constraints in consumer goods industries
Published in International Journal of Management Science and Engineering Management, 2023
Pierre-André M. Fruytier, Michael C. Georgiadis
The previous mathematical model is a NLIP formulation, which can potentially be solved using exact NLIP algorithms. Such algorithms are typically characterized by high computational costs even for medium-sized problems. Moreover, they cannot always guarantee a global optimal solution. To overcome this complexity, an evolutionary algorithm is used for the optimization. This algorithm is implemented by the first author using the following open-source Python libraries (Python Package Index - PyPI., n.d..), i.e. Pandas, NumPy, and geneticalgorithm, successfully used for earlier consulting work. The decision support system relies on the standard code of geneticalgorithm so schedulers can later benefit from the most recent releases of the library.
Linear Models with Python
Published in Technometrics, 2021
Finally, Appendix A describes how to learn Python. It is possible to install Python from Anaconda at www.anaconda.com, and to use Jupyter notebooks from www.jupyter.org. Several packages come preinstalled with the Anaconda release, and they are used for the data and some functions in the book: numpy, scipy, pandas, statsmodels, matplotlib, seaborn, scikit-learn, and patsy, just to get something similar to the base R environment. The author recommends to install also his package far away from the Python Package index (PyPi) at the repository www.pypi.org. Readers should see https://julianfaraway.github.io/LMP/serves for information about changes or errors found in the text. Several additional author’s notes on Python are as follows: (i) Base R is quite functional without loading any packages. In Python, you will always need to load some packages even to do some basic computations. (ii) Python is very fussy about namespaces. You will have to prefix every loaded function. For example, you cannot write log(x)—you’ll need to write np.log(x) indicating that log comes from the numpy package. (ii) Python array indices start from zero, it is something the R user has to continually adjust to. (iv) matplotlib is the Python equivalent of the R base plotting functionality. (v) statsmodels provides the linear modeling functionality found in R but you will find some differences that will trip you up. In particular, no intercept term is included by default and the handling of saturated models is different. Of course, you can work around all these issues. (vi) Python uses pipes very commonly. It helps if you have already started using these in R via the %>% operator to get you into that frame of mind.