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👋 Hi, I’m Harshal Shende (@harshal0815), a final year B.Tech EEE undergrad at National Institute of Technology, Karnataka, India.
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👀 I’m interested in machine learning, AI and web development.
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🌱 I’m currently learning Deep Learning and Reinforcement Learning.
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📫 Follow me on Instagram, LinkedIn and Twitter.You can also reach out to me via email [email protected]
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😄 Pronouns: He/Him
Web Dev and Systems enthusiast. Here for learning new stuff.
- National Institute of Technology, Karnataka
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root-project/root
root-project/root PublicThe official repository for ROOT: analyzing, storing and visualizing big data, scientifically
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GSoC_22
GSoC_22 1# Overview
2Toolkit for Multivariate Analysis (TMVA) is a multi-purpose machine learning toolkit integrated into the ROOT scientific software framework. It comes with an automatically generated Python interface, which closely follows the C++ interface. The goal of this project is to enhance the Python interface to make it more “pythonic”, i.e. easier to use. This project aims to simplify complex workflows and enhancement of the python interface, greatly reducing the amount of code that has to be written, including pythonizations for TMVA GUI and Hist functions and converters for PyROOT NumPy arrays to convert RTensor from and to PyROOT NumPy arrays.
34To make it easier to use ROOT from Python, or to use a more pythonic syntax, PyROOT provides many pythonizations for ROOT classes. A pythonization is a piece of code that injects some new behavior in a ROOT class, e.g. to add new methods, to make the class iterable from Python, or override arithmetic operators. Pythonizations can be implemented in Python or C++ (via the Python/C API). Automatic binding generation mostly gets the job done, but unless a C++ library was designed with expressiveness and interactivity in mind, using it will feel stilted. Thus, it is beneficial to implement pythonizations. Some of these are already provided by default, e.g. for STL containers.
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