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README

Algo-ScriptML

script.png

License: MIT made-with-python Open Source Love svg1 PRs Welcome contributions welcome Maintenance

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Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

The goal of this project is not to create algorithms that are as streamlined and computationally efficient as possible, but rather to present their inner workings in a clear and usable manner.

Algorithms:

⚙️ Contribution Guidelines

Please go through the whole Contributing Guidelines here.

  • Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
  • You can only work on issues that have been assigned to you.
  • If you want to contribute to an existing algorithm, we prefer that you create an issue before making a PR and link your PR to that issue.
  • If you have modified/added code work, make sure the code compiles before submitting.
  • Strictly use snake_case (underscore_separated) in your file_name and push it incorrect folder.
  • Do not update the README.md.

📂 Where to upload the files

  • Your files should be uploaded inside the *code folder into the corresponding language folder (For instance, if you wrote code for a K-Means Implementation, it goes inside the K-Means folder).
  • Under no circumstances create new folders within the language folders to upload your code unless specifically told to do so.
  • Edit the corresponding README.md file to add the link to your code in the corresponding section (GitHub Markdown Guide)
The value of a strong contribution stays beyond everything and gives you satisfaction 👍🌟

📖 Code Of Conduct

You can find our Code of Conduct here.

📝 License

This project follows the MIT License.

😇 Maintainers

Aditya Kumar Gupta 💻 🖋 Ashwani Rathee 💻 Yukti Sachdeva 💻

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Core symbols most depended-on inside this repo

predict
called by 4
Bayesian Regression/bayessian_regression.py
hypothetical_function
called by 4
Logistic Regression/Logistic_Regression_base.py
learn
called by 3
Bayesian Regression/bayessian_regression.py
prettify_ax
called by 3
Bayesian Regression/bayessian_regression.py
fit
called by 3
K-Means/kmeans.py
entropy
called by 3
Random Forest/randomForest.py
gini
called by 3
Decision Tree/decision_tree_implementation.py
pairwise_distances
called by 2
Spectral Clustering/spectral_clustering.py

Shape

Method 159
Class 31
Function 24

Languages

Python100%

Modules by API surface

Decision Tree/decision_tree_implementation.py23 symbols
Random Forest/randomForest.py20 symbols
Preprocessing/min_max_scaler.py13 symbols
Hidden Markov Model/hmm.py12 symbols
Lowess Regression/lowessregression.py10 symbols
Linear Regression/Linear_Regression.py10 symbols
Preprocessing/standard_scaler.py9 symbols
Logistic Regression/Logistic_Regression_base.py9 symbols
Spectral Clustering/spectral_clustering.py8 symbols
Naive Bayes/naive_bayes.py8 symbols
Gaussian Mixture Model/GaussianMixtureModel.py8 symbols
Genetic Algorithm/genetic_algorithm.py7 symbols

For agents

$ claude mcp add Algo-ScriptML \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact