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项目作者： saniikakulkarni

项目描述 ：
Implementing Gaussian Mixture Model from scratch using python class and Expectation Maximization algorithm. It is a clustering algorithm having certain advantages over kmeans algorithm.
高级语言： Jupyter Notebook
项目地址: git://github.com/saniikakulkarni/Gaussian-Mixture-Model-from-scratch.git


Gaussian-Mixture-Model-from-scratch

Type of algorithm: Clustering algorithm

Dataset used: Iris dataset imported from sklearn

Output of final cluster



Requirements:

Jupyter notebook or Google Colab

Libraries:

Pandas: https://pandas.pydata.org/docs/getting_started/install.html

Numpy: https://numpy.org/install/

Matplotlib: https://matplotlib.org/stable/users/installing.html

sklearn: https://scikit-learn.org/stable/install.html

scipy: https://pypi.org/project/scipy/

Steps involved:

For Google Colab:

  1. Open google colab on any browser.
  2. Upload the file “Gaussian_Mixture_Model_from_scratch.ipynb” in the Google Colab.
  3. Run all the cells in the notebook and view the output.
  4. See the plots to visualize the final results.

For Jupyter Notebook:

  1. Run the jupyter notebook.
  2. Select the file “Gaussian_Mixture_Model_from_scratch.ipynb” from the location where you saved the file.
  3. Install the required packages mentioned above.
  4. Run all the cells in the notebook and view the output.
  5. See the plots to visualize the final results.

References:

http://www.oranlooney.com/post/ml-from-scratch-part-5-gmm/