The Planar Color Classifier is a Python-based tool that enables efficient classification of data points within a two-dimensional dataset based on color information. This README provides essential information about how to use and understand the classifier.
To use the Planar Color Classifier, you will need the following Python libraries installed:
- Matplotlib
- NumPy
- SciKit-Learn
- h5py (for handling HDF5 datasets)
You can install these libraries using pip:
pip install matplotlib numpy scikit-learn h5py
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Dataset Preparation: Ensure that your dataset is in HDF5 format. You can create or convert datasets to this format using the h5py library.
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Classifier Implementation: The classifier is implemented in a Python script. Import it into your project and instantiate an instance of the classifier.
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Training: Use your prepared dataset to train the classifier. The training process will involve extracting color features from the dataset and labeling data points according to your classification task.
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Classification: After training, you can use the classifier to predict the category of new data points based on their color features.
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Visualization: The classifier includes visualization tools using Matplotlib to help you visualize the classification results and assess the model's performance.
Contributions and improvements to the Planar Color Classifier are welcome. Please fork this repository, make your changes, and submit a pull request.
We would like to thank the open-source community for the libraries and tools that made this project possible.