<p align="center">
<img src="assets/logo.png" alt="Alt text"/>
</p>
# Scikit-JAX: Classical Machine Learning on the GPU
Welcome to **Scikit-JAX**, a machine learning library designed to leverage the power of GPUs through JAX for efficient and scalable classical machine learning algorithms. Our library provides implementations for a variety of classical machine learning techniques, optimized for performance and ease of use.
## Features
- **Linear Regression**: Implemented with options for different weight initialization methods and dropout regularization.
- **KMeans**: Clustering algorithm to group data points into clusters.
- **Principal Component Analysis (PCA)**: Dimensionality reduction technique to simplify data while preserving essential features.
- **Multinomial Naive Bayes**: Classifier suitable for discrete data, such as text classification tasks.
- **Gaussian Naive Bayes**: Classifier for continuous data with a normal distribution assumption.
## Installation
To install Scikit-JAX, you can use pip. The package is available on PyPI:
```python
pip install scikit-jax
```
## Usage
Here is a quick guide on how to use the key components of Scikit-JAX.
### Linear Regression
```py
from skjax.linear_model import LinearRegression
# Initialize the model
model = LinearRegression(weights_init='xavier', epochs=100, learning_rate=0.01)
# Fit the model
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Plot losses
model.plot_losses()
```
### K-Means
```python
from skjax.clustering import KMeans
# Initialize the model
kmeans = KMeans(num_clusters=3)
# Fit the model
kmeans.fit(X_train)
```
### Gaussian Naive Bayes
```python
from skjax.naive_bayes import GaussianNaiveBayes
# Initialize the model
nb = GaussianNaiveBayes()
# Fit the model
nb.fit(X_train, y_train)
# Make predictions
predictions = nb.predict(X_test)
```
### License
Scikit-JAX is licensed under the [MIT License](LICENSE.txt).
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"description": "<p align=\"center\">\n <img src=\"assets/logo.png\" alt=\"Alt text\"/>\n</p>\n\n# Scikit-JAX: Classical Machine Learning on the GPU\n\nWelcome to **Scikit-JAX**, a machine learning library designed to leverage the power of GPUs through JAX for efficient and scalable classical machine learning algorithms. Our library provides implementations for a variety of classical machine learning techniques, optimized for performance and ease of use.\n\n## Features\n\n- **Linear Regression**: Implemented with options for different weight initialization methods and dropout regularization.\n- **KMeans**: Clustering algorithm to group data points into clusters.\n- **Principal Component Analysis (PCA)**: Dimensionality reduction technique to simplify data while preserving essential features.\n- **Multinomial Naive Bayes**: Classifier suitable for discrete data, such as text classification tasks.\n- **Gaussian Naive Bayes**: Classifier for continuous data with a normal distribution assumption.\n\n## Installation\n\nTo install Scikit-JAX, you can use pip. The package is available on PyPI:\n\n```python\npip install scikit-jax\n```\n\n## Usage\n\nHere is a quick guide on how to use the key components of Scikit-JAX.\n\n### Linear Regression\n```py\nfrom skjax.linear_model import LinearRegression\n\n# Initialize the model\nmodel = LinearRegression(weights_init='xavier', epochs=100, learning_rate=0.01)\n\n# Fit the model\nmodel.fit(X_train, y_train)\n\n# Make predictions\npredictions = model.predict(X_test)\n\n# Plot losses\nmodel.plot_losses()\n```\n\n### K-Means\n```python\nfrom skjax.clustering import KMeans\n\n# Initialize the model\nkmeans = KMeans(num_clusters=3)\n\n# Fit the model\nkmeans.fit(X_train)\n```\n\n### Gaussian Naive Bayes\n```python\nfrom skjax.naive_bayes import GaussianNaiveBayes\n\n# Initialize the model\nnb = GaussianNaiveBayes()\n\n# Fit the model\nnb.fit(X_train, y_train)\n\n# Make predictions\npredictions = nb.predict(X_test)\n```\n\n### License\n\nScikit-JAX is licensed under the [MIT License](LICENSE.txt).\n",
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