scikit-jax 0.0.1

Classical machine learning algorithms on the GPU.

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:

pip install scikit-jax

Usage

Here is a quick guide on how to use the key components of Scikit-JAX.

Linear Regression

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

from skjax.clustering import KMeans

# Initialize the model
kmeans = KMeans(num_clusters=3)

# Fit the model
kmeans.fit(X_train)

# Predict clusters
clusters = kmeans.predict(X_test)

Gaussian Naive Bayes

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.