semvec/pypi-packages huggingface dataset:
scikit-jax 0.0.3.dev1
Classical machine learning algorithms on the GPU/TPU.
Requires Python: >=3.9
Keywords: jax classical machine learning

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  <img src="assets/logo.png" alt="Alt text"/>
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# 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).
facialmatch 1.0.1
face matching
Keywords: python, face, face matching, face detection, face recognition, opencv, opencv-python, numpy, scikit-learn


# facialmatch



**facialmatch** is a Python package that provides easy-to-use functions for comparing faces in images and extracting facial regions from a given image.



## Features



- 🧑‍🤝‍🧑 **Compare Faces:** Compare two images and check if they match with a given similarity threshold.

- 🖼️ **Extract Face:** Extract and return the facial region from an image.



## Installation



You can install the package via pip:



```bash

pip install facialmatch

```



## Usage



**match.py**

```bash

from facialmatch import compare_faces



# Compare two faces

result = compare_faces("path/to/image1.jpg", "path/to/image2.jpg")



# Custom similarity threshold

custom_result = compare_faces("path/to/image1.jpg", "path/to/image2.jpg", minimum_similarity=0.80)

```

**output**

```bash

{

  "similarity": 0.86,

  "match": "true"

}



```

##

**ExtractFace.py**

```bash

from facialmatch import get_face_from_image

import matplotlib.pyplot as plt



# Extract a face from an image

face_image = get_face_from_image("path/to/image")



# Display the extracted face using matplotlib

plt.imshow(face_image)

plt.axis('off')  # Turn off the axis

plt.show()

```