semvec/pypi-packages huggingface dataset:
starpoint 0.7.0
SDK for Starpoint DB
Requires Python: >=3.7

# Starpoint AI SDK

## Installation

`pip install starpoint`

## Quickstart

After you have a API key and a collection created on your starpoint account

```python
from starpoint.db import Client

client = Client(api_key="YOUR_API_KEY_HERE")

documents = [
  {
    "embeddings": [0.1, 0.2, 0.3, 0.4, 0.5],
    "metadata": {
      "label1": "0",
      "label2": "1",
    }
  },
]

// Instead of collection_name you can also use collection_id="COLLECTION_ID"
client.insert(documents=documents, collection_name="COLLECTION_NAME")

```

## Contributing

Make sure you have installed dev requirements

```
pip install -r dev-requirements.txt
```

Unit tests should be passing. You can run them via

```
pytest ./tests
```
scikit-neutrosophic 0.0.2
Neutrosophic Machine Learning Algorithms
Requires Python: >=3.6

# scikit-neutrosophic

A Python library for Neutrosophic Machine Learning Algorithms.

## Installation

You can install the library using pip:

```sh
pip install scikit-neutrosophic

```

## Usage

### Neutrosophic k-NN
```python
from scikit_neutrosophic.neutrosophic_knn import NeutrosophicKNN

# Example usage
import numpy as np

X_train = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])
y_train = np.array([0, 1, 0, 1])

knn = NeutrosophicKNN(k=3)
knn.fit(X_train, y_train)
print(knn.predict(np.array([[2, 3]])))
```
### Neutrosophic SVM
```python
from scikit_neutrosophic.neutrosophic_svm import NeutrosophicSVM

svm = NeutrosophicSVM()
svm.fit(X_train, y_train, neutrosophic_params=None)
print(svm.predict(np.array([[2, 3]])))
```

### Neutrosophic Linear Regression

```python
from scikit_neutrosophic.neutrosophic_linear_regression import NeutrosophicLinearRegression

reg = NeutrosophicLinearRegression()
reg.fit(X_train[:, 0], y_train, neutrosophic_params=None)
print(reg.predict(np.array([2, 3, 4])))

```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License.
prymes 0.0.1a1
Python package for prime-related things
Requires Python: >=3.12
Keywords: number-theory, primes

# PrYmes

![PyPI - Version](https://img.shields.io/pypi/v/prymes)
![PyPI - License](https://img.shields.io/pypi/l/prymes)

Python package for prime-related things.

## Install

### (Recommended) pip

To install PrYmes using pip, run the following command:
```
pip install prymes
```

### Source

To install PrYmes from source, clone PrYmes' Github repository using `git`:
```
git clone https://github.com/cyan-ice/prymes.git
```
Then, in the `prymes` repository you just cloned, execute the following command:
```
pip install dist/prymes-*-py3-none-any.whl
```
Where `*` is the latest version of PrYmes.

## Testing

To execute all tests, run
```
python tests/test.py
```
In the current directory.

You should see something like this:
```
Test #1: primality_test... Passed in 0.012s
Passed 1/1
```

## Documentation

The documentation is at https://prymes-docs.readthedocs.io/latest/.