semvec/pypi-packages huggingface dataset:
mikegrad 0.2.0
A minimal autograd framework
Requires Python: >=3.6

# mikegrad

A minimal autograd framework with some custom features.

## Features

- **Autograd engine**: Automatic differentiation for gradient computation
- **Backpropagation**: Implementation from scratch
- **Basic neural network components**: Build your own neural networks
- **Activation functions**:
  - Sigmoid
  - Tanh
  - Softmax
  - ReLU
  - LeakyReLU
- **Loss functions**:
  - CrossEntropyLoss
  - Binary Cross-Entropy Loss (BCELoss)
  - Mean Squared Error (MSELoss)
- **Optimizers**:
  - SGD
  - Adam
  - AdamW
- **Utility functions**:
  - `argmax`: Find the index of the maximum value in a tensor
  - `from_list`: Convert a Python list of numerical values into a list of `Value` class instances
  - `to_list`: Convert a list of `Value` class instances back into a Python list of numerical values
  - `from_numpy`: Convert a NumPy array into a list of `Value` class instances
  - `to_numpy`: Convert a list of `Value` class instances back into a NumPy array
  - `from_pandas`: Convert a Pandas DataFrame into a list of `Value` class instances
  - `one_hot_encode`: Create one-hot encoded vectors from categorical data
- **Example training**:
  - Example training on `California Housing` dataset for regression ([toy_classification.ipynb](examples/toy_classification.ipynb)).
  - Example training on `Breast Canser` dataset for classification ([toy_regression.ipynb](examples/toy_classification.ipynb)).
  - Examples of `Binary Classification`, `Multiclass Classification`, `Regression` for demonstrating training ([example.ipynb](examples/example.ipynb)).

### Note:

This project is inspired from Andrej Karpathy's ["Neural Networks: Zero to Hero - Micrograd"](https://www.youtube.com/watch?v=VMj-3S1tku0) video.
itemdb 1.3.0
Easy transactional database for Python dicts, backed by SQLite
Requires Python: >=3.7
Keywords: database, sqlite, no-sql

[![PyPI Version](https://img.shields.io/pypi/v/itemdb.svg)](https://pypi.python.org/pypi/itemdb/)
[![CI](https://github.com/almarklein/itemdb/workflows/CI/badge.svg)](https://github.com/almarklein/itemdb/actions)
[![Documentation Status](https://readthedocs.org/projects/itemdb/badge/?version=latest)](https://itemdb.readthedocs.io/en/latest/?badge=latest)

# itemdb


The itemdb library allows you to store and retrieve Python dicts in a
database on the local filesystem, in an easy, fast, and reliable way.

Based on the rock-solid and ACID compliant SQLite, but with easy and
explicit transactions using a ``with`` statement. It provides a simple
object-based API, with the flexibility to store (JSON-compatible) items
with arbitrary fields, and add indices when needed.

Originally developed as part of [TimaTagger](https://timetagger.app)
and [MyPaaS](https://github.com/almarklein/mypaas).


## Installation

```
pip install itemdb
```


## Quick usage example

```py
import itemdb

# Open the database and make sure there is a table with appopriate indices
db = itemdb.ItemDB(":memory:")
db.ensure_table("persons", "!name", "age")

# Add some items to the db
with db:
    db.put_one("persons", name="Jane", age=22)
    db.put_one("persons", name="John", age=18, fav_number=7)
    db.put("persons", {"name": "Guido"}, {"name": "Anne", "age": 42})

# Update an item
with db:
    db.put_one("persons", name="John", age=19, fav_number=8)

# Query items
db.count_all("persons")  # -> 4
db.select("persons", "age > ?", 20)  # -> list of 2 items
```

See the [guide](https://itemdb.readthedocs.io/en/latest/guide.html) for details.


## Async

The `AsyncItemDB` class provides the same API, but async:

```py
import itemdb

db = await itemdb.AsyncItemDB(":memory:")
await db.ensure_table("persons", "!name", "age")

async with db:
    await db.put_one("persons", name="Jane", age=22)

...
```

Alternatively, a decorator is provided to turn a normal function into an async one
(running in a separate thread).

```py

@asycify
def your_db_interaction_logic(...):
    db = ItemDB(filename)
    ...

async def your_async_code(...):
    await your_db_interaction_logic(...)
```


## License

MIT


## Developers

* Run `ruff format` to autoformat.
* Run `ruff check` to lint.
* Run `pytest tests` to run unit tests.
grayness-index-python 0.0.5
Python package for Grayness Index
Requires Python: >=3.7
Keywords: computational photography, color constancy, illuminant estimation, computer vision

# Grayness Index

Unofficial Python implementation of the paper named ["On Finding Gray Pixels"](https://openaccess.thecvf.com/content_CVPR_2019/papers/Qian_On_Finding_Gray_Pixels_CVPR_2019_paper.pdf). 

Disclaimer:

*You may copy, distribute and modify the software provided that modifications are described and licensed for free under LGPL. Derivatives works (including modifications or anything statically linked to the library) can only be redistributed under LGPL, but applications that use the library don't have to be.*

## Installation

```
pip install grayness-index-python
```

## Basic Usage

```
import numpy as np
import math
from scipy.io import loadmat

from grayness_index import GraynessIndex

gi = GraynessIndex()
mat = loadmat("imgs/exampleimg.mat")
gt = mat["gt"]
input_im = mat["input_im"]  # np.ndarray with shape H x W x 3.
pred_illum = gi.apply(input_im)
error = np.arccos(pred_illum @ gt.transpose()) * 180 / math.pi
log.info(f"Error: {error}")
```

## References

[Official MatLab Code](https://github.com/yanlinqian/Grayness-Index)

To cite the original paper:

```
@inproceedings{qian2019cvpr,
  title={On Finding Gray Pixels},
  author={Qian, Yanlin and K{\"a}m{\"a}r{\"a}inen, Joni-Kristian and Nikkanen, Jarno and Matas, Jiri},
  booktitle={IEEE International Conference of Computer Vision and Pattern Recognition},
  year={2019}
}
```