semvec/pypi-packages huggingface dataset:
torchdecomp 1.3.4
A set of matrix decomposition algorithms implemented as PyTorch classes
License: MIT
Requires Python: <4.0,>=3.10
Keywords: pytorch, machine learning

[![DOI](https://zenodo.org/badge/739235639.svg)](https://zenodo.org/doi/10.5281/zenodo.10677187)
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# PyTorchDecomp
A set of matrix decomposition algorithms implemented as PyTorch classes


## Installation

Because PyTorchDecomp is a PyPI package, please install it by `pip` command as follows:

```shell
python -m venv env
pip install torchdecomp
```

For the other OS-specific or package-manager-specific installation, please check the [README.md](https://github.com/pytorch/pytorch) of PyTorch.


## Usage

See the [tutorials](https://chiba-ai-med.github.io/PyTorchDecomp/tutorials.html).

## References

- **LU/QR/Cholesky/Eigenvalue Decomposition**
  - Gene H. Golub, Charles F. Van Loan Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)
- **Principal Component Analysis (PCA) / Partial Least Squares (PLS)**
  - R. Arora, A. Cotter, K. Livescu and N. Srebro, Stochastic optimization for PCA and PLS, 2012 50th Annual Allerton Conference on Communication, Control, and Computing, 2012, 861-868. 2012
- **Independent Component Analysis (ICA)**
  - Hybarinen, A. and Oja, E. Independent component analysis: algorithms and applications, Neural Networks, 13, 411-430. 2000
- **Deep Deterministic ICA (DDICA)**
  - H. Li, S. Yu and J. C. Príncipe, Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3878-3882, 2022
- **Non-negative Matrix Factorization (NMF)**
  - Kimura, K. A Study on Efficient Algorithms for Nonnegative Matrix/Tensor Factorization, Ph.D. Thesis, 2017
  - **Exponent term depending on Beta parameter**
    - Nakano, M. et al., Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with Beta-divergence. IEEE MLSP, 283-288, 2010
  - **Beta-divergence NMF and Backpropagation**
    - https://yoyololicon.github.io/posts/2021/02/torchnmf-algorithm/

## Contributing

If you have suggestions for how `PyTorchDecomp` could be improved, or want to report a bug, open an issue! We'd love all and any contributions.

For more, check out the [Contributing Guide](https://github.com/chiba-ai-med/PyTorchDecomp/blob/main/CONTRIBUTING.md).


## License

PyTorchDecomp has a MIT license, as found in the [LICENSE](https://github.com/chiba-ai-med/PyTorchDecomp/blob/main/LICENSE) file.


## Authors
- Koki Tsuyuzaki
quantaforge 0.1.3
A comprehensive library for creating and backtesting trading strategies.
License: MIT
Requires Python: >=3.6

# Quantaforge

Quantaforge is a comprehensive library for creating and backtesting trading strategies.

## Features

- Define custom trading strategies using various indicators, signals, and risk management techniques.
- Backtest strategies with historical data.
- Optimize strategy parameters.
- Generate detailed performance reports.

## Installation
```bash
pip install quantaforge
```

## Usage
```python
import quantaforge as qf

# Create a new strategy
strategy = qf.Strategy()

# Add a new indicator
strategy.add_indicator(qf.Indicator('SMA', 5))

# Add a new signal
strategy.add_signal(qf.Signal('CrossOver', 'SMA', 5))

# Add a new position
strategy.add_position(qf.Position('Long', 1))

# Add a new risk management
strategy.add_risk_management(qf.RiskManagement('StopLoss', 0.02))

# Add a new portfolio
portfolio = qf.Portfolio('SimplePortfolio', 100000)
strategy.add_portfolio(portfolio)

# Add a new backtest
backtest = qf.Backtest(name='SimpleBacktest', strategy=strategy, portfolio=portfolio)

# Mock data to emulate a 5 day SMA crossover strategy

data = {
    'symbol': ['AAPL'] * 20,
    'close': [150, 152, 148, 151, 155, 160, 158, 162, 165, 170, 175, 180, 190, 210, 200, 190, 180, 170, 160, 150]
}

# Set the data for the backtest
backtest.set_data(data)
strategy.add_backtest(backtest)

# Add a new report
report = qf.Report(portfolio=portfolio)
strategy.add_report(report)

# Run the strategy
strategy.run()
strategy.report.generate()
```

## License
[MIT](https://choosealicense.com/licenses/mit/)
yurumikuji 0.6.1
kamidana(jinja2 cli) slack additonal.
License: MIT
Keywords: Slack, Jinja2

# yurumikuji

[![PyPI version](https://badge.fury.io/py/yurumikuji.svg)](https://badge.fury.io/py/yurumikuji)
[![Python Versions](https://img.shields.io/pypi/pyversions/wandbox_api.svg)](https://pypi.org/project/yurumikuji/)

[kamidana][] slack additonal

## Usage

```sh
$ pip install yurumikuji
$ echo "SLACK_TOKEN=<your token>" > .env
$ kamidana --additionals=yurumikuji.yurumikuji sample/profile.j2
https://secure.gravatar.com/avatar/a7614593f3f6f46b73da348c89beba81.jpg?s=512&d=https%3A%2F%2Fa.slack-edge.com%2Fdf10d%2Fimg%2Favatars%2Fava_0023-512.png
https://secure.gravatar.com/avatar/a7614593f3f6f46b73da348c89beba81.jpg?s=512&d=https%3A%2F%2Fa.slack-edge.com%2Fdf10d%2Fimg%2Favatars%2Fava_0023-512.png
active
```

sample/profile.j2

```j2
{{ ("srz_zumix" | slack_user_id | slack_user_info).profile.image_512 }}
{{ ("srz_zumix" | slack_user_id | slack_user_profile).image_512 }}
{{ "srz_zumix" | slack_user_id | slack_user_presence }}
```

## Features

### Filter

|name|usage|detail|slack required scope|
|:--|:--|:--|:--|
|slack_user_id|\<user_name\> \| slack_user_id |user name\|real_name\|display_name\|email to user_id|users:read|
|search_slack_user_id|\[\<user_names\>\] \| slack_user_id | Find user_id from multiple search candidates (user name\|real_name\|display_name\|email)|users:read|
|slack_user_presence|\<user_id\> \| slack_user_presence |get user presence|users:read|
|slack_user_info|\<user_id\> \| slack_user_info |get user info|users:read|
|slack_user_profile|\<user_id\> \| slack_user_profile |get user profile|users.profile:read|
|slack_user_info_by_email|\<user_email\> \| slack_user_info_by_email |get user info|users:read.email|
|slack_usergroup_id|\<usergroup_name\> \| slack_usergroup_id |usergroup name to usergroup_id|usergroups:read|
|slack_usergroup_member_ids|\<usergroup_id\> \| slack_usergroup_member_ids |get usergroup member user ids|usergroups:read|
|slack_usergroup_member_infos|\<usergroup_id\> \| slack_usergroup_member_infos |get usergroup member user info|usergroups:read|
|slack_usergroup_member_infos|\<usergroup_id\> \| slack_usergroup_member_infos |get usergroup member user info|usergroups:read|
|slack_search|\<search_query\> \| slack_search | search message |search:read|
|mention|\<user_or_usergroup_id\> \| mention | get user or group mention text ||
|mention_menber|\<user_id\> \| mention | get mention text ||
|mention_group|\<usergroup_id\> \| mention | get group mention text ||

### Globals

|name|usage|detail|slack required scope|
|:--|:--|:--|:--|
|slack_users_name| slack_users_name | list users name |users:read|
|slack_user_groups| slack_user_groups | list user groups |usergroups:read|

[kamidana]:https://github.com/podhmo/kamidana
spondee 0.1.3
Sentence algorithms for Stanza
License: MIT
Requires Python: <3.13,>=3.10

# Spondee

[Sentence Algorithms](https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/simple/SentenceAlgorithms.html) for [stanza](https://github.com/stanfordnlp/stanza).

## Related Works

Slot filling tasks such as those referenced by the [KILT Benchmark](https://ai.meta.com/tools/kilt/) may 
better accomplish this goal but are generally more compute intensive and less
explainable:

* [Zero-Shot Relation Extraction via Reading Comprehension](https://nlp.cs.washington.edu/zeroshot/)
* [T-Rex : A Large Scale Alignment of Natural Language with Knowledge Base Triples](https://hadyelsahar.github.io/t-rex/)

Also see [NLP-Progress: Intent Detection and Slot-Filling](https://nlpprogress.com/english/intent_detection_slot_filling.html).

This package extends grammar parsing from [Paradigms of Artificial 
Intelligence Programming: Case Studies in Common Lisp, Norvig (1992)](https://norvig.github.io/paip-lisp/#/).