semvec/pypi-packages huggingface dataset:
StableCox 0.3
The official implement of Stable Cox
Requires Python: >=3.6

[![DOI](https://zenodo.org/badge/864352564.svg)](https://doi.org/10.5281/zenodo.13852489)

# System Requirements
## Hardware requirements
`Stable Cox' package requires only a standard computer with enough RAM to support the in-memory operations.

## Software requirements
### OS requirements
This package is supported for *Linux*. The package has been tested on the following system:
+ Linux: Ubuntu 18.04

### Python Dependencies
'Stable Cox' mainly depends on the Python scientific stack.

```
lifelines=0.27.8
numpy=1.20.3
pandas=2.0.3
scikit-learn=1.3.0
```

# Run demo

## omics data
# Select topN biomarker and build a predictor on the selected biomarker panel
from StableCox import StableCox
import pandas as pd
training_pd_data = pd.read_csv('./omics_data/HCC_cancer/train_median.csv', index_col=0)

test1_pd_data = pd.read_csv('./omics_data/HCC_cancer/test1_median.csv', index_col=0)

SC = StableCox(alpha=0.0005, hidden_layer_sizes = (98, 11), W_clip=(0.4, 4))

duration_col = "Survival.months"

event_col="Survival.status"

SC.fit(training_pd_data, duration_col, event_col)

cindex = SC.predict_with_topN(test1_pd_data, topN=10)

print("cindex", cindex)


## clinical data
# Make prediction directly without biomarker selection

import pandas as pd
from StableCox import StableCox
training_pd_data = pd.read_csv('./clinical_data/breast_cancer/breast_train_survival.csv', index_col=0)

test1_pd_data = pd.read_csv('./clinical_data/breast_cancer/breast_test1_survival.csv', index_col=0)

training_pd_data = training_pd_data.drop(['Recurr.months', 'Recurr.status', 'Cohort'], axis=1)

test1_pd_data = test1_pd_data.drop(['Recurr.months', 'Recurr.status', 'Cohort'], axis=1)

SC = StableCox(alpha=0.002, hidden_layer_sizes = (69, 15), W_clip=(0.02, 2))

duration_col = "Survival.months"

event_col="Survival.status"

SC.fit(training_pd_data, duration_col, event_col)

cindex = SC.predict(test1_pd_data)

- The expected running time is from several seconds to mins depends on the number of samples.

# License
This project is licensed under the terms of the MIT license.
ipapocket 0.1.0
Toolset for interaction with FreeIPA

# ipapocket

`ipapocket` is a python library for interacting with FreeIPA network protocols. `ipapocket` is focused on providing low-level programmatic access to protocols through a convenient object-oriented API, with which you can construct packets from scratch or parse them from raw data. `ipapocket` also provides several tools as an example of what can be done with this library.

## Why?

Due to lack of [support for FreeIPA](https://github.com/fortra/impacket/pull/1684#issuecomment-1986367074) features in impacket and attempts to make a more user-friendly interface for interacting with kerberos (we plan to add more network protocols in the future).

## For developers

**WARNING: This version of the codebase is under active development so the API may change over time**

Install it via `pip` form GitHub:

```sh
pip install git+https://github.com/nu11zy/ipapocket
```

Consider to use a Python virtual environment.

## For pentesters

You can install the tools from the examples using `pipx`:

```sh
pipx install git+https://github.com/nu11zy/ipapocket
```

## Examples AKA the pentest tools

`ipp-cve-2024-3183.py`: PoC for CVE-2024-3183. To crack hashes with AES256-SHA1 (etype 18) you can use this [fork of hashcat](https://github.com/nu11zy/hashcat) with mode `32900`

`ipp-id2entry.py`: parse output of `dbscan -f id2entry.db` and decrypt principals keys (can be used for creation of tickets)

`ipp-get-tgt.py`: get AS-REP and save TGT to CCACHE (normal authentication flow with PASSWORD)

`ipp-get-tgt-spake.py`: get AS-REP and save TGT to CCACHE (SPAKE hardened password)

`ipp-get-tgs.py`: get TGS-REP and save TGS to CCACHE (only use TGT from KRB5CCNAME)

`ipp-user-enum.py`: enumarate users via Kerberos

`ipp-show-ccache.py`: describe credentials in CCACHE
rm-onelineai 0.1.1
A Python module for interacting with various AI models
Requires Python: >=3.8

# rm_onelineai

A Python module for interacting with various AI models including OpenAI, Google's Gemini, and Anthropic's Claude.

## Installation

```bash
pip install rm_onelineai
```

## Usage

```python
from rm_onelineai import openai_module, gemini_module, claude_module

# Use OpenAI
response = openai_module.generate_text("Your prompt here")

# Use Gemini
response = gemini_module.generate_text("Your prompt here")

# Use Claude
response = claude_module.generate_text("Your prompt here")
```

## License

MIT License