semvec/pypi-packages huggingface dataset:
pan-biclustering 1.0.0
Python implementation of the PAN biclustering algorithm
Requires Python: >=3.8
Keywords: PAN, BICLUSTERING, PARETO-SET-ANALYSIS

# PAN
Python implementation of the PAN biclustering algorithm [[1]](#1).

PAN is made to perform analysis on the Pareto set and front resulting from a multi-objective optimization method. In its turn, PAN is a bi-objective evolutionary algorithm, performing clustering in two spaces at the same time. Since this is another conflicting optimization problem, PAN returns multiple partitionings (collections of clusters), from which the user can choose their preferred partitioning.

PAN makes a few assumptions about the given data:
- The best partitioning in the objective space differs from the best partitioning in the decision space. If this is not the case, a single objective clustering algorithm can be used on one of the two spaces.
- Each cluster contains at least two solutions. If this is not the case, outliers need to be manually removed.

To speedup optimization, PAN uses k-medoids clustering as a local heuristic, implemented here using the fast [kmedoids](https://github.com/kno10/python-kmedoids) package.


## Installation
A pre-built package is available at [Pypi]() and can be installed with:
```sh
pip install pan_biclustering
```


## Usage
A quick example usage can be found below.

```python
from pan_biclustering.pan import PAN

# Decision space of points 1, 2, 3 and 4
pareto_set = [
    [1, 2], [2, 1], [14, 14], [13, 13],
]
# Objective space of points 1, 2, 3 and 4
pareto_front = [
    [14, 14], [13, 13], [1, 2], [2, 1],
]

def euclidean_distance(p1, p2):
    return sum([(a - b) ** 2 for a, b in zip(p1, p2)]) ** 0.5

pan = PAN(pareto_set, pareto_front, euclidean_distance)
population, population_indices = pan.find_clusters(5, 400)
partitioning = population[0]

# Two clusters, both containing two of the points 1, 2, 3 and 4
print(partitioning)
```

A second, more elaborate example can be found in the /examples folder.

To automatically pick a partitioning out of the found partitionings, a knee-point detection algorithm such as [kneed](https://pypi.org/project/kneed/) can be used on the returned population_indices.

## References
<a id="1">1</a>
Ulrich, T. (2013), Pareto-Set Analysis: Biobjective Clustering in Decision and Objective Spaces. J. Multi-Crit. Decis. Anal., 20: 217-234. https://doi.org/10.1002/mcda.1477
cmdflow-CLI 0.1.1
A CLI tool for system monitoring, making plot charts and etc.
Requires Python: >=3.6

# <CMD_FLOW: Command-Line Interface

# Overview
**CMD_FLOW** is a command-line interface (CLI) tool that provides a wide range of system information and utilities just enterting a specific commands and much more. It allows users to easily access various system metrics, perform tasks, and create visualization all from the comfort of their terminal.

# Features
- CPU usage
- CPU cores
- CPU frequency
- Memory usage
- Disk usage
- System boot time
- Battery status
- System processes
- Current user information
- Network usage
- Network connection information
- Internet connection status

# Utilities
- Open web-based documents and applications using their URLs.

- Create scatter charts, plot charts, and stem charts from user-inputted data.

- Generate tables in the terminal with custom titles, columns, and rows.

- Display all available commands in a table format.

- Create ASCII text art.

# Date and Time
- Current time, date, day, year, month, and dd/mm/yyyy format

# Installation

To install CMD_FLOW, use pip:
```bash
pip install cmdflow_CLI
```

# Usage

To use CMD_FLOW, simply run the command and pass in the desired command as an argument. For example:
 ```bash
 cmdflow <command>
 ```

For a list of all available commands and options, run:
 ```bash
 cmdflow --help
 ```



# Available Commands

- `cpu_usage`: Check CPU usage
- `cpu_cores`: Check CPU cores
- `cpu_freq`: Check CPU frequency
- `mem_usage`: Check memory usage
- `my_mem`: Know about your memory
- `disk_usage`: Know about your disk usage
- `boot_time`: Check system boot time
- `ch_battery`: Check battery status
- `sys_process`: Check system processes
- `c_user`: Check current user information
- `net_usage`: Check network usage
- `net_connection`: Know about your network connection
- `internet_con`: Know about your internet connection
- `t?`: Check current time
- `d?`: Check current date
- `day?`: Check current day
- `y?`: Check current year
- `m?`: Check current month
- `c-d?m?Y`: Check current date in dd/mm/yyyy format
- `open_url`: Open a web-based document or application using its URL
- `graph`: Create a scatter chart, plot chart, or stem chart
- `m_table`: Create a table in the terminal
- `cmds`: Display all available commands in a table format
- `c_ascii_text`: Create ASCII text art

# Acknowledgments

- [Raunak](https://github.com/CoderRony955) for creating and maintaining CMD_FLOW.