semvec/pypi-packages huggingface dataset:
emotitext 0.1.0
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# EmotiText ðŸš€

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## QuraÅŸdÄ±rÄ±lma
```bash
pip install emotitext
liesel 0.4.4
A probabilistic programming framework with a focus on semi-parametric regression
Requires Python: <3.14,>=3.13
Keywords: machine-learning, statistics

<!-- This README is for PyPI. The README for GitHub is in .github/README.md -->

# Liesel: A Probabilistic Programming Framework

<img src="https://raw.githubusercontent.com/liesel-devs/liesel/main/misc/logo/logo-light.png" alt="logo" align="right" width="185">

Liesel is a probabilistic programming framework with a focus on semi-parametric regression. It includes:

- [**Liesel**](https://github.com/liesel-devs/liesel), a library to express statistical models as Probabilistic Graphical Models (PGMs). Through the PGM representation, the user can build and update models in a natural way.
- **Goose**, a library to build custom MCMC algorithms with several parameter blocks and MCMC kernels such as the No U-Turn Sampler (NUTS), the Iteratively Weighted Least Squares (IWLS) sampler, or different Gibbs samplers. Goose also takes care of the MCMC bookkeeping and the chain post-processing.
- [**Liesel-GAM**](https://github.com/liesel-devs/liesel_gam), a modeling library built on Liesel which assists the user with the
  configuration of semi-parametric regression models such as Generalized Additive
  Models for Location, Scale and Shape (GAMLSS) with different response distributions,
  spline-based smooth terms and shrinkage priors.

The name "Liesel" is an homage to the [Gänseliesel fountain](https://en.wikipedia.org/wiki/G%C3%A4nseliesel), landmark of Liesel's birth city [Göttingen](https://en.wikipedia.org/wiki/G%C3%B6ttingen).

## Resources

- [Paper on arXiv](https://arxiv.org/abs/2209.10975)
- [Liesel & Goose repo](https://github.com/liesel-devs/liesel)
- [Liesel & Goose API docs](https://docs.liesel-project.org)
- [Liesel-GAM repo](https://github.com/liesel-devs/liesel_gam)
- [Tutorials](https://docs.liesel-project.org/en/latest/tutorials_overview.html)
Topsis-Vikas-102303451 1.0.1
A Python package for TOPSIS implementation
Requires Python: >=3.6

# TOPSIS-Vikas-102303451

[![PyPI version](https://img.shields.io/pypi/v/Topsis-Vikas-102303451)](https://pypi.org/project/Topsis-Vikas-102303451/)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## Project Description
**TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method. It evaluates alternatives based on their geometric distance to the ideal best value and the ideal worst value.

This project implements TOPSIS in Python and provides three interfaces:
1.  **Command Line Interface (CLI)**: For quick local usage.
2.  **Python Package**: Reusable library for your scripts.
3.  **Web Service**: A user-friendly web app to upload data and get results via email.

---

## Live Web Service
The web application is deployed and accessible at:
> **[https://topsis-vikas-102303451.vercel.app](https://topsis-vikas-102303451.vercel.app)**

---

## Installation & Usage

### 1. Python Package
Install from PyPi:
```bash
pip install Topsis-Vikas-102303451
```

Use in your code:
```python
from topsis_vikas import topsis

# topsis(input_file, weights, impacts, output_file)
topsis("data.csv", "1,1,1,1", "+,+,+,-", "output.csv")
```

### 2. Command Line Interface
```bash
topsis data.csv "1,1,1,1" "+,+,+,-" result.csv
```
*   **Weights**: Comma-separated (e.g., `1,1,1,1`)
*   **Impacts**: Comma-separated `+` or `-` (e.g., `+,+,+,-`)
*   **Input File**: Must contain numeric values from 2nd to last column.

---

## How TOPSIS Works
1.  **Normalization**: Normalize the decision matrix so that each criterion is comparable.
2.  **Weighting**: Multiply the normalized matrix by the weights of each criterion.
3.  **Ideal Best & Worst**: Identify the ideal best and ideal worst values for each column.
4.  **Separation Measures**: Calculate Euclidean distance of each alternative from the ideal best and worst.
5.  **Score Calculation**: Calculate the performance score.
6.  **Ranking**: Sort alternatives by score in descending order.

---

## Author
**Vikas Verma**
*   **Roll Number**: 102303451
*   **Email**: vverma_be22@thapar.edu
*   **GitHub**: [vikasverma](https://github.com/vikasverma)

Constructed with ❤️ for the Thapar Institute of Engineering & Technology.
topsis-kenpreet-102303365 1.0.2
TOPSIS implementation using Python

# TOPSIS Python Package

This package implements the TOPSIS  method as a command-line tool.

TOPSIS is a multi-criteria decision-making technique used to rank alternatives based on their distance from an ideal best and an ideal worst solution.

---

# Installation

Install the package from PyPI using:

```bash
pip install topsis-kenpreet-102303365

# Usage
topsis <input_file.csv> "<weights>" "<impacts>" <output_file.csv>
# Example
topsis data.csv "1,1,1,1,1" "+,+,-,+,+" result.csv
chronically-needs-csv 0.0.2
A JSON to CSV converter for analysts who chronically can't parse JSON themselves.
Requires Python: >=3.10
Keywords: analyst, chronis, converter, csv, json

# chronically-needs-csv

> *For Kostas, the analyst blessed with mass amounts of Chronos (time), yet mysteriously none to spare for learning `json.load()`.*

A JSON to CSV converter for people who **chronically** can't be bothered to parse JSON themselves.

## Installation

```bash
pip install chronically-needs-csv
```

Or if you somehow mess that up too:

```bash
pip install git+https://github.com/JellevanE/chronically-needs-csv.git
```

## Usage

### Command Line

```bash
# The tool you'll chronically need
chronically-needs-csv data.json

# Or use the shorthand (named after you-know-who)
chronis data.json

# Specify output file
chronis data.json output.csv

# Use pipe delimiter for arrays (good for BigQuery, Konstantinos)
chronis data.json -d "|"

# Silent mode (if you can't handle the truth)
chronis data.json --silent
```

### Python

```python
from chronically_needs_csv import convert, json_to_csv_string

# Convert file to file
convert("data.json", "output.csv")

# Convert data directly
data = [{"name": "Chronis", "skill": "SQL", "json_knowledge": None}]
csv_string = json_to_csv_string(data)
```

## Features

- **Nested objects** → flattened with dot notation (`user.address.city`)
- **Arrays** → joined with delimiter (default: `, `)
- **Missing fields** → empty cells (like your JSON knowledge)
- **Any JSON shape** → handled automatically

## Example

Input (`analysts.json`):
```json
[
  {
    "name": "Konstantinos",
    "tools": {"primary": "BigQuery", "avoided": "Python"},
    "skills": ["SQL", "asking friends for help"]
  }
]
```

Output (`analysts.csv`):
```csv
name,tools.primary,tools.avoided,skills
Kostas,BigQuery,Python,"SQL, asking friends for help"
```

## BigQuery Import

After converting, Kostas can finally do what he does best:

```sql
LOAD DATA INTO my_dataset.my_table
FROM FILES (
  format = 'CSV',
  uris = ['gs://bucket/data.csv']
);
```

## FAQ

**Q: Why is this package called "chronically-needs-csv"?**
A: Because someone *chronically* needs CSV files and his name happens to sound like Chronos. Coincidence? We think not.

**Q: Can I use this even if my name isn't Konstantinos?**
A: Yes, but you'll still see messages about him. Consider it a feature.

**Q: Why not just use pandas?**
A: Because then we couldn't make this joke.

---

*Made with mild exasperation and mass amounts of love.*
Topsis-Naman-102303352 1.0.2
A Python package for TOPSIS multi-criteria decision making.

# Topsis-Naman-102303352

**For:** UCS654 (Predictive Analytics)  
**Submitted by:** Naman Singh  
**Roll Number:** 102303352  

---

## Overview
This is a Python package that implements the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**. It is a multi-criteria decision-making (MCDM) method used to rank alternatives based on their distance from the "Ideal Best" and "Ideal Worst" solutions.



## Installation

You can install the package directly from PyPI using the following command:

```bash
pip install Topsis-Naman-102303352


## Usage

The package is designed to be executed via the command line. Use the following format:

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

## Example Input

The input file must be a CSV where the first column contains the names of the alternatives and the subsequent columns contain numeric data.

Model,Storage,Camera,Price,Looks
M1,16,12,250,5
M2,16,8,200,3
M3,32,16,300,4
M4,32,8,275,4

## Example Command

```bash
topsis data.csv "1,1,1,2,1" "+,+,-,+,+" result.csv

## Example Output

After running the command, the package performs Vector Normalization and calculates the Euclidean Distance from the Ideal Best and Ideal Worst solutions to generate the final scores and rankings.

The resulting file will contain your original columns plus the calculated Topsis Score and Rank:

Model,Storage,Camera,Price,Looks,TopsisScore,Rank
M1,16,12,250,5,0.5342,3
M2,16,8,200,3,0.3891,4
M3,32,16,300,4,0.7215,1
M4,32,8,275,4,0.6128,2

## License

https://choosealicense.com/licenses/mit/