semvec/pypi-packages huggingface dataset:
Topsis-Pranshu-102217179 0.1.0
TOPSIS method implementation by Pranshu
Requires Python: >=3.6

# Topsis-Pranshu-102217179

This is an implementation of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method by Pranshu.

## Requirements
- Python 3.6 or higher
- pandas
- numpy

## Installation
You can install the package using pip (once it's uploaded to PyPI):

```bash
pip install Topsis-Pranshu-102217179
topsis-harshtyagi-102203964 1.0.0
A Python implementation of the TOPSIS method for multi-criteria decision making.
Requires Python: >=3.6

---

# **Topsis-Harsh-102203964**

A Python package for implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This method is used for multi-criteria decision-making.

---

## **Installation**

Install the package using `pip`:

```bash
pip install Topsis-Harsh-102203964
```

---

## **Usage**

### **Command-Line Interface**

After installation, you can use the `topsis` command to calculate rankings for your dataset.

#### **Command Syntax**
```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

- `<InputDataFile>`: Path to the input CSV file.
- `<Weights>`: Comma-separated list of weights for each criterion (e.g., `2,1,3,2`).
- `<Impacts>`: Comma-separated list of impacts for each criterion (`+` for positive, `-` for negative).
- `<ResultFileName>`: Path to save the output CSV file.

---

### **Input File Format**
- The input file must be a CSV with at least 3 columns.
- The **first column** should contain the names of the objects (e.g., M1, M2, ...).
- The remaining columns should contain **numeric criteria values**.

#### Example Input (`data.csv`):
```csv
Object,Criteria1,Criteria2,Criteria3,Criteria4
M1,250,16,12,5
M2,200,16,8,3
M3,300,32,16,4
M4,275,16,8,4
M5,225,32,16,2
```

---

### **Example Usage**
1. Prepare the input CSV file (e.g., `data.csv`).
2. Run the following command:

```bash
topsis data.csv 2,1,3,2 +,+,-,+ result.csv
```

3. The result will be saved in the specified output file (e.g., `result.csv`).

#### Example Output (`result.csv`):
```csv
Object,Criteria1,Criteria2,Criteria3,Criteria4,Topsis Score,Rank
M1,250,16,12,5,0.75,2
M2,200,16,8,3,0.45,4
M3,300,32,16,4,0.85,1
M4,275,16,8,4,0.55,3
M5,225,32,16,2,0.30,5
```

---

## **Requirements**

This package requires the following Python libraries:
- `numpy`
- `pandas`

These dependencies are automatically installed with the package.

---

## **License**

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---
bensilence 0.1.4
A Python library for recording voice using voice actvity detection (VAD). Best for AI voice assistants.
Requires Python: >=3.7


This is my first repsitory. So, I may not have done everything properly. I expect your understanding.

# bensilence
A voice recorder with voice activity detection (VAD). It starts recording when VAD detects speech and stops recording as the speaker stops talking for a second. Best use case would be AI assistants. This is just another version of [rhasspy-silence](https://github.com/rhasspy/rhasspy-silence). This library uses [Cobra (Picovoice)](https://picovoice.ai/platform/cobra/) for VAD, which is better than webRTC that was used in [rhasspy-silence](https://github.com/rhasspy/rhasspy-silence).



## Installation

1. Install the package:
    ``` bash
   pip install bensilence 
    ```
### Install via GitHub
1. Clone the repository:
   ``` bash
   git clone https://github.com/benimrans/bensilence.git
   cd bensilence
   ```
2. Install dependencies:
    ```bash
   pip install -r requirements.txt 
   ```
3. Setup:
    ```bash
   py setup.py install 
   ```
## Usage/Examples

You'll need a [Picovoice](https://picovoice.ai) account to get an API key. Once you get it, you are ready to go!

```python
from bensilence import silence

silence = silence(api_key="your_picovoice_api_key")

silence.initialize()

result, file_name = silence.record()

print(result, file_name)
```

Here are some variables that you may want to change:

These are the default values.

```python
before_seconds = 1 # Adds the unrecorded parts to the recording by going back 1 second from the time the speech started.
silence_threshold = 1 # Stops recording if there's silence for 1 second. 
max_sensitivity = 2 # {1: 0.5, 2: 0.7, 3: 0.9} Value that VAD should detect to start recording.
max_recording_time = 30 # Maximum recording time.
```
## Related

This is the reason why I made this library:

[rhasspy-silence](https://github.com/rhasspy/rhasspy-silence)


## License

[MIT](https://choosealicense.com/licenses/mit/)
Topsis-Dhwani-102203783 1.0.1
Topsis-Dhwani-102203783 - A Python Package for Decision Analysis
Requires Python: >=3.6


# Topsis-Dhwani-102203783 - A Python Package for Decision Analysis

Topsis-Dhwani-102203783 is a Python package that implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This method helps in multi-criteria decision-making, enabling users to rank alternatives based on given criteria, weights, and impacts.

## Overview

The Topsis package offers:
- **Ease of Use**: A command-line interface for quick results.
- **Customizable Inputs**: Allows users to define weights and impacts for each criterion.
- **Automated Ranking**: Generates TOPSIS scores and ranks alternatives.

## Installation

You can install the package directly from PyPI using pip:

pip install Topsis-Dhwani-102203783

## Usage

To use the package:
python Topsis-Dhwani-102203783.py 102203783-data.csv "1,1,1,2" "+,+,-,+" 102203783-result.csv


## Author

Dhwani Goyal  
Email: dgoyal_be22@thapar.edu  
GitHub: [Dhwani Goyal](https://github.com/Dhwanigoyal)