semvec/pypi-packages huggingface dataset:
knnclassifier 1.0.0
A k-nearest neighbors classifier in Python 3.11.7
Requires Python: >=3.8

# KNN Classifier

**A k-Nearest Neighbors supervised classifier in Python 3.11.7**

---

# Importing the package

```bash
pip install knnclassifier
```

```python
from knnclassifier import KNNClassifier
```

This statement imports the `KNNClassifier` class, which will be used to create your classifier objects.

# Using the package

## Fitting the classifier

_Set up your training and test data and fit the classifier._

1. Populate two text files with your training and test data.

> 1.4, 4.0, 15.0, 1.1, 1.6, 5.0\
> 1.2, 4.0, 22.2, 2.2, 3.0, 6.0\
> ...

Each line serves as a record and contains a list of its data points (fields), with the record's label as the final item
in the list. In the above examples, the labels are 5.0 and 6.0, respectively. Ensure that fields are separated by a
consistent **delimiter** (e.g., a comma and a space).

2. Split the data and label arrays from the files.

```python
train_data, train_label = KNNClassifier.split_data_label_from_file("train.txt", delimiter=", ")
test_data, actual_label = KNNClassifier.split_data_label_from_file("test.txt", delimiter=", ")
```

The static method `split_data_label_from_file` returns two numpy arrays. The first contains all columns except the
last (the **data** columns), and the second contains the last column (the **label** column).

You may also specify the `delimiter`. By default, it is `", "`, but you may specify any string.

3. Initialize a `KNNClassifier` object and fit it to the training arrays.

```python
classifier = KNNClassifier()
classifier.fit(train_data, train_label)
```

## Making and checking predictions with the classifier

1. Compute and store label predictions for the test data.

```python
test_label_predictions = classifier.predict(test_data, k=5)
```

The `predict` method returns the predicted labels of each record of the test data. Specify the k value to be used during
prediction.

> [!NOTE]
> Ensure that the test data has the same number of fields (columns) as the training data. This is required for the
> algorithm to function correctly.

2. Check the accuracy of the label predictions.

```python
accuracy: float = KNNClassifier.check_accuracy(test_label_predictions, actual_label, print_info=False)
```

The static method `check_accuracy` returns the proportion of the predicted labels that match the actual labels.

You may also specify `print_info` (default: False). If set to True, each label will be printed individually.
Clypher 0.4
Encrypt and decrypt from the command line.
Requires Python: >=3.10

# Clypher

Clypher is a simple command-line interface application for encrypting and decrypting files.

## Features

- Encrypt and Decrypt files using the command line.
- Batch encryption and decryption.
- Easily extendable with new encryption algorithms.

## Installation

You can install Clypher via `pip`.

```bash

pip install clypher

```

## Usage

Encrypt a file:

```bash

clypher enc <file_path>

```

Decrypt a file:

```bash

clypher dec <file_path>

```

You can also encrypt and decrypt multiple files at the same time:

```bash

clypher enc <file_path1> <file_path2> <file_path3>

clypher dec <file_path1> <file_path2> <file_path3>

```

You can even encrypt the contents of a directory:

```bash

clypher enc ./foo/

```

Or, encrypt everything in said directory recursively:


```bash

clypher enc ./foo/ --recursive

```


To display the list of available commands:

```bash

clypher --help

```

To display more information about a command:

```bash

clypher <command> --help

```

## Documentation

For more information about how Clypher works, check the [documentation](https://maxacan.github.io/clypher).

## Contributing

Contributions are welcome! Please read the [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines.

## License

This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.
EmailTemplate 0.0.3
Email sending tempalte for begginers to send emails
Requires Python: >=3.8

# EmailTemplate
    This is to allow beginners to easily send emails.  This project contains some
    basic templates.

## emailtablenofile
    This is used to send a HTML file created by pandas in the email by passing
    the html table direclty to the template.

    Requirements:
        host= SMTP address
        sender = Senders email address
        password = Senders password
        port = Senders email port address
        reciver = Who is getting the email
        title = subject line to the email.
        text = the bodys text
        html = the HTML content for the email.

## emailtextnofile
    This is used to send emails with text only.

    Requirments:
        host= SMTP address
        sender = Senders email address
        password = Senders password
        port = Senders email port address
        reciver = Who is getting the email
        title = subject line to the email.
        text = the bodys text

## emailwithfile
    This is used to send emails with a file attached.

    Requirments:
        host= SMTP address
        sender = Senders email address
        password = Senders password
        port = Senders email port address
        reciver = Who is getting the email
        title = subject line to the email.
        text = the bodys text
        filepath = the file you want to send.
eseq 0.0.4
Process genomic sequences.
Keywords: pypi, cicd, python

\n#eSeq: process sequence

## introduction
The tool known as eSeq can offer methods for process various sequences. Those sequence could be DNA, RNA, amino acid sequence, etc.
- Detect specific sequence, namely palindromic, similarity etc.
- Iterator of sequence
- Trim adapter from sequence.
- Detect motif

quick installation
```
pip install eseq
```

## devlopment

Launch virtual environment.
```
git clone git@github.com:Tiezhengyuan/bio_sequence.git
cd bio_sequence
source venv/bin/activate
```

Run unit tests
```
pytest tests/unittests
```


## Quick tour

```
import eseq
```
RipeDB 0.4.0
A tool for making analyssis on RipeDB
Keywords: ripe ripedb

[![Downloads](https://static.pepy.tech/badge/ripedb)](https://pepy.tech/project/ripedb)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

# RipeDB
<p align="center">
  <img src="https://github.com/apt-0/RipeDB/blob/main/assets/RipeDB_Image.jpg" width="300" height="300">
</p>

RipeDB is a Python tool designed to facilitate the search and analysis of internet domain data through the RIPE DATABASE. This project allows users to obtain detailed information about domains and their IP assignments.

## Features

- Search for domain information using customizable parameters.
- Analysis of collected data and generation of xlsx reports.
- Simple and intuitive user interface.

## Prerequisites

Before you begin, make sure you have Python 3.x installed on your system. RipeDB depends on some external packages, which are listed in the requirements.txt file.

## Installation

### Stable Version
RipeDB can be easily installed via pip. This method is recommended for most users as it automatically manages all project dependencies.

To install RipeDB, run the following command:

```bash
pip install ripedb
```

### Latest Version
If you want the Stable Version use this methods:

#### PIP
```bash
git clone https://github.com/apt-0/RipeDB
cd RipeDB
pip install .
```
If you want the Latest Version use this methods:

#### Python
```bash
git clone https://github.com/apt-0/RipeDB
cd RipeDB
python -m ripedb.main
```

## Usage
To start RipeDB, run the following command:
```bash
ripedb
```
or

```bash
ripedb -q <Search-Parameter>
```
If you want to edit the result and perform DNS LOOKUP, you have to enter in editing-mode

```bash
ripedb -em
```
or 

```bash
ripedb -q <Search-Parameter> -em
```

## License
Distributed under the MIT License. See LICENSE for more information.

## Contacts
APT-0  - cryptovortex@outlook.com

## Contributors
[![Contributors](https://contrib.rocks/image?repo=apt-0/RipeDB)](https://github.com/apt-0/RipeDB/graphs/contributors)