semvec/pypi-packages huggingface dataset:
aide-fusu-jihua 2022.11.7.0
爱的复苏‘计划
Requires Python: >=3.6

# 爱的复苏‘计划

## 下载

### Docker

```
docker pull apachecn0/aide-fusu-jihua
docker run -tid -p <port>:80 apachecn0/aide-fusu-jihua
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install aide-fusu-jihua
aide-fusu-jihua <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g aide-fusu-jihua
aide-fusu-jihua <port>
# 访问 http://localhost:{port} 查看文档
```
aicong-jiena-ziji-kaishi 2022.11.7.0
爱从接纳自己开始
Requires Python: >=3.6

# 爱从接纳自己开始

## 下载

### Docker

```
docker pull apachecn0/aicong-jiena-ziji-kaishi
docker run -tid -p <port>:80 apachecn0/aicong-jiena-ziji-kaishi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install aicong-jiena-ziji-kaishi
aicong-jiena-ziji-kaishi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g aicong-jiena-ziji-kaishi
aicong-jiena-ziji-kaishi <port>
# 访问 http://localhost:{port} 查看文档
```
pyvimond 0.5.0
Tiny client library for various Vimond APIs
Requires Python: >=3.6

# pyvimond

Tiny Python client library for various Vimond APIs. Currently it provides clients for:

* Vimond REST API
* Vimond Image Service

## Usage

The examples here can be found in the example files in the project root directory

### Vimond REST API

```python
from pyvimond.vimond_client import VimondClient

if __name__ == "__main__":
    # Create a client
    api_url = "https://vimond"
    user = "user"
    secret = "secret"
    vimond = VimondClient(api_url, user, secret)

    # Get an asset
    asset = vimond.get_asset(1334686)
    print(asset["uri"])

    # Get the asset's category
    category = vimond.get_category(asset["categoryId"])
    print(category["category"]["@uri"])
§
    # Get the metadata for the asset
    metadata = vimond.get_asset_metadata(1334686)
    print(metadata)
```

### Vimond Image Service

The client uses HTTP Basic authentication when interfacing with Vimond Image Service

```python
from pyvimond.vimond_image_service_client import VimondImageServiceClient

if __name__ == "__main__":
    # Create a client
    api_url = "https://image-service"
    username = "user"
    password = "secret"
    vimond_image_service = VimondImageServiceClient(api_url, username, password)

    # Create an image pack
    imagepack_id = vimond_image_service.create_image_pack()
    print(f"Created new imagepack with ID {imagepack_id}")

    # Update the "main" location with the image from a publicly accessible URL.
    # The function will return an ID consisting of the imagepack ID plus the current UNIX timestamp. This
    # can eventually be used to reference this version of the image (assuming it gets cached somewhere) even after
    # the image has been updated later on.
    public_url = "https://cdn.pixabay.com/photo/2020/12/03/12/35/sunset-5800386_960_720.jpg"
    timestamped_imagepack_id = vimond_image_service.send_image(imagepack_id, public_url, "main")

    # Get the URL to the timestamped version with location "main"
    url = vimond_image_service.create_image_url(timestamped_imagepack_id, location="main")
    print(url)

    # Get the URL to the untimestamped version and no location
    url_without_location = vimond_image_service.create_image_url(imagepack_id)
    print(url_without_location)
```

## Testing

```bash
python3 -m unittest discover
```

## Releasing

Before beginning, make sure you have the necessary tools installed:

```bash
pip install --user --upgrade setuptools wheel twine
```

To build a new distribution and publish it on PyPi:

1. Update the version number in `setup.py`
2. Run `make release`. When asked for credentials, use `__token__` as username and the output 
   of `vault -l pypi-pyvimond-api-token` as password.

More information: https://packaging.python.org/tutorials/packaging-projects/
info-tik 20.0
For developers
Keywords: LegendsLIB


<h1 align="center">LegendsLIB</h1>
<p align="center">It's a Usefull Project for Developers ... It checkers Emails Found Or Not Found</p>

<p align="center"> • DevVlopered The Lib BY ALAA7X- SHAFEY• </p>


## Installing :
```
pip install info_tik

```
PY:-ALAA7X-ELSHAFEY
```
https://t.me/ALAA7X
```
https://t.me/Elshafey_Team
```
choquet-classifier-glenscalai 0.0.1
An implementation of the monotone nonlinear Choquet classifier for binary classification
Requires Python: >=3.9

# Choquet-classifier

Implementation of the Choquet classifier. This model was presented in "Learning monotone nonlinear models using the Choquet integral" [[1]](#1)
- 🚧Under Construction🚧

## Installation

### Dependencies
- Python (>=3.9)
- NumPy (>=1.23.3)
- SciPy (>=1.9.1)
- scikit-learn (>=1.1.1)

### User installation
- clone this repository or
- pip installation will be added soon

# Usage
The application is compatible to [scikit-learn](https://scikit-learn.org/stable/) and can be used like every other classifier from the scikit-learn library. In order to use the Choquet classifier, import the class **ChoquetClassifier** from the module **choquet_classifier** from the package **classifier**. Some examples are shown below:

## Default example

Use the contructor and the function **fit** to initialize the Choquet classifier for a given dataset.

```python
>>> from classifier.choquet_classifier import ChoquetClassifier
>>> X = [[1, 3, 2],
...      [1, 0, 3]]
>>> y = [1, 0]
>>> cc = ChoquetClassifier()
>>> cc.fit(X, y)
```
Use the function **predict** to classify samples.

```python
>>> Z = [[1, 1, 2],
...      [2, 1, 3]]
>>> cc.predict(Z)
array([0, 0])
```

## Examples with hyper-parameters

```python
>>> from classifier.choquet_classifier import ChoquetClassifier
>>> X = [[1, 3, 2],
...      [1, 0, 3]]
>>> y = [1, 0]
>>> cc = ChoquetClassifier(additivity=3, regularization=1)
>>> cc.fit(X, y)
```
Again, the function **predict** can be used to classify samples. Note the different output compared to the first example.

```python
>>> Z = [[1, 1, 2],
...      [2, 1, 3]]
>>> sc.predict(Z)
array([0, 1])
```

## Example with different class labels

The classes do not have to be labeled with 0 and 1; any integer numbers or strings may be used instead. The smaller label in terms of the relation or lexicographic ordering is given to the negative class, whereas the other label is assigned to the positive class.

The first example contains the class labels 2 and 1. Label 2 is assigned to the positive class and label 1 is assigned to the negative class since 2>1.

```python
>>> from classifier.choquet_classifier import ChoquetClassifier
>>> X = [[1, 3, 2],
...      [1, 0, 3]]
>>> y = [2, 1]
>>> cc = ChoquetClassifier()
>>> cc.fit(X, y)
>>> Z = [[1, 1, 2],
...      [2, 1, 3]]
>>> sc.predict(Z)
array([1, 1])
```

The second example contains the class labels 'one' and 'two'. Label 'one' is assigned to the negative class and label 'two' is assigned to the positive class because 'one' comes lexicographically first.

```python
>>> from classifier.choquet_classifier import ChoquetClassifier
>>> X = [[1, 3, 2],
...      [1, 0, 3]]
>>> y = ['two', 'one']
>>> cc = ChoquetClassifier()
>>> cc.fit(X, y)
>>> Z = [[1, 1, 2],
...      [2, 1, 3]]
>>> sc.predict(Z)
array(['one', 'one'])
```

# Licence
[MIT](https://choosealicense.com/licenses/mit/)

# Reference
[1] Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski and Eyke Hüllermeier. Learning monotone nonlinear models using the Choquet integral. 2012.