semvec/pypi-packages huggingface dataset:
storage-script 0.2.4
Rasa Storage Script
Requires Python: >=3.7,<4.0

# storage-script

Script for uploading/downloading models to/from AWS S3 Cloud Storage

- [Installation](#installation)
- [Usage](#usage)

## Environment Variables

Please make sure to add the following variables in your environment

```
export AWS_ACCESS_KEY_ID=
export AWS_SECRET_ACCESS_KEY=
export AWS_DEFAULT_REGION=
export AWS_S3_BUCKET_RASA_MODELS=
export AWS_ENDPOINT_URL=
```

## Requirements

* Local Python Installation
* [Boto3](https://pypi.org/project/boto3/)

## Installation
```
pip install git+https://github.tools.sap/dsx/storage-script.git
```

## Usage

```
rasa_storage [-h] [--version] {upload,download} 

    positional arguments:
    {upload,download}  Rasa Storage Commands
        upload           Uploads RASA models to AWS S3
        download         Downloads RASA models from AWS S3
    optional arguments:
    -h, --help         show this help message and exit
    --version, -v      Package Version
````

### Upload

```
rasa_storage upload [-h] --model MODEL [--folder FOLDER]
    -h, --help            show this help message and exit
    --model MODEL, -m MODEL
                            Relative path to the Model (.tar.gz file) (default:
                            None)
    
    optional arguments:
    --folder FOLDER, -f FOLDER
                            Optional: Folder Name where the model needs to be
                            saved in AWS S3 (default: master)
````

### Download

```
rasa_storage download [-h] --model MODEL [--folder FOLDER]

    -h, --help            show this help message and exit
    --model MODEL, -m MODEL
                            Model Name(.tar.gz file) (default: None)
    optional arguments:
    --folder FOLDER, -f FOLDER
                            Optional: Folder Name from where the model needs to be
                        downloaded (default: master)
```
py4svd-regressor 0.0.1
SVD Based Linear Regression Python Library
Requires Python: >=3.7

# py4svd-regressor

SVD Based Linear Regression Python Library

## Getting Started

This project is simply an implementation of a Linear Regression algorithm based on SVD in python programming language

### Prerequisites

Numpy

### Installation

The easiest way to install py4svd-regressor is by using pip

```
pip install py4svd-regressor
```

### Usage
2 public methods are provided namely ```learn``` and ```predict```. The ```learn``` method used to train the model. It takes 2 arguments, the data, and its target. The ```predict``` method used to predict the given data, it takes 1 argument, it is the  data user wanted to predict. It returns the resulting prediction. The weight and intercept are stored on attributes namely ```w``` and ```b``` respectively
```
from py4svd_regressor.regression import Svd_Regressor
from sklearn.datasets import make_regression
from matplotlib import pyplot

x, y = make_regression(n_samples=200, n_features=1, noise=5)
model = Svd_Regressor()
model.train(x, y)
pyplot.scatter(x, y)
pyplot.plot(x, model.w*x + model.b, color='red')
pyplot.show()
y_predict = model.predict(x)
```
<a href="https://ibb.co/1bmf57T"><img src="https://i.ibb.co/QXKFRpM/svd-regression.png" alt="svd-regression" border="0"></a>