semvec/pypi-packages huggingface dataset:
parish 0.0.1.dev1
Minimal Reinforcement Learning Framework!
Requires Python: >=3.8


<p align="center">
    <h1 align="center">📿 Parish</h1>
    <p align="center">
        <i>Minimal Reinforcement Learning Framework</i>
    </p>
</p>

<p align="center">
    <a href="https://choosealicense.com/licenses/mit/" target="_blank">
        <img alt="license" src="https://img.shields.io/github/license/nmichlo/parish?style=flat-square&color=lightgrey"/>
    </a>
    <a href="https://pypi.org/project/parish" target="_blank">
        <img alt="python versions" src="https://img.shields.io/pypi/pyversions/parish?style=flat-square"/>
    </a>
    <a href="https://pypi.org/project/parish" target="_blank">
        <img alt="pypi version" src="https://img.shields.io/pypi/v/parish?style=flat-square&color=blue"/>
    </a>
    <a href="https://github.com/nmichlo/parish/actions?query=workflow%3Atest">
        <img alt="tests status" src="https://img.shields.io/github/workflow/status/nmichlo/parish/test?label=tests&style=flat-square"/>
    </a>
    <a href="https://codecov.io/gh/nmichlo/parish/">
        <img alt="code coverage" src="https://img.shields.io/codecov/c/gh/nmichlo/parish?token=86IZK3J038&style=flat-square"/>
    </a>
</p>

<p align="center">
    <p align="center">
        <a href="https://github.com/nmichlo/parish/issues/new/choose">Contributions</a> are welcome!
    </p>
</p>

----------------------

## Table Of Contents

- [Overview](#overview)
- [Roadmap](#roadmap)

----------------------

## Overview

Minimal reinforcement learning framework.

Get started with `parish` by installing it with $`pip install parish` or cloning this repository.

## Roadmap

This project is being built while I learn about reinforcement
learning, the roadmap for the project includes:
- Common RL Algorithms (Easily Comparable)
- Common RL Performance Tricks (Separate from Algorithms)
- Common RL Models
- Flexible/Modular Components
- Multiprocessing
- Distributed Computing
pseudoprimes 2022.5.1
Test and find prime numbers
Requires Python: >=3.8
Keywords: prime,primes,pseudoprimes,primality

# Pseudoprimes

Test and find prime numbers.

[![CI](https://github.com/oittaa/pseudoprimes-py/actions/workflows/main.yml/badge.svg)](https://github.com/oittaa/pseudoprimes-py/actions/workflows/main.yml)
[![codecov](https://codecov.io/gh/oittaa/pseudoprimes-py/branch/main/graph/badge.svg?token=CDOIHDYMUR)](https://codecov.io/gh/oittaa/pseudoprimes-py)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

```python
>>> import pseudoprimes
>>> pseudoprimes.is_prime(11)
True
>>> pseudoprimes.is_prime(1022117)  # 1009*1013
False
>>> pseudoprimes.get_prime(128)  # 128-bit prime
181872728983755108091298489166590324849
>>> pseudoprimes.next_prime(3)
5
>>> pseudoprimes.prev_prime(100)
97
```

## Installation

```bash
pip install pseudoprimes
```

## Probabilistic or deterministic?

The used Miller–Rabin primality test[^1] is deterministic until ~3.3 \* 10^24[^2] (or ~1.37 \* 2^81). Numbers larger than that are tested using a probabilistic Baillie–PSW primality test[^3]. While there are no known composite numbers that pass the test, it's believed they exist.

[^1]: https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test#Deterministic_variants
[^2]: The exact number is 3,317,044,064,679,887,385,961,981 https://oeis.org/A014233
[^3]: https://en.wikipedia.org/wiki/Baillie%E2%80%93PSW_primality_test
atmega 0.0.2
Provides an API for atmega devices (memory manipulation)
Keywords: API,development,FTDI,RS232,USB,Serial

# ATMEGA API
Provides an API for ATMEGA microcontroller through an FTDI USB device.  
**Installation:** `pip install atmega`  
**Note:** On linux the user should be in the group `dialout` and `uucp` 

# Local installation

## Requirements
**Install dependencies** with pip: `pip install -r requirements.txt`  
**Install the library** with pip: `pip install .`  
**Run tests:** `python setup.py test`

# Usage

```python
import logging
from atmega.ram import RAM

# Set global logging level
logging.basicConfig(level=logging.INFO)

# Create device ram object
dev = RAM()

# Reset ram to 0x11
dev.reset(0x11)

# Set high baudrate
dev.change_baudrate(1000000)

# Dump ram into file
dev.dump_to_file("dump.txt")

# Close device
dev.close()
```
ml-clerk 1.0.0
Module to record/document your model changes

### What is ml-clerk?
ML-clerk, at its core, is a lightweight logging library that helps you keep track of performance, model parameters, timing (or whatever) throughout your data science experiment. 

### I'm a data scientist. Why should I care?
As a data scientist you're constantly running new experiments. As you run new experiments, you are recording your results (if not, you should be recording your results). ML clerk does exactly that. It reduces the inertia by automatically recording the results of your experiment, so you don't have to worry about keeping track.

### Ok now that I'm convinced, where does model-clerk log stuff?
ML clerk has two places where it tracks things - excel or google sheets. Excel setup is easier, so start with that. For google sheets usage, see the link below.

### Train a model - for demo purposes only
```python
# Imports
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

dataset = load_iris()

# Define data
X = dataset['data']
y = dataset['target']

# Train the model
logistic_regression_classifier = LogisticRegression()
logistic_regression_classifier.fit(X, y)

predictions = logistic_regression_classifier.predict(X)
probabilities = logistic_regression_classifier.predict_proba(X)
```

### Excel Usage
```python
# Record artifacts to excel
from ml_clerk import Clerk

# Set up the clerk with excel mode
clerk = Clerk(excel_mode=True)

# file_path refers to the excel workbook you want to record in, and the sheet name referes to the sheet
clerk.set_up(filepath='hello_world.xlsx', sheet_name='Sheet1')

# Record all the artifacts in one go
clerk.record(predictions=predictions, probabilities=probabilities, model_parameters=logistic_regression_classifier.get_params())
```

Figure out google sheets permissions prior to using the google sheets mode. Follow directions below if you need any help.
### Google Sheets Usage
```python
# Record artifacts to excel
from ml_clerk import Clerk

# Set up the clerk with google sheets mode
clerk = Clerk(google_sheets_mode=True)

# file_path refers to the excel workbook you want to record in, and the sheet name referes to the sheet
clerk.set_up(file_path='#your google sheets url', sheet_name='#your sheet name')

# Record all the artifacts in one go
clerk.record(predictions=predictions, probabilities=probabilities, model_parameters=logistic_regression_classifier.get_params())
```

### Google sheets permissions
1. Follow this link for how to permission your Google account - https://erikrood.com/Posts/py_gsheets.html
2. Make sure you enable the Google sheets API under the project in Google console. 
3. Update the `.env` with the path to token.json credentials file.
superr 0.0.1
Simplyfied Math Library.
Keywords: python,math,cmath,eazy,easy,better


This is a simply library that makes the Math library easy to use.
We also added more functions to this. 

The functions include:
add(x, y)
sub(x, y) - subtract
mul(x, y) - multiply
div(x, y) - divide
power(x, y) - x^y
intdiv(x, y) - x // y
modl(x, y) - x % y
value(x, y) - < or >
acos(x) - acos
acosh(x) - acosh
asin(x) - asin
asinh(x) - asinh
atan(x) - atan
atan2(x) - atan2
ceil(x) - ceiling functions
comb(x) - comb
copsig(x) - copysign
cos(x) - cosin  
cosh(x) - cosh
deg(x) - degrees
dist(x, y) - distance
erf(x) - error func 
exp(x) - exp
expm(x) - expm1
abs(x) - abosult value
fact(x) - factorial
flr(x) - floor func
fmod(x) - fmod
frexp(x) - frexp
gsum(x) - gsum
gamma(x) - gamma
gcd(x, y) - gre. com. div.
hypot(x) - hypot
icl(c, x) - isclose
ifin(x) - isfinite
iinf(x) - isInfinite
inan(x) - isNaN
flsqrt(x) - floorSqrt
log(x) - logarithms
log1(x) - log1p
log2(x) - log2
radc(x) - convert to radians
sin(x)  - sin
sinh(x) - sinh
sqrt(x) - sqrt
tan(x) - tan
tanh(x) - tanh
trunc(x) - trunc
e() - euler number
pi() - 3.14.....
tau() - number print