semvec/pypi-packages huggingface dataset:
nhlang 0.1.1
A simple interpreter for NH Language
License: MIT
Requires Python: >=3.7

# NH Language

**NH Language** là một ngôn ngữ lập trình đơn giản, do Hwi GD tạo ra.  
Được thiết kế để dễ học, dễ test các biến, vòng lặp, điều kiện, và in ra màn hình.

## Cài đặt

Cài trực tiếp từ PyPI (sau khi upload):

```bash
pip install nhlang
isilon-sdk 0.7.0
Python language bindings for managing OneFS clusters.
License: MIT
Requires Python: >=2.7
Keywords: Swagger, Isilon SDK, OneFS, PowerScale

About
-----

This package is part of the Isilon SDK. It includes language bindings
for easier programmatic access to the OneFS API for cluster
configuration (on your cluster this is the REST API made up of all the
URIs underneath ``https://[cluster]:8080/platform/*``, also called the
"Platform API" or "PAPI"). The SDK also includes language bindings for
the OneFS RAN (i.e. RESTful Access to Namespace) interface, which
provides access to the OneFS filesystem namespace.

Installation
------------

``pip install isilon_sdk``

Example program
---------------

Please select the subpackage as applicable to the OneFS version of your
cluster by referring to the below table:


OneFS Version and respective package names are as:

============= ==================
OneFS Release Package Name      
9.5.0.0       isilon_sdk.v9_5_0 
9.6.0.0       isilon_sdk.v9_6_0 
9.7.0.0       isilon_sdk.v9_7_0 
9.8.0.0       isilon_sdk.v9_8_0 
9.9.0.0       isilon_sdk.v9_9_0 
9.10.0.0      isilon_sdk.v9_10_0
9.11.0.0      isilon_sdk.v9_11_0
9.12.0.0      isilon_sdk.v9_12_0
============= ==================

Here’s an example of using the Python PAPI bindings to retrieve a list
of NFS exports from your clusters

::

   from __future__ import print_function

   from pprint import pprint
   import time
   import urllib3

   import isilon_sdk.v9_12_0
   from isilon_sdk.v9_12_0.rest import ApiException

   urllib3.disable_warnings()

   # configure cluster connection: basicAuth
   configuration = isilon_sdk.v9_12_0.Configuration()
   configuration.host = 'https://<NODE_IP>:8080'
   configuration.username = 'root'
   configuration.password = 'a'
   configuration.verify_ssl = False

   # create an instance of the API class
   api_client = isilon_sdk.v9_12_0.ApiClient(configuration)
   api_instance = isilon_sdk.v9_12_0.ProtocolsApi(api_client)

   # get all exports
   sort = 'description'
   limit = 50
   order = 'ASC'
   try:
       api_response = api_instance.list_nfs_exports(sort=sort, limit=limit, dir=order)
       pprint(api_response)
   except ApiException as e:
       print("Exception when calling ProtocolsApi->list_nfs_exports: %s\n" % e)

More Info
---------------
See the Github repo for more information:
https://github.com/isilon/isilon_sdk
myrlkit 0.1.2
A tiny, teaching-focused reinforcement learning kit (Q-learning, SARSA, Bandits) with toy environments.
License: MIT
Requires Python: >=3.9

# myrlkit

**A minimal, lightweight reinforcement learning toolkit** — built for quick experimentation, small projects, and clear, dependency-light code.  
Includes core RL algorithms and simple toy environments with ready-to-run examples and matplotlib visualizations.

## ✨ Features
- **Algorithms**: Q-learning, SARSA, and Epsilon-Greedy Bandit.
- **Environments**: GridWorld and K-armed Bandit.
- **Visualization**: Built-in learning curves and policy renderings.
- **No heavy frameworks** — just Python + NumPy + Matplotlib.
- **Readable & well-documented** — easy to extend and adapt.

## Install

```bash
pip install myrlkit
```

> If installing from source:
```bash
pip install -e .
```

## Quickstart

### Q-learning on GridWorld

```python
import numpy as np
from myrlkit.agents import QLearningAgent
from myrlkit.envs import GridWorld

env = GridWorld(width=5, height=5, start=(0,0), goal=(4,4), obstacles=[(1,1), (1,2), (2,1)])
agent = QLearningAgent(state_size=env.n_states, action_size=env.n_actions, alpha=0.5, gamma=0.99, epsilon=0.1)

episodes = 300
rewards = []
for _ in range(episodes):
    s = env.reset()
    done = False
    total = 0.0
    while not done:
        a = agent.choose_action(s)
        ns, r, done, _ = env.step(a)
        agent.update(s, a, r, ns, done)
        s = ns
        total += r
    rewards.append(total)

policy = env.render_policy(agent.q_table)
print(policy)
```

### SARSA on GridWorld

```python
from myrlkit.agents import SARSAAgent
from myrlkit.envs import GridWorld

env = GridWorld(width=4, height=4, start=(0,0), goal=(3,3))
agent = SARSAAgent(state_size=env.n_states, action_size=env.n_actions, alpha=0.5, gamma=0.99, epsilon=0.1)
```

### Epsilon-Greedy Bandit

```python
from myrlkit.agents import EpsilonGreedyBandit
from myrlkit.envs import KArmedBandit

env = KArmedBandit(k=10, means=[0.0]*10, std=1.0, seed=42)
agent = EpsilonGreedyBandit(k=env.k, epsilon=0.1)

rewards = []
for t in range(1000):
    a = agent.select_action()
    r = env.pull(a)
    agent.update(a, r)
    rewards.append(r)
```

## API

- `myrlkit.agents.QLearningAgent`
- `myrlkit.agents.SARSAAgent`
- `myrlkit.agents.EpsilonGreedyBandit`
- `myrlkit.envs.GridWorld`
- `myrlkit.envs.KArmedBandit`

## Examples

See `examples/` for runnable scripts (learning curves, policy printouts).

## License

MIT



---
## Author
Nihar Lohar  
LinkedIn: https://www.linkedin.com/in/nihar-lohar-20234a287/
github: https://github.com/Nihar3453/passport_ocr