semvec/pypi-packages huggingface dataset:
rainiee-decision-engine 2.1.0
rainiee decision engine api
License: MIT
Requires Python: >=3

rainiee_decision_engine
===============
* easy to use as most of the data returned are json 

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

    pip install rainiee_decision_engine==2.1.0
    from rainiee_decision_engine.decision_engine_client import DecisionEngineClient
    rainiee_client = DecisionEngineClient(username,password)

Upgrade
---------------

    pip install rainiee_decision_engine==2.0.0 --upgrade

Quick Start
--------------

algorithm 
  
        1: algorithm_solve_vanilla_mvo
              symbol_list = ['300370.sz','300102.sz','300345.sz']
              rainiee_client.algorithm_solve_vanilla_mvo(symbol_list,2350,2360)
        2: algorithm_solve_maximize_sharpe
              symbol_list = ['300370.sz','300102.sz','300345.sz']
              rainiee_client.algorithm_solve_maximize_sharpe(symbol_list,2350,2360)
        3: algorithm_solve_vanilla_mvo_realtime
              symbol_list = ['300370.sz','300102.sz','300345.sz']
              rainiee_client.algorithm_solve_vanilla_mvo_realtime(symbol_list,2350,2360)
        4: monitoring_baseline
              portf = [{'percentage': 1.0, 'symbol': '300547.sz'}, {'percentage': 0.5, 'symbol': '300647.sz'}]
              hold_index = 2530
              hold_period = 5
              rainiee_client.monitoring_baseline(portf,hold_index,hold_period)
        5: cn_stockstats_returns_matrix
              symbol_list =['300370.sz','300102.sz','300345.sz','300341.sz','300806.sz','300249.sz','300727.sz','300373.sz','300808.sz',
              '300474.sz','300805.sz','300803.sz']
              rainiee_client.cn_stockstats_returns_matrix(symbol_list,2581,2581)

        6: trade_execute_order
              rainiee_client.trade_execute_order('300647.sz',2500,'buy',15)
PMKL-py 0.1.1
Tessellated Kernels SVM
License: MIT
Keywords: Kernel learning,SVM,Multiple Kernel Learning

Tessellated Kernel Learning (TKL) is a free machine learning MATLAB and python toolbox for learning optimal Tessellated Kernel (TK) functions for Support Vector Machine (SVM) classification and regression problems. TKs are a class of kernel functions that are ideal for kernel learning because they admit a linear parameterization (tractability); are dense in the set of all kernels (accuracy); and every member is universal so that the hypothesis space is infinite-dimensional (scalability).

What can I do with TKL?

TKL can be used to

1. Learn optimal TK kernel functions for a given set of inputs x and outputs y.
2. Train a Support Vector Machine (SVM) using the optimal TK kernel function.
3. Outputs may be binary (classification) or real valued (regression).


How to use TKL
If you only want to use TKL-SVM or build a Tessellated kernel, please look at these examples 1 and 2, as well as the basic functions for the kernel and the model construction.

What is Multiple Kernel Learning?
To whom is interested in what MKL is, a good paper is Multiple kernel learning algorithms M GГ¶nen or just read on wikipedia.

How to understand the TKL?
What is TKL and how to use it, you can read one of these articles 1 or 2. And we also have a shortened version on our website

Technical Support
Our goal is to make use of TKL as simple as humanly possible. However, our background is not in coding and sometimes we come up short. If you are having a serious technical issue and neither the help commands nor the manual are helping, and believe there is a bug in the program, please report it to: brendon.colbert@asu.edu or atalitck@asu.edu. If there is a bug, we will add it to the known bug list and do our best to fix it.

Alternatively, if you would like to volunteer for the TKL development team, we would be happy to include you (no compensation - Sorry). Send an email to mpeet@asu.edu

released-08/03/21
graph-scheduling 1.0.3
scheduling algorithm
License: MIT

# graph-scheduling

## Installation
```pip install graph-scheduling```

## Use Case
```
Exam for subjects A, B, C, D, E, F & G are to be scheduled in minimum number of days. Some of the exams cannot be scheduled together.

{'A' : {'B', 'D', 'E'}} implies that A cannot be scheduled with B, D or E.
 
How many days are required to schedule the exams?  
```

## How to use it?
```
A graph must be defined such that every key must be matched with the set of values that it cannot be scheduled together with. 

{'A' : {'B', 'D', 'E'}} implies that A cannot be scheduled with B, D or E
```

## License

© 2021 Roman Regmi

This repository is licensed under the MIT license. 
See LICENSE for details.
StringiPy 0.1.0
Python Functional String Utilities
License: MIT

# stringipy: developer utilities for strings


## What is it?

There are instances that you got a list over the internet, and you want to transform those strings so you can paste them easily in your code? Or you just want to make use this library to perform basic string transformation. **stringipy** got you covered.

**stringipy** is created guided with functional programming paradigms that can easily pipe operations in a single line of code.



## Main Features

* `FluentString` and `FluentStringList` handles the functional string operations
* Current features are `snake_case`, `prefix`, `suffix`, `join`... more to come
* This applies to both string and list of strings



## How to install

Library is available in Python Package Index (PyPI).

```bash
pip install stringipy
```



## Getting Started

Here's an example of how to perform and chain string operations.

```python
data = ["HelloDarkness", "MyOldFriend"]

FluentStringList(data) \
	.snake_case() \
	.prefix("\"") \
    .suffix("\"") \
    .join(", ") \
    .prefix("[") \
    .suffix("]") \
    .print()
```

Output

```
["hello_darkness", "my_old_friend"]
```



## Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

The library does not require much except for `pytest` and should work with the `python 3.x` versions. Feel free to send me a pull request or open an issue to discuss feature requests.

## License

MIT