nlptoolkit-hmm-cy 1.0.6

Hidden Markov Models ============

    Video Lectures
    ============
    
    [<img src="https://github.com/StarlangSoftware/Hmm/blob/master/video1.jpg" width="50%">](https://youtu.be/zHj5mK3jcyk)[<img src="https://github.com/StarlangSoftware/Hmm/blob/master/video2.jpg" width="50%">](https://youtu.be/LM0ld3UKCEs)
    
    For Developers
    ============
    You can also see [Python](https://github.com/starlangsoftware/Hmm-Py), [Java](https://github.com/starlangsoftware/Hmm), [C++](https://github.com/starlangsoftware/Hmm-CPP), [C](https://github.com/starlangsoftware/Hmm-C), [Swift](https://github.com/starlangsoftware/Hmm-Swift), [Js](https://github.com/starlangsoftware/Hmm-Js), [Php](https://github.com/starlangsoftware/Hmm-Php), or [C#](https://github.com/starlangsoftware/Hmm-CS) repository.
    
    ## Requirements
    
    * [Python 3.7 or higher](#python)
    * [Git](#git)
    
    ### Python 
    
    To check if you have a compatible version of Python installed, use the following command:
    
        python -V
        
    You can find the latest version of Python [here](https://www.python.org/downloads/).
    
    ### Git
    
    Install the [latest version of Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git).
    
    ## Pip Install
    
    	pip3 install NlpToolkit-Hmm-Cy
    
    ## Download Code
    
    In order to work on code, create a fork from GitHub page. 
    Use Git for cloning the code to your local or below line for Ubuntu:
    
    	git clone <your-fork-git-link>
    
    A directory called Hmm will be created. Or you can use below link for exploring the code:
    
    	git clone https://github.com/starlangsoftware/Hmm-Py.git
    
    ## Open project with Pycharm IDE
    
    Steps for opening the cloned project:
    
    * Start IDE
    * Select **File | Open** from main menu
    * Choose `Hmm-PY` file
    * Select open as project option
    * Couple of seconds, dependencies will be downloaded. 
    
    Detailed Description
    ============
    
    + [Hmm](#hmm)
    
    ## Hmm
    
    Hmm modelini üretmek için
    
    	Hmm(self, states: set, observations: list, emittedSymbols: list)
    
    
    Viterbi algoritması ile en olası State listesini elde etmek için
    
    	viterbi(self, s: list) -> list