Metadata-Version: 2.1
Name: NlpToolkit-Hmm
Version: 1.0.6
Summary: Hidden Markov Model Library
Home-page: https://github.com/StarlangSoftware/Hmm-Py
Author: olcaytaner
Author-email: olcay.yildiz@ozyegin.edu.tr
License: UNKNOWN
Description: For Developers
        ============
        
        You can also see [Cython](https://github.com/starlangsoftware/Hmm-Cy), [Java](https://github.com/starlangsoftware/Hmm), [C++](https://github.com/starlangsoftware/Hmm-CPP), [Swift](https://github.com/starlangsoftware/Hmm-Swift),  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
        	
        ## 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
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
