Metadata-Version: 2.1
Name: thomas-core
Version: 0.1.1
Summary: Thomas, a library for working with Bayesian Networks.
Home-page: https://github.com/mellesies/thomas-core
Author: Melle Sieswerda
Author-email: m.sieswerda@iknl.nl
License: UNKNOWN
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        # Thomas
        Very simple (almost naive ;-) bayesian network implementation.
        
        Contains examples (`thomas.core.examples`) from the book "Probabilistic
        Graphical Models: Principles and Techniques" from Koller and Friedman ([PGM
        Stanford](http://pgm.stanford.edu)) and from the lecture by [Adnan
        Darwiche](http://web.cs.ucla.edu/~darwiche/) on YouTube: * [6a. Inference by
        Variable Elimination I (Chapter
        6)](https://www.youtube.com/watch?v=7oRReD_ayWo). * [6b. Inference by Variable
        Elimination II (Chapter 6)](https://www.youtube.com/watch?v=QSSmx1ndUvg).
        
        ## Installation
        
        ### Regular installation
        To install the latest version from **PyPI**:
        
        ```bash
            pip install thomas-core
        ```
        
        Install from PyyPI TEST:
        
        ```bash
            pip install -i https://test.pypi.org/simple/ --extra-index-url=https://pypi.python.org/simple thomas-core
            pip install -i https://test.pypi.org/simple/ --extra-index-url=https://pypi.python.org/simple thomas-jupyter-widget
        ```
        
        
        To install the latest version from **source**:
        
        ```bash
            pip install git+https://github.com/mellesies/thomas-core
        ```
        
        ### Development installation
        To do a development install:
        
        ```bash
            git clone https://github.com/mellesies/thomas-core
            cd thomas-core
            pip install -e .
        ```
        
        ### Additional packages
        If you're using JupyterLab, I'd recommend also installing the Widget that can display Bayesian Networks.
        
        ## Docker
        A Docker image is available should you just want to try things out.The following command will start a JupyterLab server, listening on [http://localhost:8888](http://localhost:8888).
        
        ```bash
            docker run --rm -it -p 8888:8888 mellesies/thomas-core
        ```
        
        
        ## Usage
        To get started with querying a network, try the following:
        ```python
        from thomas.core import examples
        
        # Load an example network
        Gs = examples.get_student_network()
        
        # This should output the prior probability of random variable 'S' (SAT score).
        print(Gs.P('S'))
        print()
        
        # Expected output:
        # P(S)
        # S
        # s0    0.725
        # s1    0.275
        # dtype: float64
        
        # Query for the conditional probability of S given the student is intelligent.
        print(Gs.P('S|I=i1'))
        
        # Expected output:
        # P(S)
        # S
        # s0    0.2
        # s1    0.8
        # dtype: float64
        ```
        
        Alternatively, you can have a go at the example notebooks through [Binder](https://mybinder.org):
        * [notebooks/1. Factors.ipynb](https://mybinder.org/v2/gh/mellesies/thomas-core/master?filepath=notebooks%2F1.%20Factors.ipynb)
        * [notebooks/2. Bags of factors.ipynb](https://mybinder.org/v2/gh/mellesies/thomas-core/master?filepath=notebooks%2F2.%20Bags%20of%20factors.ipynb)
        * [notebooks/3. Conditional probability tables.ipynb](https://mybinder.org/v2/gh/mellesies/thomas-core/master?filepath=notebooks%2F3.%20Conditional%20probability%20tables.ipynb)
        * [notebooks/4. Bayesian Networks.ipynb](https://mybinder.org/v2/gh/mellesies/thomas-core/master?filepath=notebooks%2F4.%20Bayesian%20Networks.ipynb)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >= 3.6
Description-Content-Type: text/markdown
