Metadata-Version: 2.1
Name: qamlz
Version: 0.2.21
Summary: Binary Classifier trained with D-Wave's Quantum Annealers.
Home-page: https://github.com/tcoulvert/qaml-z
Author: Thomas Sievert
Author-email: tcsievert@gmail.com
License: UNKNOWN
Description: # QAML-Z
        This is a supervised ML algorithm used to train a Binary Classifier on D-Wave's Quantum Annealers. The library has been set up to be compatible with Scikit-Learn's data representation. The algortihm is intended to be generalizable to any Binary ML problem.
        
        In order to run the program you'll need D-Wave credentials, these can be obtained at https://cloud.dwavesys.com/leap/signup/. You'll need a github account in order to sign up. This account will give you the "endpoint_url" and "account_token" referenced below.
        
        ## Installation
        Run the following to install:
        ```bash
        $ pip install qamlz
        ```
        
        ## Contributors
        Special thanks to everyone who helped me develop this module
        - My PI and Grad student:
            - Javier Duarte and Raghav Kansal (University of California San Diego, La Jolla, CA 92093, USA)
        - All of QMLQCF, with special mentions of:
            - Jean-Roch (California Institute of Technology, Pasadena, CA 91125, USA)
            - Daniel Lidar (University of Southern California, Los Angeles, CA 90007, USA)
            - Gabriel Perdue (Fermi National Accelerator Laboratory, Batavia, IL 60510, USA)
        - Author of the original QAML-Z code:
            - Alexander Zlokapa (Massachusetts Institute of Technology, Cambridge, MA 02139, USA)
        - Mentoring for code practices:
            - Otto Sievert (GoPro, Inc.)
        
        ## Usage
        ```python
        import qamlz
        
        # Generate the Environment (Data) for the Model
        env = qamlz.TrainEnv(X_train, y_train, endpoint_url, account_token)
        
        # Generate the Config (Hyperparameters) for the Model
        config = qamlz.ModelConfig()
        
        # Create the Model and begin training
        model = qamlz.Model(config, env)
        model.train()
        ```
        
        ## Developing QAML-Z
        To install qamlz, along with the tools you need to develop and run tests, run the following in your virtualenv:
        ```bash
        $ pip install -e .[dev]
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: dev
