Metadata-Version: 2.1
Name: robustgp
Version: 2.0
Summary: Robust Gaussian process regression based on iterative trimming.
Home-page: https://github.com/syrte/robustgp/
Author: Zhaozhou Li
Author-email: styr.py@gmail.com
License: UNKNOWN
Description: # robustgp
        
        Robust Gaussian Process Regression Based on Iterative Trimming
        
        Zhao-Zhou Li, Lu Li, Zhengyi Shao, 2020, https://arxiv.org/abs/2011.11057
        
        First application
        - Modeling Unresolved Binaries of Open Clusters in the Color-Magnitude Diagram. I. Method and Application of NGC 3532
          https://ui.adsabs.harvard.edu/abs/2020ApJ...901...49L/
        
        ## Quick start
        You can start with examples in [this notebook](https://github.com/syrte/robustgp/blob/master/notebook/Example_Neal_Dataset.ipynb).
        
        ## Dependency
        [GPy](https://github.com/SheffieldML/GPy/)
        
        ## Install
        
        Install the dependency first
        ```
        pip install GPy
        ```
        
        Assuming you want to put the code at certain directory, say `~/pythonlib`
        ```bash
        cd ~/pythonlib
        wget https://raw.githubusercontent.com/syrte/robustgp/master/robustgp/robustgp.py
        ```
        
        Then you can import `ITGP` as following,
        ```python
        import sys
        sys.path.append('~/pythonlib')
        from robustgp import ITGP
        ```
        
        Or add this in your `.bashrc` once for all
        ```bash
        export PYTHONPATH="$HOME/pythonlib:$PYTHONPATH"
        ```
        
        I will write a `setup.py` in the future for easier installation.
        
        
        ## Usage
        
        ```python
        from robustgp import ITGP
        
        # train ITGP
        res = ITGP(X, Y, alpha1=0.5, alpha2=0.975, nsh=2, ncc=2, nrw=1)
        gp, consistency = res.gp, res.consistency
        
        # make prediction
        y_avg, y_var = gp.predict(x_new)
        y_var *= consistency
        ```
        See [this notebook](https://github.com/syrte/robustgp/blob/master/notebook/Example_Neal_Dataset.ipynb) for a complete example.
        
        Here `gp` is a `GPy.core.GP` object, whose usage is illustrated [here](https://nbviewer.jupyter.org/github/SheffieldML/notebook/blob/master/GPy/basic_gp.ipynb).
        
        ## License 
        The MIT License
        
Keywords: Gaussian process,regression,robust statistics,outlier detection
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
