Metadata-Version: 1.1
Name: OWCK
Version: 1.1.3
Summary: Optimal Weighted Kriging / Gaussian Process
Home-page: https://github.com/Basvanstein/OWCK
Author: Bas van Stein and Hao Wang
Author-email: b.van.stein@liacs.leidenuniv.nl
License: GNUv2
Description: The Optimal Weighted Cluster Kriging/Gaussian Process class
        =======================
        
        This class inherited from GaussianProcess class in sklearn library
        Most of the parameters are contained in sklearn.gaussian_process.
        
        Please check the docstring of Gaussian Process parameters in sklearn.
        Only newly introduced parameters are documented below.
        
        Install Instructions
        ================
        
        Just run the install.py or install directly with pip.
        You need OpenMPI installed to use the parralel options.
        
        Pip::
        
            pip install OWCK
        
        Parameters
        ----------
        n_cluster : int, optional
            The number of clusters, determines the number of the Gaussian Process
            model to build. It is the speed-up factor in OWCK.
        cluster_method : string, optional
            The clustering algorithm used to partition the data set.
            Built-in clustering algorithm are:
                'k-mean', 'GMM', 'fuzzy-c-mean', 'random', 'tree'
                Note that GMM, fuzzy-c-mean are fuzzy clustering algorithms 
                With these algorithms you can set the overlap you desire.
                tree is a regression tree clustering-based approach
        overlap : float, optional
            The percentage of overlap when using a fuzzy cluster method.
            Each cluster will be of the same size.
        is_parallel : boolean, optional
            A boolean switching parallel model fitting on. If it is True, then
            all the underlying Gaussian Process model will be fitted in parallel,
            supported by MPI. Otherwise, all the models will be fitted sequentially.
            
        Attributes
        ----------
        cluster_label : the cluster label of the training set after clustering
        clusterer : the clustering algorithm used.
        models : a list of (fitted) Gaussian Process models built on each cluster.
        
        Usage
        ----------
        Example code::
        
            from OWCK import OWCK
            owck_model = OWCK(cluster_method='tree')
            owck_model.fit(X,y)
            pred_y, var_y = owck_model.predict(x_new)
        
        References
        ----------
        
        .. [SWKBE15] `Bas van Stein, Hao Wang, Wojtek Kowalczyk, Thomas Baeck 
            and Michael Emmerich. Optimally Weighted Cluster Kriging for Big 
            Data Regression. In 14th International Symposium, IDA 2015, pages 
            310-321, 2015`
            http://link.springer.com/chapter/10.1007%2F978-3-319-24465-5_27#
        
Keywords: kriging guassian-process data-mining big-data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
