Metadata-Version: 1.1
Name: cobras-ts
Version: 0.0.3
Summary: Semi-supervised time series clustering with COBRAS
Home-page: https://bitbucket.org/toon_vc/cobras_ts/
Author: Toon Van Craenendonck
Author-email: toon.vancraenendonck@cs.kuleuven.be
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: =================================
        COBRAS for time-series clustering
        =================================
        
        Library for semi-supervised time series clustering using pairwise constraints.
        
        
        -----------------
        Installation
        -----------------
        
        This package is available on PyPi::
        
            $ pip install cobras_ts
        
        
        -----------------
        Usage
        -----------------
        
        Examples can also be found in the examples subdirectory.
        
        Running COBRAS_kShape:
        
            .. code-block:: python
        
                import os
                import numpy as np
                from cobras_ts import cobras_kshape
        
                ucr_path = '/path/to/UCR/archive'
                dataset = 'ECG200'
                budget = 100
        
                data = np.loadtxt(os.path.join(ucr_path,dataset,dataset + '_TEST'), delimiter=',')
                series = data[:,1:]
                labels = data[:,0]
        
                clusterer = cobras_kshape.COBRAS_kShape(series, labels, budget)
                clusterings, runtimes, ml, cl = clusterer.cluster()
        
        
        Running COBRAS_DTW:
        
        This uses the dtaidistance package to compute the DTW distance matrix.
        Note that constructing this matrix is typically the most time consuming step, and significant speedups can be achieved
        by using the C implementation in the dtaidistance package.
        
            .. code-block:: python
        
                import os
                import numpy as np
                from cobras_ts import cobras_dtw
                from dtaidistance import dtw
        
                ucr_path = '/path/to/UCR/archive'
                dataset = 'ECG200'
                budget = 100
                alpha = 0.5
                window = 10
        
                data = np.loadtxt(os.path.join(ucr_path,dataset,dataset + '_TEST'), delimiter=',')
                series = data[:,1:]
                labels = data[:,0]
        
                dists = dtw.distance_matrix(series, window=int(0.01 * window * series.shape[1]))
                dists[dists == np.inf] = 0
                dists = dists + dists.T - np.diag(np.diag(dists))
                affinities = np.exp(-dists * alpha)
        
                clusterer = cobras_dtw.COBRAS_DTW(affinities, labels, budget)
                clusterings, runtimes, ml, cl = clusterer.cluster()
        
        
        -----------------
        Dependencies
        -----------------
        
        This package uses Python3, numpy, scikit-learn, kshape and dtaidistance.
        
        -----------------
        Contact
        -----------------
        Toon Van Craenendonck at toon.vancraenendonck@cs.kuleuven.be
        
        -----------------
        License
        -----------------
        
            COBRAS code for semi-supervised time series clustering.
        
            Copyright 2018 KU Leuven, DTAI Research Group
        
            Licensed under the Apache License, Version 2.0 (the "License");
            you may not use this file except in compliance with the License.
            You may obtain a copy of the License at
        
            http://www.apache.org/licenses/LICENSE-2.0
        
            Unless required by applicable law or agreed to in writing, software
            distributed under the License is distributed on an "AS IS" BASIS,
            WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
            See the License for the specific language governing permissions and
            limitations under the License.
Keywords: clustering timeseries semi-supervised pairwise constraints
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
