Metadata-Version: 1.0
Name: kenchi
Version: 0.4.0
Summary: A set of python modules for anomaly detection
Home-page: http://kenchi.readthedocs.io
Author: Kon
Author-email: kon.y.ohr.n@gmail.com
License: MIT
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        kenchi
        ======
        
        This is a set of python modules for anomaly detection.
        
        Requirements
        ------------
        
        -  Python (>=3.5)
        -  matplotlib (>=2.0.2)
        -  numpy (>=1.11.2)
        -  pandas (>=0.20.3)
        -  scipy (>=0.18.1)
        -  scikit-learn (>=0.18.0)
        
        Installation
        ------------
        
        You can install via pip.
        
        ::
        
            pip install kenchi
        
        Usage
        -----
        
        .. code:: python
        
            >>> import numpy as np
            >>> from kenchi.outlier_detection import GaussianOutlierDetector
            >>> train_size   = 1000
            >>> test_size    = 100
            >>> n_outliers   = 10
            >>> n_features   = 10
            >>> rnd          = np.random.RandomState(0)
            >>> mean         = np.zeros(n_features)
            >>> cov          = np.eye(n_features)
            >>> # Generate the training data
            >>> X_train      = rnd.multivariate_normal(
            ...     mean     = mean,
            ...     cov      = cov,
            ...     size     = train_size
            ... )
            >>> # Generate the test data that contains outliers
            >>> X_test       = np.concatenate((
            ...     rnd.multivariate_normal(
            ...         mean = mean,
            ...         cov  = cov,
            ...         size = test_size - n_outliers
            ...     ),
            ...     rnd.uniform(-10.0, 10.0, size=(n_outliers, n_features))
            ... ))
            >>> # Fit the model according to the given training data
            >>> det          = GaussianOutlierDetector().fit(X_train)
            >>> # Detect if a particular sample is an outlier or not
            >>> det.detect(X_test)
            array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                   0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                   0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                   0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
                   1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
        
        License
        -------
        
        The MIT License (MIT)
        
        Copyright (c) 2017 Kon
        
Platform: UNKNOWN
