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It is compatible with scikit-learn_.\n\n\nAlgorithms supported:\n\n- Sliced Inverse Regression (SIR) [1]_\n- Sliced Average Variance Estimation (SAVE) [2]_\n\nDocumentation / Website: https://joshloyal.github.io/sliced/\n\n\nExample\n-------\nExample that shows how to learn a one dimensional subspace from a dataset with ten features:\n\n.. code-block:: python\n\n   from sliced.datasets import make_cubic\n   from sliced import SlicedInverseRegression\n\n   # load the 10-dimensional dataset\n   X, y = make_cubic(random_state=123)\n\n   # Set the options for SIR\n   sir = SlicedInverseRegression(n_directions=1)\n\n   # fit the model\n   sir.fit(X, y)\n\n   # transform into the new subspace\n   X_sir = sir.transform(X)\n\n\nInstallation\n------------\n\nDependencies\n------------\nsliced requires:\n\n- Python (\u003e= 2.7 or \u003e= 3.4)\n- NumPy (\u003e= 1.8.2)\n- SciPy (\u003e= 0.13.3)\n- Scikit-learn (\u003e=0.17)\n\nAdditionally, to run examples, you need matplotlib(\u003e=2.0.0).\n\nInstallation\n------------\nYou need a working installation of numpy and scipy to install sliced. If you have a working installation of numpy and scipy, the easiest way to install sliced is using ``pip``::\n\n    pip install -U sliced\n\nIf you prefer, you can clone the repository and run the setup.py file. Use the following commands to get the copy from GitHub and install all the dependencies::\n\n    git clone https://github.com/joshloyal/sliced.git\n    cd sliced\n    pip install .\n\nOr install using pip and GitHub::\n\n    pip install -U git+https://github.com/joshloyal/sliced.git\n\nTesting\n-------\nAfter installation, you can use pytest to run the test suite via setup.py::\n\n    python setup.py test\n\nReferences:\n-----------\n.. [1] : Li, K C. (1991)\n        \"Sliced Inverse Regression for Dimension Reduction (with discussion)\",\n        Journal of the American Statistical Association, 86, 316-342.\n.. [2] : Shao, Y, Cook, RD and Weisberg, S (2007).\n         \"Marginal Tests with Sliced Average Variance Estimation\",\n         Biometrika, 94, 285-296.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshloyal%2Fsliced","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoshloyal%2Fsliced","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshloyal%2Fsliced/lists"}