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        "description": "One-hot multinomial logistic regression\n========================\n\nQuick Start\n-----------\n\nInstallation\n~~~~~~~~~~~~\n\n- To install ``ohmlr`` on your computer using ``pip``, execute\n\n  .. code-block:: sh\n\n     pip install ohmlr\n\n- Test out ``ohmlr`` in Python:\n\n  .. code-block:: python\n\n     import ohmlr\n     import numpy as np\n\n     # create model and generate data\n     n_features = 16\n     n_x_classes = np.random.randint(2, 10, size=n_features)\n     n_y_classes = 8\n     model = ohmlr.ohmlr().random(n_features, n_x_classes, n_y_classes)\n     x, y = model.generate_data(n_samples=1000)\n\n     # fit and score model\n     model.fit(x, y)\n     print(model.score(x, y))\n\n\nLinks\n-----\n\nOnline documentation:\n    http://joepatmckenna.github.io/ohmlr\n\nSource code repository:\n    https://github.com/joepatmckenna/ohmlr\n\nPython package index:\n    https://pypi.python.org/pypi/ohmlr",
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