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        "description": "| |Build Status|\n\nMultilayer Perceptron in Python\n-----------------------------------\n\nPython implementation of multilayer perceptron neural network from scratch.\n\n    | Minimal neural network class with regularization using scipy minimize. Contains clear pydoc for learners to better understand each stage in the neural network.\n    | https://github.com/paulokuong/mlperceptron\n\nRequirements\n------------\n\n-  Python 3.4 (tested)\n\nGoal\n----\n\n| To provide an example of a simple MLP for educational purpose.\n\nCode sample\n-----------\n\n| Predicting outcome of AND logic gate:\n\nX = 000, 001, 010, 011, 100, 101, 110, 111\n    y = 0,0,0,0,0,0,1\n\n    Data we want to predict:\n    p = 011, 111, 000, 010, 111\n    Expected results are: 0, 1, 0, 0, 1\n\n.. code:: python\n\n    import numpy as np\n    from mlperceptron.mlperceptron import NeuralNetwork\n\n    X = np.matrix(\n        '0 0 0;0 0 1;0 1 0;0 1 1;1 0 0;1 0 1;1 1 0;1 1 1')\n    y = np.matrix('0;0;0;0;0;0;0;1')\n    n = NeuralNetwork((5,5,))\n\n    g = n.train(X, y, 0.01, show_cost=True)\n    y_pred = n.predict(np.matrix('0 1 1;1 1 1;0 0 0;0 1 0;1 1 1'), g)\n\n    print(y_pred)\n    print(n.accuracy(y_pred, np.matrix('0;1;0;0;1')))\n\n\nContributors\n------------\n\n-  Paulo Kuong (`@pkuong`_)\n\n.. _@pkuong: https://github.com/paulokuong\n\n.. |Build Status| image:: https://travis-ci.org/paulokuong/mlperceptron.svg?branch=master\n.. target: https://travis-ci.org/paulokuong/mlperceptron\n",
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