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        "author": "Amartya Sanyal",
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        "description": "========\nmatSHEEP\n========\n\nThis library is a programmatic interface in python to generate a circuit for the bigger and more useful SHEEP library.\n\nThe library has a few data types :\n\n* variables - A single bit (Could also be used as a normal scalar)\n  \n* enc_vec - One dimensional bit vector (Could be used a one dimensional vector of any data type)\n\n* enc_mat - Two dimensional bit matrix (Could be used a one dimensional vector of any data type)\n\n* enc_tensor3 - Three dimensional bit tensor.\n\n\nTo create a circuit, the basic class to inherit is ``mini_mod`` in ``mathsheep.interactions``. To add more components, you can use ``self.add(component)`` inside the ``create`` function as shown below.::\n\n  class oneb_adder(mini_mod):\n       def __init__(self, name, inputs, outputs, nb=None,\n                       randomize_temps=1, carry=True):\n\t    mini_mod.__init__(self, name, inputs, outputs)\n            self.create(...)\n\n       def create(self, ...):\n    \t     self.add(..)\n\n   \nTwo types of components can be added.\n\n* Assignments (``from matSHEEP.interactions``)\n\n   - mono_assign\n\n     + alias\n     + negate\n   \n   - bi_assign\n   \n     + xor\n     + and\n     + or\n     + constand\n   \n   - tri_assign\n     \n     + mux\n\n* Other mini_mods\n   \nThere are a few predefined mini_mods. They can be found in\n\n* ``matSHEEP.reusable_modules``\n   - oneb_adder - Add two bits\n   - nb_adder  - Adders x and y with incoming carrt where input is ``[cin x y]``\n   - nb_adder_xy - Adds x and y with  ``input = (x, y)``\n   - compare_cp - Compares ciphertext with plaintext with ``input = (c,p)``\n\n* ``matSHEEP.functions``\n   - reduce_add - Counts the number of ones in a bit vector.\n\n* ``matSHEEP.nn_layer``\n   - sign_fn\n   - linear_layer_1d - Inner Product of a weight vector with encrypted bit vector  followed by a sign function.\n   - linear_layer - Inner Product of a weight matrix with an encrypted bit vector followed by a sign function.\n   - conv_layer - A convolution Layer. (Look at examples)\n\n* ``matSHEEP.vector_ops``\n   - vec_mono_op_cond - Takes a plaintext ``cond`` vector, a plaintext tuple ``ass_types`` containing only ``alias`` and ``negate`` as values and an encrypted bit vector ``input``. It outputs an encrypted bit vector where the ith position has the ``ass_types[cond[idx]]`` operation applied on  ``input[idx]``.\n   - Similar operation for matrix and tensor.\n\nYou can also visualize the circuits you create. ``test.sheep`` is a circuit file.::\n\n    import sys\n    import matSHEEP.create_graph as cg\n    complete_node = cg.get_circuit_graph('./test.sheep')\n    ng = cg.networkx_graph(complete_node)\n    ng.draw()\n\nAnd you can get\n\n.. image:: https://raw.githubusercontent.com/amartya18x/matSHEEP/master/images/LL4.png\n   :height: 30px\n\n\ngFor more high level operations and results using layers of Neural Networks visit this markdown\n",
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