{ "info": { "author": "Mandar Gogate", "author_email": "contact@mandargogate.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 1 - Planning", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7" ], "description": "# Overview\n\nRandom Neural Network Simulator implemented in Python.\n\n[![PyPI Version](https://img.shields.io/pypi/v/rnnsim.svg)](https://pypi.org/project/rnnsim)\n[![PyPI License](https://img.shields.io/pypi/l/rnnsim.svg)](https://pypi.org/project/rnnsim)\n\n# Setup\n\n## Requirements\n\n* Python 3.6+\n* NumPy\n* Sklearn \n\n## Installation\n\nInstall this library directly into an activated virtual environment:\n\n```bash\n$ pip install rnnsim\n```\n\nor add it to your [Poetry](https://poetry.eustace.io/) project:\n\n```bash\n$ poetry add rnnsim\n```\n\n# Usage\n\nAfter installation, the package can either be used as:\n\n```python\n\nfrom rnnsim.model import SequentialRNN\n\nsequential_model = SequentialRNN([2, 2, 1])\nsequential_model.compile()\nsequential_model.fit(train_data=(X_train, y_train), epochs=50, metrics=\"acc\")\nprint(sequential_model.score((X_test, y_test)))\n```\n\nor \n\n```python\nfrom rnnsim.RNN import RNN\n\n# define model connections\nconn_plus = {\n 1: [3, 4], 2: [3, 4],\n 3: [5], 4: [5], 5: []}\nconn_minus = {\n 1: [3, 4], 2: [3, 4],\n 3: [5], 4: [5], 5: []}\nmodel = RNN(n_total=5, input_neurons=2, output_neurons=1, conn_plus=conn_plus, conn_minus=conn_minus)\nmodel.fit(epochs=N_Iterations, train_data=(X, Y))\n```\n\n\nReferences\n\n1. E. Gelenbe, Random neural networks with negative and positive signals and product\nform solution,\" Neural Computation, vol. 1, no. 4, pp. 502-511, 1989.\n2. E. Gelenbe, Stability of the random neural network model,\" Neural Computation, vol.\n2, no. 2, pp. 239-247, 1990.", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://pypi.org/project/rnnsim", "keywords": "", "license": "MIT", "maintainer": "Mandar Gogate", "maintainer_email": "contact@mandargogate.com", "name": "rnnsim", "package_url": "https://pypi.org/project/rnnsim/", "platform": "", "project_url": "https://pypi.org/project/rnnsim/", "project_urls": { "Documentation": "https://rnnsim.readthedocs.io", "Homepage": "https://pypi.org/project/rnnsim", "Repository": "https://github.com/MandarGogate/RNNSim" }, "release_url": "https://pypi.org/project/rnnsim/0.1/", "requires_dist": [ "numpy (>=1.16.1,<2.0.0)" ], "requires_python": ">=3.6,<4.0", "summary": "Random Neural Network Simulator implemented in Python.", "version": "0.1" }, "last_serial": 5827594, "releases": { "0.1": [ { "comment_text": "", "digests": { "md5": "baa73e3aefc9b214893cecee308e0ea6", "sha256": "7cf0cc53cd4c89d28f0ec6e18df5d5b08b466994ddf1e22adc8db91b73c632a2" }, "downloads": -1, "filename": "rnnsim-0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "baa73e3aefc9b214893cecee308e0ea6", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6,<4.0", "size": 7735, "upload_time": "2019-09-13T20:17:55", "url": "https://files.pythonhosted.org/packages/89/51/484fd628ae76350f555648c1d27e4b329f55f93815e28f44530d008276cb/rnnsim-0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "04566ea8b6bfd2c7312ee8b425b932c7", "sha256": "a39263686c8c72e1129536af121f9d7b3fb98f6de7adc7446e605271b051e817" }, "downloads": -1, "filename": "rnnsim-0.1.tar.gz", "has_sig": false, "md5_digest": "04566ea8b6bfd2c7312ee8b425b932c7", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6,<4.0", "size": 7161, "upload_time": "2019-09-13T20:17:57", "url": "https://files.pythonhosted.org/packages/d7/c1/6ccd1bdde27b2e63ee4f5167ac00f2297bb37c63f479980ff6603a416d9e/rnnsim-0.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "baa73e3aefc9b214893cecee308e0ea6", "sha256": "7cf0cc53cd4c89d28f0ec6e18df5d5b08b466994ddf1e22adc8db91b73c632a2" }, "downloads": -1, "filename": "rnnsim-0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "baa73e3aefc9b214893cecee308e0ea6", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6,<4.0", "size": 7735, "upload_time": "2019-09-13T20:17:55", "url": "https://files.pythonhosted.org/packages/89/51/484fd628ae76350f555648c1d27e4b329f55f93815e28f44530d008276cb/rnnsim-0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "04566ea8b6bfd2c7312ee8b425b932c7", "sha256": "a39263686c8c72e1129536af121f9d7b3fb98f6de7adc7446e605271b051e817" }, "downloads": -1, "filename": "rnnsim-0.1.tar.gz", "has_sig": false, "md5_digest": "04566ea8b6bfd2c7312ee8b425b932c7", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6,<4.0", "size": 7161, "upload_time": "2019-09-13T20:17:57", "url": "https://files.pythonhosted.org/packages/d7/c1/6ccd1bdde27b2e63ee4f5167ac00f2297bb37c63f479980ff6603a416d9e/rnnsim-0.1.tar.gz" } ] }