{ "info": { "author": "QuantumCore", "author_email": "info-dev@qcore.co.jp", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: Apache Software License", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: Implementation :: PyPy" ], "description": "\n![Logo](./pics/logo.png)\n\n--------------------------------------------------------------------------------\n\nPyQCore is a client in Python to access our high speed reservoir computing engine, WebQore. \n\nWe are in an early-release beta. Expect some adventures and rough edges.\n\n### Update \n2019.06.13 Release 0.2.1: Add `sofmax_top` option to `classification_predict` to show the softmax values with predicted classes.\n2019.03.08 Release 0.2.0: Add `regression_train`, `regression_test`, `regression_predict`. \n\n- [More about PyQCore](#more-about-pyqcore)\n - [About QuantumCore Engine](#about-quantumcore-engine)\n- [Installation](#installation)\n- [Getting Started](#getting-started)\n- [Communication](#communication)\n- [License](#license)\n\n\n\n## More about PyQCore\n\nAt a granular level, PyQcore is a library that consists of the following components:\n\n| Component | Description |\n| ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |\n| **pyqcore.client.SimpleQCoreClient** | a client class that makes model on WebQore Engine |\n| **pyqcore.examples.jpvow.load_jpvow** | a function that loads sample data from [UCI repository](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels) and return as sklearn format |\n\nThis library is enable to use the WebQore Engine in your codes. Also you need get license code from QuantumCore website to use this library([QuantumCore Inc.](https://www.qcore.co.jp) ).\n\n### About QuantumCore Engine\n\nWebQore Engine is developed by [QuantumCore Inc.](https://www.qcore.co.jp) This new model is 4Kx faster and lighter than previous LSTM or RNN model without GPU. Also you do not have to adjust parameters anymore. \nThe Engine you use with this library is currently running on t2.micro on AWS ([check out the speck here](https://aws.amazon.com/ec2/instance-types/)). \n The company also plans to impliment this algorithm on small edge computer.\n\n\n\n## Installation \nThis library requires **Python > 3.5**. \n\nInstalling PyQcore is not difficult. Just run the command below:\n\n```\npip install pyqcore\n```\nOr clone this repository and run:\n\n```\npython setup.py install\n```\n\n\n## Getting Started\n\nRefer our [sample scrpts](./docs/sample.py) or [tutorial notebook](./docs/tutorial1.ipynb). \nMake sure that you need get license code from [QuantumCore Inc.](https://www.qcore.co.jp)\n\n## Communication\n* GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.\n* Mailing List: please send any issue at [info-dev@qcore.co.jp](mailto:info-dev@qcore.co.jp)\n\n\n## License\n\nCopyright (c) 2019 QuantumCore Inc.\n\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/qcore-info/pyqcore", "keywords": "", "license": "Apache License 2.0", "maintainer": "", "maintainer_email": "", "name": "pyqcore", "package_url": "https://pypi.org/project/pyqcore/", "platform": "", 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