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            "Development Status :: 5 - Production/Stable",
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            "License :: OSI Approved :: Apache Software License",
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        "description": "playground-data\n====================================\n\nData Generation for Neural Network Playground of Deep Insider.\n\nThis project/package that exists as an aid to the [Nerural Network Playground - Deep Insider][playground page] which was forked from [tensorflow/playground: Deep playground][original page].\n\nOfficial pages\n-------------------------------------------------------------------\n\n- [The python package \"playground-data\" on PyPI for this project is available here][pypi].\n- [The source for this package is available here][src]. [![Build Status](https://travis-ci.org/DeepInsider/playground-data.svg?branch=master)](https://travis-ci.org/DeepInsider/playground-data)\n\nRequirements\n-------------------------------------------------------------------\n\n- Python 2: 2.7+ | Python 3: 3.4, 3.5, 3.6, 3.7+\n- numpy\n- matplotlib\n\nInstall this package using `pip`\n-------------------------------------------------------------------\n\n```bash\npip install playground-data\n```\n\nUsage\n-------------------------------------------------------------------\n\n```python\nfrom __future__ import print_function\n\nprint('Import plygdata package as pg')\n\nimport plygdata as pg\n\n# Or, you can 'import' classes and functions directly like this:\n# from plygdata.datahelper import DatasetType\n# from plygdata.dataset import generate\n```\n\n```python\nprint('Imported \"playground-data\" package version is ...')\n\nprint(pg.__version__)\n```\n\n```python\nprint('Code for plotting sample graph')\n\nimport pprint\npprint.pprint(dir(pg))    # How to find class members\n\npprint.pprint(dir(pg.DatasetType))\n#['ClassifyCircleData',\n# 'ClassifySpiralData',\n# 'ClassifyTwoGaussData',\n# 'ClassifyXORData',\n# 'RegressGaussian',\n# 'RegressPlane',\n# ...]\n\nfig, ax = pg.plot_sample(pg.DatasetType.ClassifyCircleData)\n# # uncomment if a graph is not shown\n# import matplotlib.pyplot as plt\n# plt.show()\n```\n\n```python\nprint('Basic code for generating and graphing data')\n\ndata_noise=0.0\nvalidation_data_ratio = 0.5\n\n# Generate data\ndata_array = pg.generate_data(pg.DatasetType.ClassifyCircleData, data_noise)\n#data_array = pg.generate_data(pg.DatasetType.ClassifyXORData, data_noise)\n#data_array = pg.generate_data(pg.DatasetType.ClassifyTwoGaussData, data_noise)\n#data_array = pg.generate_data(pg.DatasetType.ClassifySpiralData, data_noise)\n#data_array = pg.generate_data(pg.DatasetType.RegressPlane, data_noise)\n#data_array = pg.generate_data(pg.DatasetType.RegressGaussian, data_noise)\n\n# Divide the data for training and validating at a specified ratio (further, separate each data into Coordinate point data part and teacher label part)\nX_train, y_train, X_valid, y_valid = pg.split_data(data_array, validation_size=validation_data_ratio)\n# You can use training_size instead of validation_size. training_size takes precedence over validation_size.\n\n# Plot the data on the standard graph for Playground\nfig, ax = pg.plot_points_with_playground_style(X_train, y_train, X_valid, y_valid, figsize = (6, 6), dpi = 100)\n\n# # get figure + axes of matplotlib graph and plot the data points\n# fig = pg.get_playground_figure(enable_colorbar=True)\n# ax = pg.get_playground_axes(fig)\n# pg.plot_points(ax, X_train, y_train, X_valid, y_valid)\n# # These 3 lines equal to `plot_points_with_playground_style` function\n\n# draw the decision boundary of X1 input (feature)\npg.draw_decision_boundary(fig, ax, node_id=pg.InputType.X1, discretize=False)\n\n# # uncomment if a graph is not shown\n# import matplotlib.pyplot as plt\n# plt.show()\n```\n\n```python\nprint('Signature of main @staticmethod')\n\nimport sys\nif sys.version_info[0] < 3: # inspect.signature was introduced at version Python 3.3\n  !pip install funcsigs\n\ntry:\n    from inspect import signature\nexcept ImportError:\n    from funcsigs import signature\n\nprint('pg.plot_sample', str(signature(pg.plot_sample)))\n# pg.plot_sample (data_type, noise=0.0, validation_size=0.5, visualize_validation_data=False, figsize=(5, 5), dpi=100, node_id=None, discretize=False)\n\nprint('pg.generate', str(signature(pg.generate)))\n# pg.generate (data_type, noise=0.0)\n\nprint('pg.split_data', str(signature(pg.data)))\n# pg.split_data (data, validation_size=0.5, label_num=1)\n\nprint('pg.plot_points_with_playground_style', str(signature(pg.plot_points_with_playground_style)))\n# pg.plot_points_with_playground_style (X_train, y_train, X_valid=None, y_valid=None, figsize=(5, 5), dpi=100)\n\nprint('pg.get_playground_figure', str(signature(pg.get_playground_figure)))\n# pg.get_playground_figure (enable_colorbar=False)\n\nprint('pg.get_playground_axes', str(signature(pg.get_playground_axes)))\n# pg.get_playground_axes (fig)\n\nprint('pg.plot_points', str(signature(pg.plot_points)))\n# pg.plot_points (ax, X_train, y_train, X_valid=None, y_valid=None)\n\nprint('pg.draw_decision_boundary', str(signature(pg.draw_decision_boundary)))\n# pg.draw_decision_boundary (fig, ax, node_id='x', discretize=False, enable_colorbar=True)\n\n```\n\nSample Web app\n-------------------------------------------------------------------\n\n- [Sample Web app of Neural Network Playground-DATA using TensorFlow.js](https://deepinsider.github.io/playground-data/)\n\nLicense\n-------------------------------------------------------------------\n\nCopyright 2018 Digital Advantage Co., Ltd. All Rights Reserved.\nLicensed under the Apache License, Version 2.0.\n\n### The licenses of using open-source code\n\nThis project uses the JavaScript-to-Python-translation of the following open-source code:\n\n[tensorflow / playground (Deep playground) / dataset.ts][dataset.py origin], [heatmap.ts][heatmap.py origin], [playground.ts][playground.py origin], [state.ts][state.py origin]  \nCopyright 2016 Google Inc. All Rights Reserved.  \nLicensed under the Apache License, Version 2.0.\n\n [d3 / d3-scale / linear.js][scalelinear.py origin]  \nCopyright 2010-2015 Mike Bostock. All rights reserved.  \nLicensed under the BSD 3-Clause \"New\" or \"Revised\" License.\n\n[playground page]: https://deepinsider.github.io/playground/\n[original page]: https://github.com/tensorflow/playground\n[src]: https://github.com/DeepInsider/playground-data\n[pypi]: https://pypi.org/project/playground-data/\n[dataset.py origin]: https://github.com/tensorflow/playground/blob/master/src/dataset.ts\n[heatmap.py origin]: https://github.com/tensorflow/playground/blob/master/src/heatmap.ts\n[playground.py origin]: https://github.com/tensorflow/playground/blob/master/src/playground.ts\n[scalelinear.py origin]: https://github.com/d3/d3-scale/blob/master/src/linear.js\n[state.py origin]: https://github.com/tensorflow/playground/blob/master/src/state.ts",
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