Metadata-Version: 2.1
Name: playground-data
Version: 0.6.5
Summary: Data Generation for Neural Network Playground of Deep Insider
Home-page: https://github.com/DeepInsider/playground-data
Author: Digital Advantage Inc.
License: Apache License 2.0
Project-URL: Bug Reports, https://github.com/DeepInsider/playground-data/issues
Project-URL: Source, https://github.com/DeepInsider/playground-data/
Project-URL: Playground Deep Insider version, https://re.deepinsider.jp/playground/index.html
Project-URL: Playground Original version, http://playground.tensorflow.org/
Description: playground-data
        ====================================
        
        Data Generation for Neural Network Playground of Deep Insider.
        
        This 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].
        
        Official pages
        -------------------------------------------------------------------
        
        - [The python package "playground-data" on PyPI for this project is available here][pypi].
        - [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)
        
        Requirements
        -------------------------------------------------------------------
        
        - Python 2: 2.7+ | Python 3: 3.4, 3.5, 3.6+
        - numpy
        - matplotlib
        
        Install this package using `pip`
        -------------------------------------------------------------------
        
        ```bash
        pip install playground-data
        ```
        
        Usage
        -------------------------------------------------------------------
        
        ```python
        from __future__ import print_function
        
        print('Import plygdata package as pg')
        
        import plygdata as pg
        
        # Or, you can 'import' class directly like this:
        # from plygdata.datahelper import DataHelper, DatasetType
        # from plygdata.dataset import DataGenerator
        ```
        
        ```python
        print('Code for plotting sample graph')
        
        #dir(pg.DataHelper)    # How to find class members
        #dir(pg.DataGenerator)
        
        dir(pg.DatasetType)
        #['ClassifyCircleData',
        # 'ClassifySpiralData',
        # 'ClassifyTwoGaussData',
        # 'ClassifyXORData',
        # 'RegressGaussian',
        # 'RegressPlane',
        # ...]
        
        fig, ax = pg.DataHelper.plot_sample(pg.DatasetType.ClassifyCircleData)
        # # uncomment if a graph is not shown
        # import matplotlib.pyplot as plt
        # plt.show()
        ```
        
        ```python
        print('Basic code for generating and graphing data')
        
        data_noise=0.0
        test_data_ratio = 0.5
        
        # Generate data
        data_array = pg.DataGenerator.classify_two_gauss(noise=data_noise)
        #data_array = pg.DataGenerator.classify_circle(noise=data_noise)
        #data_array = pg.DataGenerator.classify_spiral(noise=data_noise)
        #data_array = pg.DataGenerator.classify_xor(noise=data_noise)
        #data_array = pg.DataGenerator.regress_gaussian(noise=data_noise)
        #data_array = pg.DataGenerator.regress_plane(noise=data_noise)
        
        # Divide the data for training and testing at a specified ratio (further, separate each data into Coordinate point data part and teacher label part)
        X_train, y_train, X_test, y_test = pg.DataHelper.split_train_test_x_data_label(data_array, test_size=test_data_ratio)
        
        # Plot the data on the standard graph for Playground
        fig, ax = pg.DataHelper.plot_with_playground_style(X_train, y_train, X_test, y_test)
        # # uncomment if a graph is not shown
        # import matplotlib.pyplot as plt
        # plt.show()
        ```
        
        ```python
        print('Signature of main @staticmethod')
        
        import sys
        if sys.version_info[0] < 3.3: # inspect.signature was introduced at version Python 3.3
          !pip install funcsigs
        
        try:
            from inspect import signature
        except ImportError:
            from funcsigs import signature
        
        print('pg.DataHelper.plot_sample', str(signature(pg.DataHelper.plot_sample)))
        # pg.DataHelper.plot_sample (data_type, visualize_test_data=False, noise=0.0, test_size=0.5, figsize=(5, 5), dpi=100)
        
        print('pg.DataGenerator.classify_two_gauss', str(signature(pg.DataGenerator.classify_two_gauss)))
        # pg.DataGenerator.classify_two_gauss (noise=0.0, numSamples=500)
        
        print('pg.DataHelper.split_train_test_x_data_label', str(signature(pg.DataHelper.split_train_test_x_data_label)))
        # pg.DataHelper.split_train_test_x_data_label (data, test_size=0.5, label_num=1)
        
        print('pg.DataHelper.plot_with_playground_style', str(signature(pg.DataHelper.plot_with_playground_style)))
        # pg.DataHelper.plot_with_playground_style (X_train, y_train, X_test=None, y_test=None, figsize=(5, 5), dpi=100)
        ```
        
        ```python
        print('Imported "playground-data" package version is ...')
        
        print(pg.__version__)
        ```
        
        License
        -------------------------------------------------------------------
        
        Copyright 2018 Digital Advantage Inc. All Rights Reserved.
        Licensed under the Apache License, Version 2.0.
        
        ### The licenses of using open-source code
        
        This project uses the JavaScript-to-Python-translation of the following open-source code:
        
         [Deep playground/src/dataset.ts][dataset.py origin]  
        Copyright 2016 Google Inc. All Rights Reserved.  
        Licensed under the Apache License, Version 2.0.
        
         [d3/d3-scale/linear.js][scalelinear.py origin]  
        Copyright 2010-2015 Mike Bostock. All rights reserved.  
        Licensed under the BSD 3-Clause "New" or "Revised" License.
        
        [playground page]: https://deepinsider.github.io/playground/
        [original page]: https://github.com/tensorflow/playground
        [src]: https://github.com/DeepInsider/playground-data
        [pypi]: https://pypi.org/project/playground-data/
        [dataset.py origin]: https://github.com/tensorflow/playground/blob/master/src/dataset.ts
        [scalelinear.py origin]: https://github.com/d3/d3-scale/blob/master/src/linear.js
        
Keywords: data generation tensorflow deep neural network playground
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
