playground-data 0.6.0

Data Generation for Neural Network Playground of Deep Insider

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 which was forked from tensorflow/playground: Deep playground.

Official pages

• The python package "playground-data" on PyPI for this project is available here.
• The source for this package is available here.

Requirements

• Python 2: 2.7+ | Python 3: 3.4, 3.5, 3.6+
• numpy
• matplotlib

Install this package using pip

pip install playground-data

Usage

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

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',
# ...]

pg.DataHelper.plot_sample(pg.DatasetType.ClassifyCircleData)

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)

print('Signature of main @staticmethod')

import inspect

inspect.signature(pg.DataHelper.plot_sample)
# <Signature (X_train:list, y_train:list, X_test:list=None, y_test:list=None, figsize:tuple=(5, 5), dpi:int=100)>

inspect.signature(pg.DataGenerator.classify_two_gauss)
# <Signature (noise:float=0.0, numSamples:int=500) -> list>

inspect.signature(pg.DataHelper.split_train_test_x_data_label)
# <Signature (data:list, test_size:float=0.5) -> (<class 'list'>, <class 'list'>, <class 'list'>, <class 'list'>)>

inspect.signature(pg.DataHelper.plot_with_playground_style)
# <Signature (X_train:list, y_train:list, X_test:list=None, y_test:list=None, figsize:tuple=(5, 5), dpi:int=100)>

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
Copyright 2016 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0.

d3/d3-scale/linear.js
Copyright 2010-2015 Mike Bostock. All rights reserved.
Licensed under the BSD 3-Clause "New" or "Revised" License.