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ElasticNodes.py
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# Marcus Vinicius Sousa Leite de Carvalho
#
# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement
# Non-Commercial Use Only
# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software").
#
# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at [email protected].
#
# SCOPE OF RIGHTS:
# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes.
# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others.
# If the Software includes source code or data, you may create derivative works of such portions of the Software and distribute the modified Software for non-commercial purposes, as provided herein.
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#
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#
# Copyright (c) NTUITIVE. All rights reserved.
import numpy as np
class ElasticNodes:
growable = None
prunable = None
data_mean = 0
data_standard_deviation = 0
data_variance = 0
number_samples_feed = 0
number_samples_layer = None
bias_mean = None
bias_variance = None
bias_standard_deviation = None
minimum_bias_mean = None
minimum_bias_standard_deviation = None
bias = None
var_mean = None
var_variance = None
var_standard_deviation = None
minimum_var_mean = None
minimum_var_standard_deviation = None
var = None
node_evolution = None
bias_gradient = None
bias_mean_net = None
var_mean_net = None
def __init__(self, number_hidden_layers=1):
nhl = number_hidden_layers # readability
self.number_samples_layer = np.zeros(nhl)
self.bias_mean = np.zeros(nhl)
self.bias_variance = np.zeros(nhl)
self.bias_standard_deviation = np.zeros(nhl)
self.minimum_bias_mean = np.ones(nhl) * np.inf
self.minimum_bias_standard_deviation = np.ones(nhl) * np.inf
self.BIAS = []
self.var_mean = np.zeros(nhl)
self.var_variance = np.zeros(nhl)
self.var_standard_deviation = np.zeros(nhl)
self.minimum_var_mean = np.ones(nhl) * np.inf
self.minimum_var_standard_deviation = np.ones(nhl) * np.inf
self.VAR = []
self.growable = np.ones(nhl) * False
self.prunable = []
for i in range(nhl):
self.prunable.append([-1])