mohamudmm / fuzzy-fusion Goto Github PK
View Code? Open in Web Editor NEWThis project forked from blake-ruprecht/fuzzy-fusion
Fuzzy Logic-Based Fusion of Deep Learners in Remote Sensing
This project forked from blake-ruprecht/fuzzy-fusion
Fuzzy Logic-Based Fusion of Deep Learners in Remote Sensing
################################################################################ ################################################################################ ################################################################################ # Date: AUG-16-2019 # Institution: University of Missouri (Columbia, MO) # Authors: Blake Ruprecht, Muhammad Islam, and Derek Anderson # # DESCRIPTION ------------------------------------------------------------------ # This is PyTorch code for an Adaptive Neural Fuzzy Inference System (ANFIS) # # Notes: # 1. The below FuzzyNeuron class is a single fuzzy inference system (FIS) # What does that mean? # - Its a single first-order Takagi Sugeno Kang (TSK) inference system # What does that mean? # - Each neuron consists of R different IF-THEN rules and the aggregation of # their output. # Coming soon... # - We will post cost updates that allow you to do things like # - Learn the number of rules R (via an algorithm like DBSCAN or # k-means/fcm/pcm with cluster validity) # # FOR MORE DETAILS, SEE: ------------------------------------------------------- # # Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE # Transactions on Systems, Man and Cybernetics, 23 (3), 1993 # # GNU GENERAL PUBLIC LICENSE --------------------------------------------------- # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <https://www.gnu.org/licenses/>. # ############################################################################## ############################################################################## ############################################################################## # # INSTALLATION GUIDE # # 1.) This is all Python 3.7 code, heavily utilizing PyTorch. # The following libraries must be installed to run: # # torch # tqdm # matplotlib # scikit-learn # numpy # # We installed everything using conda. The following command (in a # terminal) can be used to install each library (e.g. scikit-learn) # # conda install -c conda-forge scikit-learn # # 2.) Once all of the relevant libraries are installed, make sure you have # the “trapANFISPyTorchDeep.py” and “dataset_utils.py” in the same directory, # since we will be using “dataset_utils.py” as a library within “ANFIS…” # ############################################################################## ############################################################################## ############################################################################## # # RUNNING THE PROGRAM # # Within the file “ANFISPyTorchDeep.py” we have an example to see how to use # the main code. It is at the bottom of the file. The example will execute # when the following command is run inside the terminal: # # python ANFISPyTorchDeep.py # # This will execute everything after the “if __name__==‘__main__’:” statement # in the code. The example is either a two-layer ANFIS (the if(0) statement), # or a single ANFIS neuron. # # We recommend starting with the single neuron (which the code defaults to) # Within this example, the number of rules to be learned and the number of # antecedents can be changed, along with the number of epochs. The code will # plot the results using matplotlib, showing rule coverage. # # Finally, the initialization method can be changed within the instantiation # of the FuzzyNeuron() class in the line “net = FuzzyNeuron(R,A,2,x,l)”. # The “2” can be changed to 0, 1, 2 and will change the initialization # method as explained in the __init__ section of the FuzzyNeuron class. # ############################################################################## ############################################################################## ##############################################################################
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.