READ ME

The files in this section are Python 2.7 implementation for wireless-based localization using Gaussian Processes (GP) and Path loss models.

It is recommended to download the main code, processed datasets and notebooks. Raw datasets are also available, but not necessary if the proccessed ones are downloaded.

This text contents:
    1. Copyright
    2. Dependencies 
    2. File structure


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1. COPYRIGHT

Copyright (c) 2016, Renato Miyagusuku 
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
    * Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of the <organization> nor the
      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


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2. DEPENDENCIES
All code runs in python 2.7, core dependencies are:

1. numpy    http://www.numpy.org/                            - scientific computing
2. scipy    http://www.scipy.org/                            - scientific computing
3. GPy      https://github.com/SheffieldML/GPy               - main GP calculations      
4. ipython  http://ipython.org/                              - interactive shell
5. jupyter  http://jupyter.org/                              - documentation
4. seaborn  https://stanford.edu/~mwaskom/software/seaborn/  - optional visualizations

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2. FILE STRUCTURE

Code
Localization030916.zip
  Localization
    sensormodel : main contribution, models wireless signals using GP and Path loss
    sampling    : functions to sample from sensor models
    motionmodel : odometry model used for monte carlo localization    
    montecarlo  : main files for dual monte carlo localization implementation              

Raw dataset
wifi_raw.zip
  wifi_raw
    TrainM.txt  Contains the RSS measurements for training 
    TrainP.txt  Contains the odometry information of each training point 
    TestM.txt   Contains the RSS measurements for testing
    TestP.txt   Contains the odometry information of each training point
    
    *M.txt structure:
        training_point[int] -> macaddress -> RSS1[int], RSS2[int], RSS3[int], ...
    *P.txt structure:
        training_point[int] ->(X:xloc_mm[float], Y:yloc_mm[float], T:heading_angle_rad[float])
 
Processed dataset
wifi_processed.zip
    wifi_processed
    dataTrain   (python dictionary)
        Y: RSS measurements used for training
        X: True locations
    dataTest    (python dictionary)
        Y: RSS measurements used for testing
        Pose: Odometry information
    dataXTrue   (numpy array)
        Ground truth for dataTest 

Processed models
  models
    GPmodel1    pickled optimized GP with no priors model   
    hGPmodel1   pickled optimized GP with path loss model

Notebook
Please download the libraries, and processed datasets in the same folder as the notebook.