CDP-WMM

Paper for: Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth Image Analysis

File

datas   # container of data  
result  # container figure of dataset  
config  # the hyper parameters of dataset  
model   # dp-wmm model code  
utils   # some util function  
train   # training code  

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Requirements

matplotlib ==3.1.1  
numpy      ==1.16.4  
pandas     ==0.23.2  
scipy      ==1.1.0  
sklearn    ==0.21.3  

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Run cdp-wmm

params:

-name    dataset name  
-lp      Load hyper parameter or not  
-verbose print information or not  

-t       truncation of model  
-gamma   stick hyper params
-mth     the threshold of Cluster number  
-m       max iterations of training  

example:
python train.py -name syn_data2 -lp 1 -verbose 1 -t 10 -gamma 1 -mth 0.01 -m 100

Reference

@InProceedings{YANG2020,
author="Yang, Lin; Liu, Yuhang and Fan, Wentao",
title="Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth Image Analysis",
booktitle="Pattern Recognition and Computer Vision",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="17--28",
}