This repository is a simple implementation for Mean Shift(MS) algorithm. The basic idea of the algorithm I have done some records in /report/report.pdf. So, please read report.pdf first!!!

├── Algo1.py
├── Algo2.py
├── data/
├── Data_Process.py
├── Mean_Shift.py
├── README.md
├── Reference/
├── report/
├── res/
├── run.py
└── Visualize_Plot.py

Directories here include :

data/			—— The input original data(csv file)
Reference/		—— The reference paper
report/			—— My report resources
res/			—— the result of different data set/algorithms

The python file :

Algo1.py		—— The implementation of Algo1
Algo2.py		—— The implementation of Algo2
Mean_Shift.py	—— The process of the Mean Shift algorithm
Data_Process.py	—— The data processing methods
Visualize_Plot.py —— The data visualization methods
run.py			—— main function to run the algorithm

The main function is defined in the file run.py.

The main loop of the run.py is :

1. Decide what parameters to use. The parameters include :

   1. data_path: the path of the data, e.g: data/data1.csv

   2. algo_name: to use Algo1 or Algo2, e.g: Algo1

   3. bandwidth: the bandwidth of kernel function. (If it None is set, it means it will compute bandwidth by N).

   4. threshold: the threshold of the mean shift, e.g: 0.00001

2. By command python3 run.py

3. The log can be shown as :

   use Algo1
   get original data
   get processed data
   get original data
   set bandwidth= 0.9336688328456105
   iteration times = 1 , max_distance = 1.00001
   iteration times = 2 , max_distance = 0.17686936528050956
   iteration times = 3 , max_distance = 0.04682090822565989
   iteration times = 4 , max_distance = 0.012449725359511938
   iteration times = 5 , max_distance = 0.0033168315077783343
   iteration times = 6 , max_distance = 0.0008840820836842969
   iteration times = 7 , max_distance = 0.00023567080958232224
   iteration times = 8 , max_distance = 6.282444587304013e-05
   iteration times = 9 , max_distance = 1.674764765164474e-05
   use Algo1
   use Algo2
   N= 100
   N= 200
   index = 0 centroid = [6.264581278374883, 4.835123552413281]
   index = 1 centroid = [3.260652115284019, 0.9951555487518141]
   index = 0 variance of cluster = 0.939911335274201
   index = 1 variance of cluster = 0.4847930143675687
   

   The last output :

   index = 0 centroid = [6.264581278374883, 4.835123552413281]
   index = 1 centroid = [3.260652115284019, 0.9951555487518141]
   index = 0 variance of cluster = 0.939911335274201
   index = 1 variance of cluster = 0.4847930143675687
   

   are the centroid and variance of the corresponding cluster.

4. The result of points and clusters is shown in the file data_alg_bandwidth_threshold.png ， e.g: data1_Algo1_None_1e-05.png

In the report directory is my Task Report, I explain my understanding of the algorithm. The report is report.pdf.

• Multi-threaded optimization;
• Estimation of different parameters;