Shin-ya Hasegawa and Takao Miaki
This work introduces a randomforest method for axial positioning and characterization of a spherical particle.
(1)MATLAB Code for trained data and corresponding parameters(radius, refractive index and z potition)
folder: model_mie_particle_MATLAB_code
To start the learning process, simply run 'model_r_n_z.m'. All codes should be in the same folder. The traine data (model_r_n_z.txt and model_kyokuritsu.txt) are obtained.
(2)MATLAB Code for translation of trained nine data to 1,001 data via interpolation.
folder: 1001data_translation_MATLAB_code
To start the process, simply run 'fit_longdata_curvature.m'. 'model_r_n_z.txt' and 'model_kyokuritsu.txt' in (1) must be in this folder. Finally, 'myData.csv' which containes 1001 curvature data is obtained.
(3)Python Code for training and estimation.
folder: random_forest_python_code To start the process, simply run 'randomfor_3label.py'. 'model_r_n_z.csv' in (1) and 'myData.csv' in (2) must be in this folder. ('model_r_n_z.txt' in (1) should be translated to csv format.)