GitLab

- regression_radius_deep: adapt to new suggest and results.csv structure. Modern algorithms added. Argparse added
- comments in all scripts
Readme
- update of instructions

- regression_radius_deep: adapt to new suggest and results.csv structure. Modern algorithms added. Argparse added
- comments in all scripts
Readme
- update of instructions

- add img_process_dir
Readme
- update instructions

- change sample images to include the cluster they belong into the name
Readme
- update instructions of repository
Python
- GUI_SANS comment and cleaning

# Conflicts:
#	doc_files/Sphere/suggest.csv

# Conflicts:
#	doc_files/Sphere/suggest.csv

- create_img_param_general.m
- create_suggested_images.m
- save_sim_sphere_coresphere.m

Add example files in doc_files (results and suggest)
Add regression and CNN models

- create_img_param_general.m
- create_suggested_images.m
- save_sim_sphere_coresphere.m

- create_img_param.m
- save_sim_sphere.m
- regression_radius_stacked_model.py

-  multiv_multip_regression: added Multivariant Multiple Regression. Save reg models
- GUI_SANS: interface for evaluating one by one images. Read nxs files
- move_files: generalize for different scatter models the function: write_CNN_prediction
- label_classify_image_folder: adapt it to classify core-shell Sphere images

Matlab
- create_suggested_images: modify to create core_shell_sphere in adittion to Sphere. You can decide by inputting the model
- create_img_param_general: create core_shell_sphere in adittion to Sphere
-save_sim_sphere_coresphere: create core_shell_sphere in adittion to Sphere

* before "predicted" and "mean_predicted": instrumental parameters
* "predicted" and "mean_predicted": CNN results
* after "predicted" and "mean_predicted": sample parameters (radius, shell, rhocore...)

* before "predicted": instrumental parameters
* "predicted": CNN classification
* just after "predicted": sample parameters (radius)
* just after sample parameters: error in sample parameters (radius)

* before "predicted" and "mean_predicted": instrumental parameters
* "predicted" and "mean_predicted": CNN results
* after "predicted" and "mean_predicted": sample parameters (radius)

* before "predicted" and "mean_predicted": instrumental parameters
* "predicted" and "mean_predicted": CNN results
* after "predicted" and "mean_predicted": sample parameters

Files that has the log produced in "retrain.py" when the option "--print-misclassfied-images" is truth

- Inclusion of background in image generation
- regression_radius_deep: comments in code