# Globally-Optimal Gaussian Mixture Alignment (GOGMA): config
# Configuration file for Globally-Optimal Gaussian Mixture Alignment
# 
# Campbell, D., and Petersson, L., "GOGMA: Globally-Optimal Gaussian
# Mixture Alignment", IEEE Conference on Computer Visision and
# Pattern Recognition (CVPR), Las Vegas, USA, IEEE, Jun. 2016
# 
# For the full license, see license.txt in the root directory
# 
# Author: Dylan Campbell
# Date: 20160212
# Revision: 1.1

########################################################################################
## Branch-and-Bound Parameters
########################################################################################
# Tolerance for epsilon-optimality. Corresponds to the largest permissable gap between the lower bound and the discovered optimal value
epsilon=0.1

# Translation Bounds
translation_minimum=-0.5
translation_maximum=+0.5

# Initialisation type. Batch initialisation runs local GMA for every subcube in the first subdivision
# Standard: 0, Batch: 1
initialisation_type=1

########################################################################################
## SVGM Parameters
########################################################################################
# To specify different values for SVGM_X and SVGM_Y, use vector notation (X,Y)

# Scale parameter for SVM
sigma=(0.025,0.025)

# Nu parameter for SVM
# Lower bound on fraction of support vectors
# Upper bound on the misclassification rate
# Set min_num_components > 0 to set nu automatically
nu=(0.001,0.001)

# Randomly select n = min(num_points, max_num_points) points from point-set
# Set max_num_points=0 to use all points
max_num_points=(0,0)

# Minimum number of GMM components
# Sets nu = min_num_components / max_num_points
# Set min_num_components=0 to use nu above
min_num_components=(10,10)

# Parameter by which to inflate sigma building the SVGM
# Set to 1.0 if no inflation is desired
sigma_factor=(0.5,0.5)

########################################################################################
## Refinement SVGM Parameters
########################################################################################
# Select whether to run refinement
# On: 1, Off: 0
do_refinement=1

# Scale parameter for SVM
sigma_refinement=(0.01,0.01)

# Nu parameter for SVM
# Lower bound on fraction of support vectors
# Upper bound on the misclassification rate
# Set min_num_components > 0 to set nu automatically
nu_refinement=(0.001,0.001)

# Randomly select n = min(num_points, max_num_points) points from point-set
# Set max_num_points=0 to use all points
max_num_points_refinement=(0,0)

# Minimum number of GMM components
# Sets nu = min_num_components / max_num_points
# Set min_num_components=0 to use nu above
min_num_components_refinement=(500,500)

# Parameter by which to inflate sigma building the SVGM
# Set to 1.0 if no inflation is desired
sigma_factor_refinement=(0.5,0.5)
