subtom_cluster
Classifies particles based on given coefficients.
subtom_cluster(
'all_motl_fn_prefix', all_motl_fn_prefix ('combinedmotl/allmotl'),
'coeff_fn_prefix', coeff_fn_prefix ('class/coeff'),
'output_motl_fn_prefix', output_motl_fn_prefix ('class/allmotl'),
'iteration', iteration (1),
'cluster_type', cluster_type ('kmeans'),
'eig_idxs', eig_idxs ('all'),
'num_classes', num_classes ('2'))
Takes the motive list given by all_motl_fn_prefix and the coefficients
specified by coeff_fn_prefix for the iteration iteration and clusters
the data based on the coefficients. Clustering can be done using one of three
methods, which are specfied by cluster_type. The options are K-Means
clustering with ‘kmeans’, Hierarchical Ascendant Clustering with ‘hac’ and a
Gaussian Mixture Model with ‘gaussmix’. A subset of coefficients can be selected
and are given as a semicolon-separated string of indices as coeff_idxs. The
string can also contain ranges delimited by a dash, for example ‘1;3;5-10’. The
data will be clustered into num_classes number of clusters and the clustered
motive list will be written out to a file given by output_motl_fn_prefix.
Example
subtom_cluster(...
'all_motl_fn_prefix', 'combinedmotl/allmotl', ...
'coeff_fn_prefix', 'class/eigcoeff_msa', ...
'output_motl_fn_prefix', 'class/allmotl_msa', ...
'iteration', 1, ...
'cluster_type', 'hac', ...
'coeff_idxs', '2-5;7;9-20', ...
'num_classes', '20')