function [model, partition_size] = fit_partitioned_model(...    inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin)%function [models, partition_sizes] = fit_partitioned_model(...%    inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin)%% Fit models to different subsets (columns)  of the input/output data, % as chosen by the selectors matrix. If there is only output data, set input=[].% If there is less than min_size data in partition i, % we set model{i} = []%% Example:% selectors = [1 2 1 1 1%              1 2 2 1 2]% sel_sizes = [2 2] so there are 4 models: (1,1), (2,1), (1,2), (2,2)% We fit model{1} to data from columns 1,4% We fit model{2} to no data% We fit model{3} to data from column 3,5% We fit model{4} to data from column 2 (assuming min_size <= 1)%% For each partition, we call the specified function with the specified arguments% as follows:%  model{i}  = fn(input(:,cols{i}), output(:,cols{i}), args)% (We omit input if [])% partition_size(i) is the amount of data in the i'th partition.%% Example use: row 1 of selectors is whether an object is present/absent% and row 2 is the location.%% Demo:% inputs = 1:5; outputs = 6:10; selectors = as above% fn = 'fit_partitioned_model_testfn';% [model, partition_size] = fit_partitioned_model(inputs, outputs, selectors, [2 2], fn)% should produce% model{1}.input = [1 4], model{1}.output = [6 9]% model{2} = []% model{3}.input = [3 5], model{3}.output = [8 10], % model{4}.input = [2], model{3}.output = [7], % partition_size = [2 0 2 1]sel_ndx = subv2ind(sel_sizes, selectors');Nmodels = prod(sel_sizes);model = cell(1, Nmodels);partition_size = zeros(1, Nmodels);for m=1:Nmodels  ndx = find(sel_ndx==m);  partition_size(m) = length(ndx);  if ~isempty(partition_names) % & (partition_size(m) < min_size)    fprintf('partition %s has size %d, min size = %d\n', ...	    partition_names{m}, partition_size(m), min_size);  end  if partition_size(m) >= min_size    if isempty(inputs)      model{m} = feval(fn_name, outputs(:, ndx), varargin{:});    else      model{m} = feval(fn_name, inputs(:,ndx), outputs(:, ndx), varargin{:});    end  endend