run_tests: Use an vector of structs for the prediction methods

Allows use to use e.g. pred_methods.name.

1 files changed, 11 insertions, 11 deletions

diff --git a/run_tests.m b/run_tests.m
index 57cb722..da1f7d9 100644
--- a/run_tests.m
+++ b/run_tests.m
@@ -6,26 +6,26 @@
 train_data = q( 1:28, :);
 real_data  = q(29:42, :);
 
-pred_methods = {
-	{ 'mean',	@mean_pred },
-	{ 'regress',	@regress_pred },
-	{ 'quad',	@quad_regress_pred },
-	{ 'log reg.',	@log_regress_pred },
-	{ 'sevenday'	@sevenday_pred },
-	{ 'random'	@rand_pred },
-	{ 'regress2'	@regress_frequency_removal }
-};
+pred_methods = [
+	struct('name', 'mean',     'func', @mean_pred )
+	struct('name', 'regress',  'func', @regress_pred )
+	struct('name', 'quad',     'func', @quad_regress_pred )
+	struct('name', 'log reg.', 'func', @log_regress_pred )
+	struct('name', 'sevenday', 'func', @sevenday_pred )
+	struct('name', 'random',   'func', @rand_pred )
+	struct('name', 'regress2', 'func', @regress_frequency_removal )
+];
 num_methods = size(pred_methods, 1);
 
 pred_list = {};
 for i = 1:num_methods
-	pred_list{i} = pred_methods{i}{2}(p, train_data);
+	pred_list{i} = pred_methods(i).func(p, train_data);
 end
 
 qerr = terr = zeros(1, num_methods);
 err  = zeros(num_methods, size(real_data,2));
 for i = 1:num_methods
-	[qerr(i), terr(i), err(i, :)] = calc_error(pred_methods{i}{1}, real_data, pred_list{i});
+	[qerr(i), terr(i), err(i, :)] = calc_error(pred_methods(i).name, real_data, pred_list{i});
 end
 
 opt_data = opt_pred(real_data, pred_list);