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EXC+ACK: describe to which variables results in exercise must be assi…

…gned. Thanks to #Felix Ball# for the suggestion
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nno committed Jul 25, 2019
1 parent bb246c3 commit 7bc738f608569921337def4dd416ff63dbcfebcd
Showing with 10 additions and 8 deletions.
  1. +1 −1 doc/source/nmsm2019_intro.rst
  2. +9 −7 examples/run_meeg_timelock_measures.m
@@ -157,7 +157,7 @@ Monday
17:40-18:30 Optional: discuss your data models
-------------- ---------------------------------------------------------------------------------------------------
Tuesday
09:00 :doc:`ex_classify_lda`, :doc:`ex_nfold_crossvalidation`.
09:00 :doc:`ex_classify_lda`, :doc:`ex_nfold_crossvalidation`.
-------------- ---------------------------------------------------------------------------------------------------
10:30 Coffee break
-------------- ---------------------------------------------------------------------------------------------------
@@ -145,26 +145,28 @@
'chan',sensor_posterior_axial);

% first slice the dataset, then use cosmo_dim_prune to avoid using
% non-selected data
% non-selected data. Assign the result to ds_sel
% >@@>
ds_sel=cosmo_slice(ds,msk,2);
ds_sel=cosmo_dim_prune(ds_sel);
% <@@<

% define the neighborhood for time with a time radius of 2 time points
% Hint: use cosmo_interval_neighborhood
% define the neighborhood for time with a time radius of 2 time points,
% and assign to time_nbrhood.
% Hint: use cosmo_interval_neighborhood,

% >@@>
time_nbrhood=cosmo_interval_neighborhood(ds_sel,'time','radius',2);
% <@@<

% Define the measure to be cosmo_crossvalidation_measure
% Define the measure to be cosmo_crossvalidation_measure,
% and assign to measure
% >@@>
measure=@cosmo_crossvalidation_measure;
% <@@<

% Define the partitioning scheme using
% cosmo_independent_samples_partitioner.
% cosmo_independent_samples_partitioner, and assign to partitions.
% Use 'fold_count',5 to use 5 folds,
% and use 'test_ratio',.2 to use 20% of the data for testing (and 80% for
% training) in each fold.
@@ -175,7 +177,7 @@
'test_ratio',.2);
% <@@<

% Use the LDA classifier and the partitions just defined.
% Use the LDA classifier and the partitions just defined,
measure_args=struct();
measure_args.partitions=partitions;
measure_args.classifier=@cosmo_classify_lda;
@@ -208,7 +210,7 @@

% define the neighborhood for each dimensions
% First, set 'chan_nbrhood' using cosmo_meeg_chan_neighborhood,
% and use the 'chantype' and 'count paramters to set the channel type
% and use the 'chantype' and 'count' parameters to set the channel type
% and the number of sensors in each searchlight.
% >@@>
chan_nbrhood=cosmo_meeg_chan_neighborhood(ds_sel, 'count', chan_count, ...

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