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More support for reduced feature set
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benfulcher committed Jul 22, 2020
1 parent eb65d0d commit cf7fe06
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Showing 3 changed files with 6 additions and 3 deletions.
2 changes: 1 addition & 1 deletion PeripheryFunctions/GiveMeDefaultClassificationParams.m
Expand Up @@ -88,6 +88,6 @@
% Restrict to a reduced set of features?
% '' (all), 'catch22', 'catchaMouse16', 'noLengthLocationSpread'
% (cf. GiveMeFeatureSet)
params.reducedFeatureSet = 'catch22';
params.reducedFeatureSet = 'noLengthLocationSpread';

end
2 changes: 1 addition & 1 deletion PlottingAnalysis/TS_Plot2d.m
Expand Up @@ -252,7 +252,7 @@
legendText{i} = sprintf('Group %u (%u)',i,sum(groupLabels==classLabels{i}));
end
end
legend([handles{:}],legendText,'interpreter','none');
legend([handles{:}],legendText,'interpreter','none','Location','best');
end

%-------------------------------------------------------------------------------
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5 changes: 4 additions & 1 deletion PlottingAnalysis/TS_PlotLowDim.m
Expand Up @@ -79,6 +79,9 @@
% Give basic info about the represented classes:
TellMeAboutLabeling(TimeSeries);

% Filter down a reduced feature set if required:
[TS_DataMat,Operations] = FilterFeatures(TS_DataMat,Operations,cfnParams);

% ------------------------------------------------------------------------------
%% Do the dimensionality reduction using Matlab's built-in PCA algorithm
% ------------------------------------------------------------------------------
Expand Down Expand Up @@ -128,7 +131,7 @@
else
fprintf(1,['Computing a two-dimensional t-SNE embedding (using barnes-hut',...
' approximation) of the %u x %u data matrix...\n'], ...
numPCAComponents,size(TS_DataMat,1),size(TS_DataMat,2));
size(TS_DataMat,1),size(TS_DataMat,2));
Y = tsne(BF_NormalizeMatrix(TS_DataMat,'zscore'),'Algorithm','barneshut',...
'Distance','euclidean','NumDimensions',2);
end
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