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Mast malleable contour #136

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Mast malleable contour #136

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78ee041
AddNumPts
gcalongi Aug 31, 2021
138b149
Fix Dim Issue
gcalongi Sep 3, 2021
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30 changes: 18 additions & 12 deletions Functions/Call Classification/UnsupervisedClustering_Callback.m
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Original file line number Diff line number Diff line change
Expand Up @@ -24,12 +24,13 @@ function UnsupervisedClustering_Callback(hObject, eventdata, handles)
case 'K-means (recommended)'
[ClusteringData, ~, ~, ~, spectrogramOptions] = CreateClusteringData(handles, 'forClustering', true, 'save_data', true);
if isempty(ClusteringData); return; end
clusterParameters= inputdlg({'Shape weight','Frequency weight','Duration weight'},'Choose cluster parameters:',1,{'3','2','1'});
clusterParameters= inputdlg({'Number of Contour Pts','Shape weight','Frequency weight','Duration weight'},'Choose cluster parameters:',1,{'12','3','2','1'});
if isempty(clusterParameters); return; end
slope_weight = str2double(clusterParameters{1});
freq_weight = str2double(clusterParameters{2});
duration_weight = str2double(clusterParameters{3});
data = get_kmeans_data(ClusteringData, slope_weight, freq_weight, duration_weight);
num_pts = str2double(clusterParameters{1});
slope_weight = str2double(clusterParameters{2});
freq_weight = str2double(clusterParameters{3});
duration_weight = str2double(clusterParameters{4});
data = get_kmeans_data(ClusteringData, num_pts, slope_weight, freq_weight, duration_weight);
case 'Variational Autoencoder'
[encoderNet, decoderNet, options, ClusteringData] = create_VAE_model(handles);
data = extract_VAE_embeddings(encoderNet, options, ClusteringData);
Expand All @@ -45,10 +46,15 @@ function UnsupervisedClustering_Callback(hObject, eventdata, handles)
switch choice
case 'K-means (recommended)'
spectrogramOptions = [];
load(fullfile(PathName,FileName),'C','freq_weight','slope_weight','duration_weight','clusterName','spectrogramOptions');
load(fullfile(PathName,FileName),'C','num_pts','freq_weight','slope_weight','duration_weight','clusterName','spectrogramOptions');
ClusteringData = CreateClusteringData(handles, 'forClustering', true, 'spectrogramOptions', spectrogramOptions, 'save_data', true);
if isempty(ClusteringData); return; end
data = get_kmeans_data(ClusteringData, slope_weight, freq_weight, duration_weight)
% Set number of contour pts to default 12 if it
% didn't load as a variable
if exist('num_pts','var') ~= 1
num_pts = 12;
end
data = get_kmeans_data(ClusteringData, num_pts, slope_weight, freq_weight, duration_weight);
case 'Variational Autoencoder'
C = [];
load(fullfile(PathName,FileName),'C','encoderNet','decoderNet','options');
Expand Down Expand Up @@ -164,7 +170,7 @@ function UnsupervisedClustering_Callback(hObject, eventdata, handles)
case 'K-means (recommended)'
[FileName, PathName] = uiputfile(fullfile(handles.data.squeakfolder, 'Clustering Models', 'K-Means Model.mat'), 'Save clustering model');
if ~isnumeric(FileName)
save(fullfile(PathName, FileName), 'C', 'freq_weight', 'slope_weight', 'duration_weight', 'clusterName', 'spectrogramOptions');
save(fullfile(PathName, FileName), 'C', 'num_pts','freq_weight', 'slope_weight', 'duration_weight', 'clusterName', 'spectrogramOptions');
end
case 'ARTwarp'
[FileName, PathName] = uiputfile(fullfile(handles.data.squeakfolder, 'Clustering Models', 'ARTwarp Model.mat'), 'Save clustering model');
Expand Down Expand Up @@ -198,14 +204,14 @@ function UnsupervisedClustering_Callback(hObject, eventdata, handles)
end
end

function data = get_kmeans_data(ClusteringData, slope_weight, freq_weight, duration_weight)
function data = get_kmeans_data(ClusteringData, num_pts, slope_weight, freq_weight, duration_weight)
% Parameterize the data for kmeans
ReshapedX = cell2mat(cellfun(@(x) imresize(x',[1 13]) ,ClusteringData.xFreq,'UniformOutput',0));
ReshapedX = cell2mat(cellfun(@(x) imresize(x',[1 num_pts+1]) ,ClusteringData.xFreq,'UniformOutput',0));
slope = diff(ReshapedX,1,2);
slope = zscore(slope);
freq = cell2mat(cellfun(@(x) imresize(x',[1 12]) ,ClusteringData.xFreq,'UniformOutput',0));
freq = cell2mat(cellfun(@(x) imresize(x',[1 num_pts]) ,ClusteringData.xFreq,'UniformOutput',0));
freq = zscore(freq);
duration = repmat(ClusteringData.Duration,[1 12]);
duration = repmat(ClusteringData.Duration,[1 num_pts]);
duration = zscore(duration);
data = [
freq .* freq_weight+.001,...
Expand Down