comparison core/magnatagatune/MTTMixedFeatureGenreRandom.m @ 0:e9a9cd732c1e tip

first hg version after svn

+classdef MTTMixedFeatureGenreRandom < MTTAudioFeature & handle
+    % ---
+    %
+    % The usual worklow for these features constist of three steps
+    % 1. extract: extracts the basic single-file dependent features
+    % 2. define_global_transform: calculates the global feature
+    %       transformation parameters
+    % 3. finalise: applies the common transformations to a specific feature
+    % ---
+    
+    properties(Constant = true)
+        
+        % svn hook
+        my_revision = str2double(substr('$Rev: 455 $',  5, -1));
+    end
+       
+    properties
+        % ---
+        % Set default parameters
+        % ---
+        my_params = MTTAudioFeature.inherited_params(...
+            'MTTTagFeatureGenreBasic', ...
+            ... % ---
+            ... % following are Random parameters
+            ... % --- 
+            'nrandoms', 100 ...
+        );
+    end
+    
+    % ---
+    % member functions
+    % ---
+    methods
+        
+        % ---
+        % constructor: pointer to feature in database
+        % ---
+        function feature = MTTMixedFeatureGenreRandom(varargin)
+
+            feature = feature@MTTAudioFeature(varargin{:});
+        end
+
+        % ---
+        % extract feature data by combining genre adn basicsm features
+        % ---
+        function data = extract(feature, clip)
+            
+            % ---
+            % get MTTMixedFeatureGenreBasicSm this includes possible
+            % local normalisations
+            % ---
+            data.tags = clip.features('MTTTagFeatureGenreBasic',feature.my_params);
+            
+            % ---
+            % get genre tag features
+            % ---
+            
+            data.random = clip.features('MTTRandomFeature',feature.my_params);
+            
+            
+            % prepare field for final features
+            data.final.vector = [];
+            data.final.vector_info = struct(); 
+            data.final.dim = 0;
+           
+            % save info data
+            data.info.type = class(feature);
+            data.info.owner_id = clip.id;
+            data.info.creatorrev = feature.my_revision;
+            
+            % save parameters
+            data.info.params = feature.my_params;
+        end
+        
+        function define_global_transform(features)
+        % calculate and set normalization factors from the group of 
+        % input features. These features will be set for the full database
+        
+            if numel(features) == 1
+                error ('Insert feature array for this method');
+            end
+            
+            % ---
+            % We collect all the relevant tag 
+            % features and get the transform on this basis.
+            % ---
+            for i = 1:numel(features)
+            	tags(i) = features(i).data.tags;
+            end
+            
+            % call the features own transsform function
+            tags.define_global_transform();
+            
+            % ---
+            % Random features have no global transform
+            % ---
+
+            % ---
+            % set common feature values for mixed features
+            % ---
+            features(1).my_db.set_common([1]); %trivial common
+        end
+        
+        
+        function finalise(feature)
+        % applies a final transformation and collects the 
+        % information of this feature within a single vector
+        % see info for types in specific dimensions
+
+            for i = 1:numel(feature)
+                
+                % check for neccesary parameters
+                if isempty(feature(i).my_db.commondb)
+
+                    error('Define the global transformation first');
+                end
+                
+                % ---
+                % final data assembly
+                % ---
+                % concatenate vectors
+                feature(i).data.final.vector = ...
+                    [feature(i).data.tags.vector() ;...
+                    feature(i).data.random.vector()];
+                
+                % add up feature dimensions
+                feature(i).data.final.dim = feature(i).data.tags.dim...
+                    + feature(i).data.random.dim;
+                
+                % concatenate labels
+                lbl1 = feature(i).data.tags.labels();
+                lbl2 = feature(i).data.random.labels();
+                feature(i).data.final.vector_info.labels = ...
+                    {lbl1{:}, lbl2{:}};
+            end
+        end
+        
+        % ---
+        % destructor: do we really want to remove this 
+        % from the database? No, but 
+        % TODO: create marker for unused objects in db, and a cleanup
+        %  function
+        % ---
+        function delete(feature)
+            
+        end
+    end
+end