Add changes by Michael Stanway (April 2023)

Attempt to do PR #50 properly.

Category.m

@@ -1,27 +1,32 @@
 classdef Category
     % Represents a category of tonal waveforms.
     %   Contains methods for adding contours and calculating
-    %   how much a given contour matches others in this category.
+    %   how much a given contour matches others in this category, as well
+    %   as updating the category with a new contour or removing the contour
+    %   from a category.
 
     properties
-        reference  % The average of all contours in the category;
-                   % this is what prospective members are compared to
-        size % The number of contours in this category
+        average_cont     % The average of all contours in the category;
+                        % this is what new contours are compared to.
+        num_conts        % The number of contours which have already been categorised into this category
     end
 
     methods
-        function cat = Category(size, reference)
-            % Construct a new category from the given array, which must
-            % contain at least one contour
-            cat.size = size;
-            cat.reference = reference;
+        function cat = category(average_cont, numConts)
+            % Construct a category object. If numConts drops to be below
+            % zero, the category will need to be deleted from the network
+            % since it no longer contains any contours
+            cat.numConts = numConts;
+            cat.average_cont  = average_cont;
         end
 
         function val = compare(cat, contour)
             val = 0;
             % Compare a given contour object with the reference contour and
             % return a percentage match value
             % TODO
+
+            % Incorporate the code from ARTwarp_Calculate_Match
         end
 
         function cat = add(cat, contour)

Contour.m

@@ -1,8 +1,3 @@
-% This class represents an abstraction of a tonal waveform
-% Using ContourFactory, a dataset can be read into ARTwarp and turned into
-%   these base units (individual contours), which are then used by the
-%   neural network.
-
 classdef Contour
     % Represents a single tonal waveform
     % Holds the raw frequency values as well as extrapolated variables

ContourFactory.m

@@ -1,15 +1,16 @@
-% A factory to import contour objects from a directory containing a dataset
-% Ideally, this file should be ignored and only the created Contour objects
-%   interacted with.
-% Datasets can contain both CSV and CTR files
+% A factory to construct an object containing all the contours from within
+% the data directory (this directory may contain both ctr and csv files)
 
 classdef ContourFactory
     methods (Static)
         function contours_shuffled = load_contours(varargin)
+            % Loads all of the contours from the directory and then
+            % shuffles them
+
             if length(varargin) == 1
                 dir_path = varargin{1};
             else
-                dir_path = cd + "/Test_Data"; % temporarily the subdirectory ./Test_Data
+                dir_path = cd + "/BrazilData"; % temporarily the subdirectory ./Test_Data
             end
             % path = uigetdir('Select the folder containing the contour files') % loads folder
             contours = [ ContourFactory.getCSV(dir_path), ContourFactory.getCTR(dir_path) ];

NetworkFactory.m

@@ -1,5 +1,5 @@
 classdef NetworkFactory
-    % A factory to initialise the network to be used within an ARTwarp run.
+    % Summary of this class goes here
     %   Detailed explanation goes here
 
     methods (Static)
@@ -9,9 +9,13 @@
             net = Network(weights);
         end
 
+
         function load_network
             % Load a saved network
-            %   TODO: no idea how we would store networks
+            %   This will need to include:
+            %           Weights of each category 
+            %           Number of whistles that were previously contained
+            %           in a category
         end
 
         function net = run_categorisation(network, contours, parameters)

tst/network_test.m

@@ -15,4 +15,4 @@
 assert(isequal(network.categories(network.num_categories), cat), "Problem with adding category.");
 
 % Further tests can be added after compare function in Category.m has been
-% implemented
+% implemented