added Kernel Mutual Subspace Method

examples/kmsm.asv

@@ -0,0 +1,19 @@
+%% Load data
+testDataUsage = TestDataUsage.Subsets;
+n = 2;
+[trainData, testData, testLabels] = prepareData(testDataUsage, n);
+
+%% Train model
+numDimReferenceSubspace = 10;
+numDimInputSubspace = 4;
+sigma = 1;
+
+model = KMSM(trainData,...
+    numDimReferenceSubspace,...
+    numDimInputSubspace,...
+    sigma,...
+    testLabels);
+
+%% Evaluate model
+modelEvaluation = model.evaluate(testData);
+modelEvaluation.printResults();

examples/kmsm.m

@@ -1,60 +1,19 @@
-%% Load Data
-clear;
-clc;
-load('TsukubaHandDigitsDataset24x24.mat')
-% Check if the variable 'trainData' and 'testData' do not exist in the workspace
-% if ~(exist('trainData', 'var') == 1 && exist('testData', 'var') == 1)
-%     % If they don't exist, load the data from the .mat file
-%     load('TsukubaHandDigitsDataset24x24.mat');
-% end
-% you can use the following code to convert the test data format from 3d to 4d
-testData = subsetTestData(testData, 2);
-% specific_class = 5;
-%% to accomodate for MATLAB indexing
-% specific_class = specific_class + 1;
-training_data = cvlNormalize(trainData);
-testing_data = cvlNormalize(testData);
-% testing_data = testData(:, :, specific_class);
-size_of_test_data = size(testing_data);
+%% Load data
+testDataUsage = TestDataUsage.Subsets;
+n = 2;
+[trainData, testData, testLabels] = prepareData(testDataUsage, n);
 
-% get number of elements of size_of_test_data
-array_size = numel(size_of_test_data);
-
-if array_size == 4
-    % do nothing
-    num_sets = size_of_test_data(3);
-    num_classes = size_of_test_data(4);
-elseif array_size == 3
-    num_sets = 1;
-    num_classes = size_of_test_data(3);
-else
-    num_classes = 1;
-    num_sets = 1;
-end
-
-%% Train Model
-num_dim_reference_subspaces = 10;
-num_dim_input_subpaces = 5;
+%% Train model
+numDimReferenceSubspace = 10;
+numDimInputSubspace = 4;
 sigma = 1;
 
-reference_subspaces = cvlKernelBasisVector(training_data, num_dim_reference_subspaces, sigma);
-input_subspaces = cvlKernelBasisVector(testing_data, num_dim_input_subpaces, sigma);
-% save('reference_subspaces.mat', 'reference_subspaces');
-% reference_subspaces = reference_subspaces(:, :, 1);
-tic;
-%% Recognition Phase
-similarities = cvlKernelCanonicalAngles(training_data,reference_subspaces,...
-    testing_data, input_subspaces, sigma);
-similarities = similarities(:, :, end, end);
-% End timing and display the elapsed time
-elapsedTime = toc;
-fprintf('The code block executed in %.5f seconds.\n', elapsedTime);
-model_evaluation = ModelEvaluation(similarities, generateLabels(num_classes, num_sets));
-
-displayModelResults('Kernel Mutual Subspace Methods', model_evaluation);
+model = KMSM(trainData,...
+    numDimReferenceSubspace,...
+    numDimInputSubspace,...
+    sigma,...
+    testLabels);
 
-%% Print preditions
-% disp(model_evaluation.predicted_labels);
-% disp(model_evaluation.true_labels);
-% disp(similarities)
-% plotSimilarities(similarities)
+%% Evaluate model
+modelEvaluation = model.evaluate(testData);
+modelEvaluation.printResults();

src/classes/@CMSM/CMSM.m

@@ -1,7 +1,6 @@
 classdef CMSM
     properties
         name = 'Constrained Mutual Subspace Method';
-        trainData;
         generalizedDifferenceSubspace; % GDS
         referenceSubspaces;
         numDimInputSubspace;
@@ -13,11 +12,11 @@
         function obj = CMSM(trainData, numDimReferenceSubspace, numDimInputSubspace, indexOfEigsToKeep, labels)
             numDim = size(trainData, 1);
             numClasses = size(trainData, 3);
-            obj.trainData = trainData;
+            trainData = cvlNormalize(trainData);
             obj.numDimReferenceSubspace = numDimReferenceSubspace;
             obj.numDimInputSubspace = numDimInputSubspace;
             obj.trueTestLabels = labels;
-            subspaces = cvlBasisVector(obj.trainData,...
+            subspaces = cvlBasisVector(trainData,...
                 obj.numDimReferenceSubspace);
             P = zeros(numDim, numDim);
             for I=1:numClasses
@@ -61,6 +60,7 @@
         function scores = getSimilarityScores(obj, testData)
             testDatasize = size(testData);
             testDatasizeNumElements = numel(testDatasize);
+            testData = cvlNormalize(testData);
 
             if testDatasizeNumElements == 4
                 numSets = testDatasize(3);

src/classes/@KMSM/KMSM.m

@@ -0,0 +1,58 @@
+classdef KMSM
+    properties
+        name = 'Kernel Mutual Subspace Method';
+        trainData;
+        referenceSubspaces;
+        numDimInputSubspace;
+        numDimReferenceSubspace;
+        sigma;
+        trueTestLabels;
+    end
+
+    methods
+        function obj = KMSM(trainData, numDimReferenceSubspace, numDimInputSubspace, sigma, labels)
+            obj.trainData = cvlNormalize(trainData);
+            obj.sigma = sigma;
+            obj.numDimReferenceSubspace = numDimReferenceSubspace;
+            obj.numDimInputSubspace = numDimInputSubspace;
+            obj.trueTestLabels = labels;
+            obj.referenceSubspaces = cvlKernelBasisVector(obj.trainData,...
+                                         obj.numDimReferenceSubspace,...
+                                         obj.sigma);
+        end
+
+
+        % Returns the predicted labels for the test data
+        function prediction = predict(obj, testData)
+            similarityScores = obj.getSimilarityScores(testData);
+            eval = ModelEvaluation(similarityScores, obj.trueTestLabels, obj.name);
+            prediction = eval.predicted_labels;
+        end
+
+        % Returns the similarity scores for the test data (same as probabilities)
+        function probabilities = predictProb(obj, testData)
+            probabilities = obj.getSimilarityScores(testData);
+        end
+
+        % Returns the evaluation object for the test data
+        function eval = evaluate(obj, testData)
+            similarityScores = obj.getSimilarityScores(testData);
+            eval = ModelEvaluation(similarityScores, obj.trueTestLabels, obj.name);
+        end
+    end
+
+    methods (Access = private)
+        function scores = getSimilarityScores(obj, testData)
+            testData = cvlNormalize(testData);
+            inputSubspace = cvlKernelBasisVector(testData,...
+                                               obj.numDimInputSubspace,...
+                                               obj.sigma);
+            similarities = cvlKernelCanonicalAngles(obj.trainData,...
+            obj.referenceSubspaces,...
+            testData,...
+             inputSubspace,...
+             obj.sigma);
+            scores = similarities(:, :, end, end);
+        end
+    end
+end

src/classes/@MSM/MSM.m

@@ -1,50 +1,50 @@
 classdef MSM
     properties
         name = 'Mutual Subspace Method';
-        trainData;
         referenceSubspaces;
         numDimInputSubspace;
         numDimReferenceSubspace;
         trueTestLabels;
     end
-
+    
     methods
         function obj = MSM(trainData, numDimReferenceSubspace, numDimInputSubspace, labels)
-            obj.trainData = trainData;
+            trainData = cvlNormalize(trainData);
             obj.numDimReferenceSubspace = numDimReferenceSubspace;
             obj.numDimInputSubspace = numDimInputSubspace;
             obj.trueTestLabels = labels;
-            subspaces = cvlBasisVector(obj.trainData,...
-                                         obj.numDimReferenceSubspace);
+            subspaces = cvlBasisVector(trainData,...
+                obj.numDimReferenceSubspace);
             obj.referenceSubspaces = subspaces;
         end
-
-
+        
+        
         % Returns the predicted labels for the test data
         function prediction = predict(obj, testData)
             similarityScores = obj.getSimilarityScores(testData);
             eval = ModelEvaluation(similarityScores, obj.trueTestLabels, obj.name);
             prediction = eval.predicted_labels;
         end
-
+        
         % Returns the similarity scores for the test data (same as probabilities)
         function probabilities = predictProb(obj, testData)
             probabilities = obj.getSimilarityScores(testData);
         end
-
+        
         % Returns the evaluation object for the test data
         function eval = evaluate(obj, testData)
             similarityScores = obj.getSimilarityScores(testData);
             eval = ModelEvaluation(similarityScores, obj.trueTestLabels, obj.name);
         end
     end
-
+    
     methods (Access = private)
         function scores = getSimilarityScores(obj, testData)
+            testData = cvlNormalize(testData);
             inputSubspace = cvlBasisVector(testData,...
-                                               obj.numDimInputSubspace);
+                obj.numDimInputSubspace);
             similarities = cvlCanonicalAngles(obj.referenceSubspaces,...
-             inputSubspace);
+                inputSubspace);
             scores = similarities(:, :, end, end);
         end
     end