comparison toolboxes/distance_learning/mlr/mlr_demo.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function mlr_demo()
+
+    display('Loading Wine data');
+    load Wine;
+
+    % z-score the input dimensions
+    display('z-scoring features');
+    X = zscore(X')';
+
+    [d,n] = size(X);
+    
+    % Generate a random training/test split
+    display('Generating a 80/20 training/test split');
+    P       = randperm(n);
+    Xtrain  = X(:,P(1:floor(0.8 * n)));
+    Ytrain  = Y(P(1:floor(0.8*n)));
+    Xtest   = X(:,P((1+floor(0.8*n)):end));
+    Ytest   = Y(P((1+floor(0.8*n)):end));
+
+
+    % Optimize W for AUC
+    C = 1e-2;
+    display(sprintf('Training with C=%.2e, Delta=mAP', C));
+    [W, Xi, Diagnostics] = mlr_train(Xtrain, Ytrain, C, 'map');
+%     [W, Xi, Diagnostics] = mlr_train_primal(Xtrain, Ytrain, C, 'map');
+
+    display('Test performance in the native (normalized) metric');
+    mlr_test(eye(d), 3, Xtrain, Ytrain, Xtest, Ytest)
+
+    display('Test performance with MLR metric');
+    mlr_test(W, 3, Xtrain, Ytrain, Xtest, Ytest)
+
+    % Scatter-plot
+    figure;
+    subplot(1,2,1), drawData(eye(d), Xtrain, Ytrain, Xtest, Ytest), title('Native metric (z-scored)');
+    subplot(1,2,2), drawData(W, Xtrain, Ytrain, Xtest, Ytest), title('Learned metric (MLR-mAP)');
+
+    Diagnostics
+
+end
+
+
+function drawData(W, Xtrain, Ytrain, Xtest, Ytest);
+
+    n = length(Ytrain);
+    m = length(Ytest);
+
+    if size(W,2) == 1
+        W = diag(W);
+    end
+    % PCA the learned metric
+    Z = [Xtrain Xtest];
+    A = Z' * W * Z;
+    [v,d] = eig(A);
+
+    L = (d.^0.5) * v';
+    L = L(1:2,:);
+
+    % Draw training points
+    hold on;
+    trmarkers = {'b+', 'r+', 'g+'};
+    tsmarkers = {'bo', 'ro', 'go'};
+    for i = min(Ytrain):max(Ytrain)
+        points = find(Ytrain == i);
+        scatter(L(1,points), L(2,points), trmarkers{i});
+        points = n + find(Ytest == i);
+        scatter(L(1,points), L(2,points), tsmarkers{i});
+    end
+    legend({'Training', 'Test'});
+end