diff toolboxes/distance_learning/mlr/util/soft_classify.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/distance_learning/mlr/util/soft_classify.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,99 @@
+function Ypredict = soft_classify(W, test_k, Xtrain, Ytrain, Xtest, Testnorm)
+% Ypredict = soft_classify(W, test_k, Xtrain, Ytrain, Xtest, Testnorm)
+%
+%       W       = d-by-d positive semi-definite matrix
+%       test_k  = k-value to use for KNN
+%       Xtrain  = d-by-n matrix of training data
+%       Ytrain  = n-by-1 vector of training labels
+%       Xtest   = d-by-m matrix of testing data
+%       Testnorm= m-by-#kernels vector of k(i,i) for each i in test
+%
+
+    addpath('cuttingPlane', 'distance', 'feasible', 'initialize', 'loss', ...
+            'metricPsi', 'regularize', 'separationOracle', 'util');
+
+    [d, nTrain, nKernel] = size(Xtrain);
+    nTest       = size(Xtest, 2);
+    test_k      = min(test_k, nTrain);
+
+    if nargin < 7
+        Testnorm = [];
+    end
+
+    % Build the distance matrix
+    [D, I] = mlr_test_distance(W, Xtrain, Xtest, Testnorm);
+
+    % Compute label agreement
+    Ypredict  = histc(Ytrain(I(1:test_k,:)), unique(Ytrain)');
+
+end
+
+
+function [D,I] = mlr_test_distance(W, Xtrain, Xtest, Testnorm)
+
+    % CASES:
+    %   Raw:                        W = []
+    
+    %   Linear, full:               W = d-by-d
+    %   Single Kernel, full:        W = n-by-n
+    %   MKL, full:                  W = n-by-n-by-m
+
+    %   Linear, diagonal:           W = d-by-1
+    %   Single Kernel, diagonal:    W = n-by-1
+    %   MKL, diag:                  W = n-by-m
+    %   MKL, diag-off-diag:         W = m-by-m-by-n
+    
+    [d, nTrain, nKernel] = size(Xtrain);
+    nTest = size(Xtest, 2);
+
+    if isempty(W)
+        % W = []  => native euclidean distances
+        D = mlr_test_distance_raw(Xtrain, Xtest, Testnorm);
+
+    elseif size(W,1) == d && size(W,2) == d
+        % We're in a full-projection case
+        D = setDistanceFullMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain);
+
+    elseif size(W,1) == d && size(W,2) == nKernel
+        % We're in a simple diagonal case
+        D = setDistanceDiagMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain);
+
+    elseif size(W,1) == nKernel && size(W,2) == nKernel && size(W,3) == nTrain
+        % We're in DOD mode
+        D = setDistanceDODMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain);
+
+    else
+        % Error?
+        error('Cannot determine metric mode.');
+
+    end
+    
+    D       = full(D(1:nTrain, nTrain + (1:nTest)));
+    [v,I]   = sort(D, 1);
+end
+
+
+
+function D = mlr_test_distance_raw(Xtrain, Xtest, Testnorm)
+
+    [d, nTrain, nKernel] = size(Xtrain);
+    nTest = size(Xtest, 2);
+
+    if isempty(Testnorm)
+        % Not in kernel mode, compute distances directly
+        D = 0;
+        for i = 1:nKernel
+            D = D + setDistanceDiag([Xtrain(:,:,i) Xtest(:,:,i)], ones(d,1), ...
+                                    nTrain + (1:nTest), 1:nTrain);
+        end
+    else
+        % We are in kernel mode
+        D = sparse(nTrain + nTest, nTrain + nTest);
+        for i = 1:nKernel
+            Trainnorm = diag(Xtrain(:,:,i));
+            D(1:nTrain, nTrain + (1:nTest)) = D(1:nTrain, nTrain + (1:nTest)) ...
+                +  bsxfun(@plus, Trainnorm, bsxfun(@plus, Testnorm(:,i)', -2 * Xtest(:,:,i)));
+        end
+    end
+end
+