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55 lines
1.6 KiB
Matlab
55 lines
1.6 KiB
Matlab
function pred = svmPredict(model, X)
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%SVMPREDICT returns a vector of predictions using a trained SVM model
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%(svmTrain).
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% pred = SVMPREDICT(model, X) returns a vector of predictions using a
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% trained SVM model (svmTrain). X is a mxn matrix where there each
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% example is a row. model is a svm model returned from svmTrain.
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% predictions pred is a m x 1 column of predictions of {0, 1} values.
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%
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% Check if we are getting a column vector, if so, then assume that we only
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% need to do prediction for a single example
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if (size(X, 2) == 1)
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% Examples should be in rows
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X = X';
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end
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% Dataset
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m = size(X, 1);
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p = zeros(m, 1);
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pred = zeros(m, 1);
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if strcmp(func2str(model.kernelFunction), 'linearKernel')
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% We can use the weights and bias directly if working with the
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% linear kernel
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p = X * model.w + model.b;
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elseif strfind(func2str(model.kernelFunction), 'gaussianKernel')
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% Vectorized RBF Kernel
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% This is equivalent to computing the kernel on every pair of examples
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X1 = sum(X.^2, 2);
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X2 = sum(model.X.^2, 2)';
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K = bsxfun(@plus, X1, bsxfun(@plus, X2, - 2 * X * model.X'));
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K = model.kernelFunction(1, 0) .^ K;
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K = bsxfun(@times, model.y', K);
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K = bsxfun(@times, model.alphas', K);
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p = sum(K, 2);
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else
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% Other Non-linear kernel
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for i = 1:m
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prediction = 0;
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for j = 1:size(model.X, 1)
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prediction = prediction + ...
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model.alphas(j) * model.y(j) * ...
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model.kernelFunction(X(i,:)', model.X(j,:)');
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end
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p(i) = prediction + model.b;
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end
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end
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% Convert predictions into 0 / 1
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pred(p >= 0) = 1;
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pred(p < 0) = 0;
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end
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