30 lines
633 B
Mathematica
30 lines
633 B
Mathematica
|
function scores = kp_predict(alphas, b, d, X, Y, tst_data)
|
||
|
|
% alphas, b: sample weights and bias term for classification
|
||
|
|
% d: degree of polynomial kernel <x,y>^d
|
||
|
|
% X,Y: training data and label
|
||
|
|
% tst_data: test data for prediction
|
||
|
|
% scores: the perdiction scores.
|
||
|
|
|
||
|
|
N = size(X,1);
|
||
|
|
%TO DO: calculate the predictions
|
||
|
|
|
||
|
|
[tst_size, ~] = size(tst_data);
|
||
|
|
scores = zeros(tst_size, 1);
|
||
|
|
for t = 1 : tst_size
|
||
|
|
xt = tst_data(t, :);
|
||
|
|
|
||
|
|
sum = 0;
|
||
|
|
for i = 1 : N
|
||
|
|
ai = alphas(i);
|
||
|
|
yi = Y(i);
|
||
|
|
xi = X(i, :);
|
||
|
|
sum = sum + ai * yi * dot(xi, xt) ^ d;
|
||
|
|
end
|
||
|
|
sum = sum + b;
|
||
|
|
|
||
|
|
scores(t) = sign(sum);
|
||
|
|
|
||
|
|
end
|
||
|
|
|
||
|
|
|
||
|
|
%********************************
|