svr代码和RBF核函数

close all; clear; clc; %%% ************************************************ %%% ************ step 0: load data **************** display('step 0: load data...'); % train_x = [1 2 3 4 5 6 7 8]; % each sample arranged as a column of train_x % train_y = 2 * train_x; train_x = rand(5, 10); train_y = 2 * train_x; test_x = train_x; test_y = train_y; %% from matlab % rbf = newrb(train_x, train_y); % output = rbf(test_x); %%% ************************************************ %%% ******** step 1: initialize parameters ******** display('step 1: initialize parameters...'); numSamples = size(train_x, 2); rbf.inputSize = size(train_x, 1); rbf.hiddenSize = numSamples; % num of Radial Basis function rbf.outputSize = size(train_y, 1); rbf.alpha = 0.1; % learning rate (should not be large!) %% centre of RBF for i = 1 : rbf.hiddenSize % randomly pick up some samples to initialize centres of RBF index = randi([1, numSamples]); %取1-10范围内的伪随机数 rbf.center(:, i) = train_x(:, index); end %% delta of RBF rbf.delta = rand(1, rbf.hiddenSize); %% weight of RBF r = 1.0; % random number between [-r, r] rbf.weight = rand(rbf.outputSize, rbf.hiddenSize) * 2 * r - r; %%% ************************************************ %%% ************ step 2: start training ************ display('step 2: start training...'); maxIter = 400; preCost = 0; for i = 1 : maxIter fprintf(1, 'Iteration %d ,', i); rbf = trainRBF(rbf, train_x, train_y); fprintf(1, 'the cost is %d \n', rbf.cost); curCost = rbf.cost; if abs(curCost - preCost) < 1e-8 disp('Reached iteration termination condition and Termination now!'); break; end preCost = curCost; end %%% ************************************************ %%% ************ step 3: start testing ************ display('step 3: start testing...'); Green = zeros(rbf.hiddenSize, 1); for i = 1 : size(test_x, 2) for j = 1 : rbf.hiddenSize Green(j, 1) = green(test_x(:, i), rbf.center(:, j), rbf.delta(j)); end output(:, i) = rbf.weight * Green; end disp(test_y); disp(output);