函数plotboundary 源程序：
function plotboundary(labels, features, model, varargin)
% PLOTBOUNDARY
Plot the SVM classification boundary in Exercise 8
%
%
plotboundary(labels, features, model, fill_on) plots the training data
%
and decision boundary, given a model produced by LIBSVM
%
%
The parameter 'fill_on' is a boolean that indicates whether a filled-in
%
contour map should be produced.
figure; hold on
% Make classification predictions over a grid of values
xplot = linspace(min(features(:,1)), max(features(:,1)), 100)';
yplot = linspace(min(features(:,2)), max(features(:,2)), 100)';
[X, Y] = meshgrid(xplot, yplot);
vals = zeros(size(X));
for i = 1:size(X, 2)
x = [X(:,i),Y(:,i)];
% Need to use evalc here to suppress LIBSVM accuracy printouts
[T,predicted_labels, accuracy, decision_values] = ...
evalc('svmpredict(ones(size(x(:,1))), x, model)');
clear T;
vals(:,i) = decision_values;
end
% Plot the SVM boundary
colormap bone
if (size(varargin, 2) == 1) && (varargin{1} == 't')
contourf(X,Y, vals, 50, 'LineStyle', 'none');
end
contour(X,Y, vals, [0 0], 'LineWidth', 2, 'Color', 'k');
% Plot the training data on top of the boundary
pos = find(labels == 1);
neg = find(labels == -1);
plot(features(pos,1), features(pos,2), 'ko', 'MarkerFaceColor', 'r'); hold on;
plot(features(neg,1), features(neg,2), 'ko', 'MarkerFaceColor', 'g')
Ex8b.txt 分类 主程序:
% Exercise 8: SVM Nonlinear classification
clear all; close all; clc
% Load training features and labels
[y, x] = read_sparse('ex8a.txt');
gamma = 100;
% Libsvm options
% -s 0 : classification
% -t 2 : RBF kernel
% -g : gamma in the RBF kernel
model = svmtrain(y, x, sprintf('-s 0 -t 2 -g %g', gamma));
% Display training accuracy
[predicted_label, accuracy, decision_values] = svmpredict(y, x, model);
% Plot training data and decision boundary
plotboundary(y, x, model);
title(sprintf('\\gamma = %g', gamma), 'FontSize', 14);
结果：
ex8b.txt 分类
主程序：
% Exercise 8: SVM Nonlinear classification
clear all; close all; clc
% Load training features and labels
[y, x] = read_sparse('ex8b.txt');
gamma = n;
% Libsvm options
% -s 0 : classification
% -t 2 : RBF kernel
% -g : gamma in the RBF kernel
model = svmtrain(y, x, sprintf('-s 0 -t 2 -g %g', gamma));
% Display training accuracy
[predicted_label, accuracy, decision_values] = svmpredict(y, x, model);
% Plot training data and decision boundary
plotboundary(y, x, model);
title(sprintf('\\gamma = %g', gamma), 'FontSize', 14);
结果：
当 n=1 时：
当 n=10 时：
当 n=100 时
当 n=1000 时