matlab 高斯过程回归

atmip9wb 于 6个月前发布在 Matlab

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我正在编写一个高斯过程回归算法。下面是代码：

% Data generating function

fh = @(x)(2*cos(2*pi*x/10).*x);

% range

x = -5:0.01:5;
N = length(x);

% Sampled data points from the generating function

M = 50;
selection = boolean(zeros(N,1));
j = randsample(N, M);

% mark them

selection(j) = 1;
Xa = x(j);

% compute the function and extract mean

f = fh(Xa) - mean(fh(Xa));
sigma2 = 1;

% computing the interpolation using all x's
% It is expected that for points used to build the GP cov. matrix, the
% uncertainty is reduced...

K = squareform(pdist(x'));
K = exp(-(0.5*K.^2)/sigma2);

% upper left corner of K

Kaa = K(selection,selection);

% lower right corner of K

Kbb = K(~selection,~selection);

% upper right corner of K

Kab = K(selection,~selection);

% mean of posterior

m = Kab'*inv(Kaa+0.001*eye(M))*f';

% cov. matrix of posterior

D = Kbb - Kab'*inv(Kaa + 0.001*eye(M))*Kab;

% sampling M functions from from GP

[A,B,C] = svd(Kaa);
F0 = A*sqrt(B)*randn(M,M);
% mean from GP using sampled points

F0m = mean(F0,2);
F0d = std(F0,0,2);

%%
% put together data and estimation

F = zeros(N,1);
S = zeros(N,1);
F(selection) = f' + F0m;
S(selection) = F0d;

% sampling M function from posterior

[A,B,C] = svd(D);
a = A*sqrt(B)*randn(N-M,M);
% mean from posterior GPs

Fm = m + mean(a,2);
Fmd = std(a,0,2);
F(~selection) = Fm;
S(~selection) = Fmd;

%%

figure;
% show what we got...

plot(x, F, ':r', x, F-2*S, ':b', x, F+2*S, ':b'), grid on;
hold on;
% show points we got

plot(Xa, f, 'Ok');
% show the whole curve

plot(x, fh(x)-mean(fh(x)), 'k');
grid on;

字符串
我希望得到一个很好的数字，其中未知数据点的不确定性很大，而采样数据点周围的不确定性很小。我得到了一个奇怪的数字，更奇怪的是采样数据点周围的不确定性比其他数据点更大。我做错了什么？

matlab

来源：https://stackoverflow.com/questions/31032154/gaussian-process-regression

1条答案

按热度按时间

pobjuy321#

你的代码有几处错误。以下是最重要的几点：

导致一切出错的主要错误是f的索引。您正在定义Xa = x(j)，但实际上应该执行Xa = x(selection)，以便索引与您在内核矩阵K上使用的索引一致。
减去样本均值f = fh(Xa) - mean(fh(Xa))没有任何意义，并且会使图中的圆偏离实际函数。（如果选择减去某个值，它应该是一个固定的数字或函数，并且不依赖于随机采样的观测值。）
您应该直接从m和D计算后验均值和方差;不需要从后验中采样，然后获得这些样本的估计值。

这里是修改后的版本的脚本与上述几点固定。

%% Init
% Data generating function
fh = @(x)(2*cos(2*pi*x/10).*x);
% range
x = -5:0.01:5;
N = length(x);
% Sampled data points from the generating function
M = 5;
selection = boolean(zeros(N,1));
j = randsample(N, M);
% mark them
selection(j) = 1;
Xa = x(selection);

%% GP computations
% compute the function and extract mean
f = fh(Xa);
sigma2 = 2;
sigma_noise = 0.01;
var_kernel = 10;
% computing the interpolation using all x's
% It is expected that for points used to build the GP cov. matrix, the
% uncertainty is reduced...
K = squareform(pdist(x'));
K = var_kernel*exp(-(0.5*K.^2)/sigma2);
% upper left corner of K
Kaa = K(selection,selection);
% lower right corner of K
Kbb = K(~selection,~selection);
% upper right corner of K
Kab = K(selection,~selection);
% mean of posterior
m = Kab'/(Kaa + sigma_noise*eye(M))*f';
% cov. matrix of posterior
D = Kbb - Kab'/(Kaa + sigma_noise*eye(M))*Kab;

%% Plot
figure;
grid on;
hold on;
% GP estimates
plot(x(~selection), m);
plot(x(~selection), m + 2*sqrt(diag(D)), 'g-');
plot(x(~selection), m - 2*sqrt(diag(D)), 'g-');
% Observations
plot(Xa, f, 'Ok');
% True function
plot(x, fh(x), 'k');

字符串
由此生成的图，其中包含5个随机选择的观测值，真实函数以黑色显示，后验均值以蓝色显示，置信区间以绿色显示。
x1c 0d1x的数据

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matlab 高斯过程回归

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