Matlab 数据的高斯分块

我有一个简单的问题，如何对数据点进行高斯分类。假设在X=100时，我检测到5000个电子，但我的半高宽大约是4个点。在matlab中，是否有可能用以X=100为中心的高斯分布来存储5000个电子。比如X=99和X=101之间的2500个电子，95和105之间的5000个电子

听起来您在一个点上有一个测量（

X=100

，

e=5000

），并且还知道半高宽的值（

FWHM=4

）

如果确实是这样，您可以这样计算

sigma

：

sigma = FWHM/ 2/sqrt(2*log(2));

[N, binCtr] = hist(sigma*randn(e,1) + X, Nbins);

% Example data
FWHM = 4;
e = 100000;
X = 100;
Nbins = 100;

% your std. dev.
sigma = FWHM/ 2/sqrt(2*log(2));

% Find where to start with the bin edges. That is, find the point 
% where the PDF indicates that at most 1 electron is expected to fall 
f = @(x, mu, sigma) exp(-0.5*((x-mu)/sigma).^2)/sigma/sqrt(2*pi);
g = @(y) quadgk(@(x)f(x,X,sigma), -inf, y)*e - 1; 
h = fzero(g, X-FWHM*3);

% Create initial bin edges
binEdges = [-inf  linspace(h, 2*X-h, Nbins-2)  +inf];

% Bin electrons in batches
c = e;
done = false;
step = 5e3;
Nout = zeros(Nbins,1);
while ~done

    % electrons still to be binned
    c = c - step;

    % Last step
    if c <= 0
        step = c+step;
        c = 0;
        done = true;
    end

    % Bin the next batch
    N = histc(sigma*randn(step,1) + X, binEdges);   
    Nout = Nout + N;

end

% Bin edges must now be re-defined
binEdges =[...
    2*binEdges(2)-binEdges(3),... 
    binEdges(2:end-1),...
    2*binEdges(end-1)-binEdges(end-2)];

你可以这样做垃圾箱：

sigma = FWHM/ 2/sqrt(2*log(2));

[N, binCtr] = hist(sigma*randn(e,1) + X, Nbins);

% Example data
FWHM = 4;
e = 100000;
X = 100;
Nbins = 100;

% your std. dev.
sigma = FWHM/ 2/sqrt(2*log(2));

% Find where to start with the bin edges. That is, find the point 
% where the PDF indicates that at most 1 electron is expected to fall 
f = @(x, mu, sigma) exp(-0.5*((x-mu)/sigma).^2)/sigma/sqrt(2*pi);
g = @(y) quadgk(@(x)f(x,X,sigma), -inf, y)*e - 1; 
h = fzero(g, X-FWHM*3);

% Create initial bin edges
binEdges = [-inf  linspace(h, 2*X-h, Nbins-2)  +inf];

% Bin electrons in batches
c = e;
done = false;
step = 5e3;
Nout = zeros(Nbins,1);
while ~done

    % electrons still to be binned
    c = c - step;

    % Last step
    if c <= 0
        step = c+step;
        c = 0;
        done = true;
    end

    % Bin the next batch
    N = histc(sigma*randn(step,1) + X, binEdges);   
    Nout = Nout + N;

end

% Bin edges must now be re-defined
binEdges =[...
    2*binEdges(2)-binEdges(3),... 
    binEdges(2:end-1),...
    2*binEdges(end-1)-binEdges(end-2)];

其中，

N

是每个料仓中的电子量，

binCtr

是料仓中心，

Nbins

是要使用的料仓量

如果电子的数量变大，你可能会耗尽内存。在这种情况下，最好做同样的事情，但批量较小，如：

sigma = FWHM/ 2/sqrt(2*log(2));

[N, binCtr] = hist(sigma*randn(e,1) + X, Nbins);

% Example data
FWHM = 4;
e = 100000;
X = 100;
Nbins = 100;

% your std. dev.
sigma = FWHM/ 2/sqrt(2*log(2));

% Find where to start with the bin edges. That is, find the point 
% where the PDF indicates that at most 1 electron is expected to fall 
f = @(x, mu, sigma) exp(-0.5*((x-mu)/sigma).^2)/sigma/sqrt(2*pi);
g = @(y) quadgk(@(x)f(x,X,sigma), -inf, y)*e - 1; 
h = fzero(g, X-FWHM*3);

% Create initial bin edges
binEdges = [-inf  linspace(h, 2*X-h, Nbins-2)  +inf];

% Bin electrons in batches
c = e;
done = false;
step = 5e3;
Nout = zeros(Nbins,1);
while ~done

    % electrons still to be binned
    c = c - step;

    % Last step
    if c <= 0
        step = c+step;
        c = 0;
        done = true;
    end

    % Bin the next batch
    N = histc(sigma*randn(step,1) + X, binEdges);   
    Nout = Nout + N;

end

% Bin edges must now be re-defined
binEdges =[...
    2*binEdges(2)-binEdges(3),... 
    binEdges(2:end-1),...
    2*binEdges(end-1)-binEdges(end-2)];

%示例数据
半高宽=4；
e=100000；
X=100；
Nbins=100；
%你的性病患者。
西格玛=FWHM/2/sqrt（2*log（2））；
%找到从箱子边缘开始的位置。也就是说，找到重点
%其中PDF表示预计最多有1个电子下落
f=@（x，mu，sigma）exp（-0.5*（（x-mu）/sigma）。^2）/sigma/sqrt（2*pi）；
g=@（y）quadgk（@（x）f（x，x，sigma），-inf，y）*e-1；
h=f0（g，X-FWHM*3）；
%创建初始箱子边
binEdges=[-inf-linspace（h，2*X-h，Nbins-2）+inf]；
%分批存储电子
c=e；
完成=错误；
步骤=5e3；
努特=零（Nbins，1）；
完成时
%电子仍然需要被储存
c=c-阶跃；
%最后一步
如果c，我想这听起来像我想做的。我有一个关于半高宽的近似概念，想把电子放在离散点的边界之间。我会试试这个，然后再打给你。非常感谢。如果这个数字真的很大呢。假设e=12345678。当我试图存储这么多数据时，我的matlab崩溃了。