how to plot a gaussian 1D in matlab

for k = 1 : K 
    ax = linspace(min_x,max_x,100); 
    y = my_gaussian(x,means,vars); 
    plot(ax,y);
end

Answer 1

[M]

 You can use Matlab function to construct Gaussian function :

 x = 0:0.1:10;
y = gaussmf(x,[2 5]);
plot(x,y)

https://fr.mathworks.com/help/fuzzy/gaussmf.html

[Gadadhar Sahoo]

how to plot using both means and variance for gaussian 1D

[M]

Did you read the documentation ? Those are the second parameter you give to the function gaussmf(x,[sigma,mean]) .

[Gadadhar Sahoo]

i am not getting the gaussian bell curve..here is my code

clc clear load fisheriris [N, M] = size(meas); x = meas(:,1)'; max_x = max(max((x))); min_x = min(min(x)); K = 3; means = min_x + (max_x - min_x)*rand(1, K); vars = ones(1, K); prior = ones(1,K)/K; prob = zeros(N, K); for g = 1 : 1 for p = 1 : N for k = 1 : K gaussian = (1/sqrt(2*pi*vars(k)))*exp(-(x(p)-means(k)).^2/(2*vars(k))); prob(p,k) = gaussian* prior(k); end sum_probs = sum(prob(p,:)); prob(p,:) = prob(p,:)/sum_probs; end for k = 1 : K means(k) = sum(prob(:,k)'.*x)/N; vars(k) = sum(prob(:,k)'.*(x - means(k)).^2)/N; prior(k) = sum(prob(:,k))/N; end end figure scatter(x,zeros(1,N)); hold on for k = 1 : K ax = linspace(min_x,max_x,100); y = gaussmf(ax,[means,vars]); plot(ax,y);

end

[Chad MacDonald]

The gaussmf function evaluates a Gaussian membership function for a fuzzy logic system, which is not the same thing as a Gaussian distribution. For more information on Gaussian probability distributions, see Normal Distribution (Statistics and Machine Learning Toolbox).

Answer 2

[Adam Danz]

Fully parameterized gaussian function (no toolboxes needed)

If you don't have the Fuzzy Logic toolbox (and therefore do not have access to gaussmf), here's a simple anonymous function to create a paramaterized gaussian curve.

gaus = @(x,mu,sig,amp,vo)amp*exp(-(((x-mu).^2)/(2*sig.^2)))+vo;

• x is an array of x-values.
• mu is the mean
• sig is the standard deviation
• amp is the (positive or negative)
• vo is the vertical offset from baseline (positive or negative)

To add noise along the y-axis of the guassian,

y = gaus(___); 
yh = y + randn(size(y))*amp*.10; % noise is 10% of the amp

The doc page on the Normal Distribution may also be helpful.

Demo

x = linspace(-5,25,100);
mu = 10;
sig = 5;
amp = 9;
vo = -5; 
y = gaus(x,mu,sig,amp,vo);
% Plot gaussian
plot(x, y, 'b-', 'LineWidth',3)
% Add noise
yh = y + randn(size(y))*amp*.10;
hold on
plot(x, yh, 'ro','markersize', 4)
grid on
title(sprintf('Guassian with \\mu=%.1f \\sigma=%.1f amp=%.1f vo=%.1f', ...
    mu, sig, amp, vo))

Comparison with gaussmf()

x = linspace(-15,10,100);
mu = -5.8;
sig = 2.5;
amp = 1; 
vo = 0; 
y = gaus(x,mu,sig,amp,vo);
% Plot gaussian from custom function
plot(x, y, 'b-', 'LineWidth',3, 'DisplayName','Custom function')
% Plot gaussian from custom function
y2 = gaussmf(x,[sig,mu]); 
hold on
plot(x, y2, 'r--', 'LineWidth',4, 'DisplayName','gaussmf()')
grid on
title(sprintf('Guassian with \\mu=%.1f \\sigma=%.1f amp=%.1f vo=%.1f', ...
    mu, sig, amp, vo))
legend()

[Kibum Nam]

very useful. thanks a lot.