Undefined function or method 'gaussian1D' for input arguments of type 'double'.

I get this error when i simulate the code "y1 = gaussian1D(x, mu(1), sigma(1)); "Undefined function or method 'gaussian1D' for input arguments of type 'double'. please tell me how can i get its toolbox.

Answer 1

[Ced]

The function gaussian1D does not exist, therefore you cannot call it. If you are looking to get a sample from the 1D Gaussian distribution, use "randn" instead. Note that this is the N(0,1) distribution, so you need to fix it manually, e.g.

sample = mu + sigma*randn(1,1);

where mu is the mean and sigma the standard deviation.

[Star Strider]

Actually, you can just write it as:

sample = mu + sigma*randn;

to get a single value.

[Zishan]

Basically I am trying to perform GMM classification and I am following this example. I have my own data set and I am trying to implement it in this example. Please tell me if 'gaussian1D' is not a function then what does it mean in the following example. The substitute for 'gaussian1D' and similarly 'weightedAverage'

% $Author: ChrisMcCormick $ $Date: 2014/05/19 22:00:00 $ $Revision: 1.3 $

%%====================================================== %% STEP 1a: Generate data from two 1D distributions.

mu1 = 10; % Mean sigma1 = 1; % Sigma m1 = 100; % Number of points

mu2 = 20; sigma2 = 3; m2 = 200;

% Generate the data. X1 = (randn(m1, 1) * sigma1) + mu1; X2 = (randn(m2, 1) * sigma2) + mu2;

X = [X1; X2];

%%===================================================== %% STEP 1b: Plot the data points and their pdfs.

x = [0:0.1:30]; y1 = gaussian1D(x, mu1, sigma1); y2 = gaussian1D(x, mu2, sigma2);

hold off; plot(x, y1, 'b-'); hold on; plot(x, y2, 'r-'); plot(X1, zeros(size(X1)), 'bx', 'markersize', 10); plot(X2, zeros(size(X2)), 'rx', 'markersize', 10);

set(gcf,'color','white') % White background for the figure.

%%==================================================== %% STEP 2: Choose initial values for the parameters.

% Set 'm' to the number of data points. m = size(X, 1);

% Set 'k' to the number of clusters to find. k = 2;

% Randomly select k data points to serve as the means. indeces = randperm(m); mu = zeros(1, k); for (i = 1 : k) mu(i) = X(indeces(i)); end

% Use the overal variance of the dataset as the initial variance for each cluster. sigma = ones(1, k) * sqrt(var(X));

% Assign equal prior probabilities to each cluster. phi = ones(1, k) * (1 / k);

%%=================================================== %% STEP 3: Run Expectation Maximization

% Matrix to hold the probability that each data point belongs to each cluster. % One row per data point, one column per cluster. W = zeros(m, k);

% Loop until convergence. for (iter = 1:1000)

    fprintf('  EM Iteration %d\n', iter);

    %%===============================================
    %%STEP 3a: Expectation
    %
    % Calculate the probability for each data point for each distribution.

    % Matrix to hold the pdf value for each every data point for every cluster.
    % One row per data point, one column per cluster.
    pdf = zeros(m, k);

    % For each cluster...
    for (j = 1 : k)

        % Evaluate the Gaussian for all data points for cluster 'j'.
        pdf(:, j) = gaussian1D(X, mu(j), sigma(j));
    end

    % Multiply each pdf value by the prior probability for each cluster.
    %    pdf  [m  x  k]
    %    phi  [1  x  k]   
    %  pdf_w  [m  x  k]
    pdf_w = bsxfun(@times, pdf, phi);

    % Divide the weighted probabilities by the sum of weighted probabilities for each cluster.
    %   sum(pdf_w, 2) -- sum over the clusters.
    W = bsxfun(@rdivide, pdf_w, sum(pdf_w, 2));

    %%===============================================
    %%STEP 3b: Maximization
    %%Calculate the probability for each data point for each distribution.

    % Store the previous means so we can check for convergence.
    prevMu = mu;    

    % For each of the clusters...
    for (j = 1 : k)

        % Calculate the prior probability for cluster 'j'.
        phi(j) = mean(W(:, j));

        % Calculate the new mean for cluster 'j' by taking the weighted
        % average of *all* data points.
        mu(j) = weightedAverage(W(:, j), X);

        % Calculate the variance for cluster 'j' by taking the weighted
        % average of the squared differences from the mean for all data
        % points.
        variance = weightedAverage(W(:, j), (X - mu(j)).^2);

        % Calculate sigma by taking the square root of the variance.
        sigma(j) = sqrt(variance);
    end

    % Check for convergence.
    % Comparing floating point values for equality is generally a bad idea, but
    % it seems to be working fine.
    if (mu == prevMu)
        break
    end

% End of Expectation Maximization loop. end

%%===================================================== %% STEP 4: Plot the data points and their estimated pdfs.

x = [0:0.1:30]; y1 = gaussian1D(x, mu(1), sigma(1)); y2 = gaussian1D(x, mu(2), sigma(2));

% Plot over the existing figure, using black lines for the estimated pdfs. plot(x, y1, 'k-'); plot(x, y2, 'k-');

[Image Analyst]

It is a function that ChrisMcCormick wrote but was not supplied with the rest of his code, so evidently you do not have it. Contact ChrisMcCormick to get it if you don't want to use randn.