function [y,z] = my_filter(b,a,x,z,T)
    % Cast the coefficients to the coefficient type
    b = cast(b,'like',T.coeffs);
    a = cast(a,'like',T.coeffs);
    % Create the output using zeros with the data type
    y = zeros(size(x),'like',T.data);
    for i = 1:length(x)
        y(i) = b(1)*x(i) + z(1);
        z(1) = b(2)*x(i) + z(2) - a(2) * y(i);
        z(2) = b(3)*x(i)        - a(3) * y(i);
    end
end

function zeros_ones_cast_example

    % Define coefficients for a filter with specification
    % [b,a] = butter(2,0.25)
    b = [0.097631072937818   0.195262145875635   0.097631072937818];
    a = [1.000000000000000  -0.942809041582063   0.333333333333333];

    % Define floating-point types
    T_float.coeffs = double([]);
    T_float.data   = double([]);

    % Create a step input using ones with the 
    % floating-point data type
    t = 0:20;
    x_float = ones(size(t),'like',T_float.data);

    % Initialize the states using zeros with the 
    % floating-point data type
    z_float = zeros(1,2,'like',T_float.data);

    % Run the floating-point algorithm
    y_float = my_filter(b,a,x_float,z_float,T_float);
     
    % Define fixed-point types
    T_fixed.coeffs = fi([],true,8,6);
    T_fixed.data   = fi([],true,8,6);

    % Create a step input using ones with the 
    % fixed-point data type
    x_fixed = ones(size(t),'like',T_fixed.data);

    % Initialize the states using zeros with the 
    % fixed-point data type
    z_fixed = zeros(1,2,'like',T_fixed.data);

    % Run the fixed-point algorithm
    y_fixed = my_filter(b,a,x_fixed,z_fixed,T_fixed);
     
    % Compare the results
    coder.extrinsic('clf','subplot','plot','legend')
    clf
    subplot(211)
    plot(t,y_float,'co-',t,y_fixed,'kx-')
    legend('Floating-point output','Fixed-point output')
    title('Step response')
    subplot(212)
    plot(t,y_float - double(y_fixed),'rs-')
    legend('Error')
    figure(gcf)
end