cast

Cast variable to different data type

Syntax

Description

example

b = cast(a,'like',p) converts a to the same numerictype, complexity (real or complex), and fimath as p. If a and p are both real, then b is also real. Otherwise, b is complex.

Examples

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Convert an int8 Value to Fixed Point

Define a scalar 8–bit integer.

a = int8(5);

Create a signed fi object with word length of 24 and fraction length of 12.

p = fi([],1,24,12);

Convert a to fixed point with numerictype, complexity (real or complex), and fimath of the specified fi object, p.

b = cast(a, 'like', p)
b = 

     5

          DataTypeMode: Fixed-point: binary point scaling
            Signedness: Signed
            WordLength: 24
        FractionLength: 12

Match Data Type and Complex Nature of p

Define a complex fi object.

p = fi( [1+2i 3i],1,24,12);

Define a scalar 8–bit integer.

a = int8(5);

Convert a to the same data type and complexity as p.

b = cast(a,'like',p)
b = 

   5.0000 + 0.0000i

          DataTypeMode: Fixed-point: binary point scaling
            Signedness: Signed
            WordLength: 24
        FractionLength: 12

Convert an Array to Fixed Point

Define a 2-by-3 matrix of ones.

A = ones(2,3);

Create a signed fi object with word length of 16 and fraction length of 8.

p = fi([],1,16,8);

Convert A to the same data type and complexity (real or complex) as p.

B = cast(A,'like',p)
B = 

     1     1     1
     1     1     1

          DataTypeMode: Fixed-point: binary point scaling
            Signedness: Signed
            WordLength: 16
        FractionLength: 8

Write MATLAB Code That Is Independent of Data Types

Write a MATLAB® algorithm that you can run with different data types without changing the algorithm itself. To reuse the algorithm, define the data types separately from the algorithm.

This approach allows you to define a baseline by running the algorithm with floating-point data types. You can then test the algorithm with different fixed-point data types and compare the fixed-point behavior to the baseline without making any modifications to the original MATLAB code.

Write a MATLAB function, my_filter, that takes an input parameter, T, which is a structure that defines the data types of the coefficients and the input and output data.

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

Write a MATLAB function, zeros_ones_cast_example, that calls my_filter with a floating-point step input and a fixed-point step input, and then compares the results.

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

Input Arguments

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a — Variable that you want to cast to a different data typefi object | numeric variable

Variable, specified as a fi object or numeric variable.

Complex Number Support: Yes

p — Prototypefi object | numeric variable

Prototype, specified as a fi object or numeric variable. To use the prototype to specify a complex object, you must specify a value for the prototype. Otherwise, you do not need to specify a value.

Complex Number Support: Yes

More About

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Tips

Using the b = cast(a,'like',p) syntax to specify data types separately from algorithm code allows you to:

  • Reuse your algorithm code with different data types.

  • Keep your algorithm uncluttered with data type specifications and switch statements for different data types.

  • Improve readability of your algorithm code.

  • Switch between fixed-point and floating-point data types to compare baselines.

  • Switch between variations of fixed-point settings without changing the algorithm code.

See Also

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