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quantizenumeric

Quantize numeric data

Description

example

y = quantizenumeric(x,s,w,f) quantizes the value specified in x using signedness s, word length w, and fraction length f.

Use quantizenumeric when you want to simulate full-precision arithmetic with doubles and then add quantization at certain steps in your algorithm without casting to fixed-point types.

example

y = quantizenumeric(x,s,w,f,r) also specifies rounding mode r.

example

y = quantizenumeric(x,s,w,f,r,o) also specifies overflow mode o.

Examples

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Quantize the value of pi using a signed numeric type with a word length of 16 bits, a fraction length of 13 bits, and rounding towards positive infinity.

x = pi;
y = quantizenumeric(x,1,16,13,'ceil')
y = 3.1416

Specify a different rounding method. Observe that rounding towards zero affects the quantized value.

x = pi;
y = quantizenumeric(x,1,16,13,'fix')
y = 3.1415

Input Arguments

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Value to quantize, specified as a scalar, vector, matrix, or multidimensional array.

Data Types: double
Complex Number Support: Yes

Signedness of quantized value, specified as either 0 or 'false' (unsigned) or 1 or 'true' (signed).

Data Types: double

Word length of quantized value, specified as a positive scalar integer.

Data Types: double

Fraction length of quantized value, specified as a scalar integer.

Data Types: double

Rounding method to use for quantization, specified as a character vector:

  • 'ceil' — Round towards positive infinity (same as 'ceiling')

  • 'ceiling' — Round towards positive infinity (same as 'ceil')

  • 'convergent'— Convergent rounding

  • 'fix'— Round towards zero (same as 'zero')

  • 'floor'— Round towards negative infinity

  • 'nearest'— Round towards nearest with ties rounding towards positive infinity

  • 'round'— Round towards nearest with ties rounding up in absolute value

  • 'zero'— Round towards zero (same as 'fix')

Data Types: char

Overflow action to use for quantization, specified as either 'saturate' or 'wrap'.

Data Types: char

Output Arguments

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Quantized output value, returned as a scalar, vector, matrix, or multidimensional array. y always has the same dimensions as x and is always a double.

Tips

  • Use quantizenumeric when you want to simulate full-precision arithmetic with doubles and then add quantization at certain steps in your algorithm without casting to fixed-point types.

  • When designing fixed-point algorithms, use cast, zeros, ones, eye, and subsasgn to separate the core algorithm from data type definitions.

Compatibility Considerations

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Behavior changed in R2021b

See Also

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Introduced in R2016a