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Why Do Floating-Point Comparisons in Go Sometimes Yield Unexpected Results?

Patricia Arquette
Release: 2024-12-04 11:33:09
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Why Do Floating-Point Comparisons in Go Sometimes Yield Unexpected Results?

Floating-Point Precision Differences in Go: Constants vs. Variables

In Go, floating-point literals and constants maintain arbitrary precision until assigned to a specific type. However, certain operations and assignments introduce precision differences that can be unexpected.

Consider the following code:

package main

import (
    "fmt"
)

func main() {
    x := 10.1

    fmt.Println("x == 10.1:        ", x == 10.1)
    fmt.Println("x*3.0 == 10.1*3.0:", x*3.0 == 10.1*3.0)
}
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This code produces the output:

x == 10.1:         true
x*3.0 == 10.1*3.0: false
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Why are these expressions not equal, despite performing the same floating-point operation?

Untyped Constants and Variables

Number literals and constants in Go are untyped, with unlimited precision. Upon assignment to a variable, such as x := 10.1, the literal is converted to the variable's type (float64 in this case) and loses some precision.

Full Precision Constants

In contrast, literal expressions like 10.1*3.0 maintain full precision before evaluation. When assigned to a variable, they are immediately converted to the variable's type, ensuring precision is maintained.

Precision Considerations

Understanding the differences in precision handling can be crucial for accurate floating-point calculations. For example, when comparing values to a specified tolerance, it is important to use literals or expressions with full precision to avoid false positives.

Documentation

This behavior is well-documented in the Go blog article "Constants" under the "Floats" header, which explains that while numeric constants have arbitrary precision, they must fit within the range of the variable they are assigned to.

Conclusion

The difference in precision between floating-point constants vs. variables in Go is by design, allowing for greater flexibility and performance trade-offs in floating-point operations. By understanding these precision differences, programmers can optimize their code for accuracy and efficiency.

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