IntelliJ IDEA has a code inspection for Kotlin that will warn you if a decimal floating-point literal exceeds the precision of its type (Float or Double). It will suggest an equivalent literal (one that maps to the same binary floating-point number) that has fewer digits, or has the same number of digits but is closer to the floating-point number.
For Doubles for example, every literal over 17-digits should be flagged, since it never takes more than 17 digits to specify any double-precision binary floating-point value. Literals with 16 or 17 digits should be flagged if there is a replacement that is shorter or closer. And no literal with 15 digits or fewer should ever be flagged, since doubles have of 15-digits of precision.
But IntelliJ doesn’t always adhere to that, like when it suggests an 18-digit replacement for a 13-digit literal!
For my recent search for short examples of double rounding errors in decimal to double to float conversions I wrote a Kotlin program to generate and test random decimal strings. While this was sufficient to find examples, I realized I could do a more direct search by generating only decimal strings with the underlying double rounding error bit patterns. I’ll show you the Java BigDecimal based Kotlin program I wrote for this purpose.
In my previous exploration of double rounding errors in decimal to float conversions I showed two decimal numbers that experienced a double rounding error when converted to float (single-precision) through an intermediate double (double-precision). I generated the examples indirectly by setting bit combinations that forced the error, using their corresponding exact decimal representations. As a result, the decimal numbers were long (55 digits each). Mark Dickinson derived a much shorter 17 digit example, but I hadn’t contemplated how to generate even shorter numbers — or whether they existed at all — until Per Vognsen wrote me recently to ask.
The easiest way for me to approach Per’s question was to search for examples, rather than try to find a way to construct them. As such, I wrote a simple Kotlin1 program to generate decimal strings and check them. I tested all float-range (including subnormal) decimal numbers of 9 digits or fewer, and tens of billions of random 10 to 17 digit float-range (normal only) numbers. I found example 7 to 17 digit numbers that, when converted to float through a double, suffer a double rounding error.
The maximum digit counts are useful if you want to print the full decimal value of a floating-point number (worst case format specifier and buffer size) or if you are writing or trying to understand a decimal to floating-point conversion routine (worst case number of input digits that must be converted).
The binary fraction 0.101 converts to the decimal fraction 0.625; the binary fraction 0.1010001 converts to the decimal fraction 0.6328125; the binary fraction 0.00111011011 converts to the decimal fraction 0.23193359375. In each of those examples, the binary fraction converts to a decimal fraction — that is, a terminating decimal representation — that has the same number of digits as the binary fraction has bits.
One digit per bit? We know that’s not true for binary integers. But it is true for binary fractions; every binary fraction of length n has a corresponding equivalent decimal fraction of length n.
This is the reason why you get all those “extra” digits when you print the full decimal value of an IEEE binary floating-point fraction, and why glibc strtod() and Visual C++ strtod() were once broken.
Every double-precision floating-point number can be specified with 17 significant decimal digits or less. A simple way to generate this 17-digit number is to round the full-precision decimal value of the double to 17 digits. For example, the double-precision value 0x1.6d4c11d09ffa1p-3, which in decimal is 1.783677474777478899614635565740172751247882843017578125 x 10-1, can be recovered from the decimal floating-point literal 1.7836774747774789e-1. The extra digits are unnecessary, since they will only take you to the same double.
On the other hand, an arbitrary, arbitrarily long decimal literal rounded or truncated to 17 digits may not convert to the double-precision value it’s supposed to. This is a subtle point, one that has even tripped up implementers of widely used decimal to floating-point conversion routines (glibc strtod() and Visual C++ strtod(), for example).
How many decimal digits of precision does a binary floating-point number have?
For example, does an IEEE single-precision binary floating-point number, or float as it’s known, have 6-8 digits? 7-8 digits? 6-9 digits? 6 digits? 7 digits? 7.22 digits? 6-112 digits? (I’ve seen all those answers on the Web.)
PHP’s base_convert() is a useful function that converts integers between any pair of bases, 2 through 36. However, you might hesitate to use it after reading this vague and mysterious warning in its documentation:
base_convert() may lose precision on large numbers due to properties related to the internal “double” or “float” type used.
The truth is that it works perfectly for integers up to a certain maximum — you just have to know what that is. I will show you this maximum value in each of the 35 bases, and how to check if the values you are using are within this limit.
The 2015 edition of Hour of Code includes a new blocks-based, Star Wars themed coding lesson. In one of the exercises — a simple sprite-based game — you are asked to code a loop that adds 100 to your score every time R2-D2 encounters a Rebel Pilot. But instead of 100, I plugged in a floating-point number; I got the expected “unexpected” results.
I have been writing about the spacing of decimal and binary floating-point numbers, and about how their relative spacing determines whether numbers round-trip between those two bases. I’ve stated an inequality that captures the required spacing, and from it I have derived formulas that specify the number of digits required for round-trip conversions. I have not proven that this inequality holds, but I will prove “half” of it here. (I’m looking for help to prove the other half.)
In my article “7 Bits Are Not Enough for 2-Digit Accuracy” I showed how the relative spacing of decimal and binary floating-point numbers dictates when all conversions between those two bases round-trip. There are two formulas that capture this relationship, and I will derive them in this article. I will also show that it takes one more digit (or bit) of precision to round-trip a floating-point number than it does to round-trip an integer of equal significance.