## Feature #1408

### 0.1.to_r not equal to (1/10)

**Description**

=begin

$ ruby -e 'p 0.1.to_r'

(3602879701896397/36028797018963968)

whereas

$ ruby -e 'p "0.1".to_r'

(1/10)

=end

**Related issues**

### History

#### #1 Updated by Heesob Park over 7 years ago

=begin

2009/4/27 Martin DeMello martindemello@gmail.com:

On Sun, Apr 26, 2009 at 2:51 PM, Heesob Park redmine@ruby-lang.org wrote:

$ ruby -e 'p 0.1.to_r'

(3602879701896397/36028797018963968)whereas

$ ruby -e 'p "0.1".to_r'

(1/10)What, in theory, could be done about this? By the time to_r is

invoked, 0.1 is already a binary float, with the implicit rounding

off.In theory, Float#to_r could be done through Float#to_s#to_r.

Regards,

Park Heesob

=end

#### #2 Updated by Shyouhei Urabe over 7 years ago

=begin

Heesob Park wrote:

2009/4/27 Martin DeMello martindemello@gmail.com:

On Sun, Apr 26, 2009 at 2:51 PM, Heesob Park redmine@ruby-lang.org wrote:

$ ruby -e 'p 0.1.to_r'

(3602879701896397/36028797018963968)whereas

$ ruby -e 'p "0.1".to_r'

(1/10)

What, in theory, could be done about this? By the time to_r is

invoked, 0.1 is already a binary float, with the implicit rounding

off.In theory, Float#to_r could be done through Float#to_s#to_r.

-1. That loses data.

=end

#### #3 Updated by Roger Pack over 7 years ago

=begin

-1 that loses data.

True--however the (current) code for String#to_s attempts to determine whether the floating point number "is the equivalent default for the rounded value" (i.e. if it round trips).

Do you think that using a comparisong like this (similar to what Park suggested) would be good enough for deducing the true original value? (I've thought of proposing a similar thing for BigDecimal,

ex: BigDecimal(0.1) => #

-=r

=end

#### #4 Updated by tadayoshi funaba over 7 years ago

=begin

to_r should provide exact conversion.

I think ruby may provide "rationalize" on common lisp or scheme.

but not yet.

=end

#### #5 Updated by Nobuyoshi Nakada over 7 years ago

=begin

Hi,

At Fri, 1 May 2009 21:12:52 +0900,

Roger Pack wrote in :

True--however the (current) code for String#to_s attempts to

determine whether the floating point number "is the

equivalent default for the rounded value" (i.e. if it round

trips).

What about this?

Index: rational.c

===================================================================

--- rational.c (revision 23433)

+++ rational.c (working copy)

@@ -1286,4 +1286,5 @@ integer_to_r(VALUE self)

}

+#if 0

static void

float_decode_internal(VALUE self, VALUE *rf, VALUE *rn)

@@ -1299,5 +1300,4 @@ float_decode_internal(VALUE self, VALUE

}

-#if 0

static VALUE

float_decode(VALUE self)

@@ -1310,11 +1310,82 @@ float_decode(VALUE self)

#endif

+#if FLT_RADIX == 2 && SIZEOF_BDIGITS * 2 * CHAR_BIT > DBL_MANT_DIG

+# ifdef HAVE_LONG_LONG

+# define BDIGITDBL2NUM(x) ULL2NUM(x)

+# else

+# define BDIGITDBL2NUM(x) ULONG2NUM(x)

+# endif

+#else

+# define NEEDS_FDIV

+static ID id_fdiv;

+fun2(fdiv)

+#endif

+

+static VALUE

+float_r_round(double a, double f, int n)

+{

+ int i, r;

+#ifdef BDIGITDBL2NUM

+ BDIGIT_DBL fn = (BDIGIT_DBL)fabs(f);

+ BDIGIT_DBL d1 = (BDIGIT_DBL)1 << -n, d2 = d1;

+ BDIGIT_DBL rv = d1 % fn;

+ VALUE b, d;

+ if (rv < 10) {

+ for (i = 1, r = (int)rv; i <= r; ++i) {

+ if ((double)fn / --d2 != a) break;

+ if (fn % (d1 = d2) == 0) break;

+ }

+ }

+ else if ((rv = fn - rv) && rv < 10) {

+ for (i = 1, r = (int)rv; i <= r; ++i) {

+ if ((double)fn / ++d2 != a) break;

+ if (fn % (d1 = d2) == 0) break;

+ }

+ }

+ b = BDIGITDBL2NUM(fn);

+ d = BDIGITDBL2NUM(d1);

+ if (f < 0) b = f_negate(b);

+#else

+ VALUE d2, fn, rv;

+ VALUE b = rb_dbl2big(f);

+ VALUE d = rb_big_pow(rb_uint2big(FLT_RADIX), INT2FIX(-n));

+ if (FIXNUM_P(d)) {

+ d = rb_uint2big(FIX2LONG(d));

+ }

+ d2 = d;

+ fn = f_abs(b);

+ rv = rb_big_modulo(d, fn);

+ if (FIXNUM_P(rv) && (r = FIX2LONG(rv)) < 10) {

+ for (i = 1; i <= r; ++i) {

+ d2 = f_sub(d2, INT2FIX(1));

+ if (RFLOAT_VALUE(f_fdiv(fn, d2)) != a) break;

+ if (f_mod(fn, d = d2) == INT2FIX(0)) break;

+ }

+ }

+ else if (FIXNUM_P(rv = f_sub(fn, rv)) && (r = FIX2LONG(rv)) < 10) {

+ for (i = 1; i <= r; ++i) {

+ d2 = f_add(d2, INT2FIX(1));

+ if (RFLOAT_VALUE(f_fdiv(fn, d2)) != a) break;

+ if (f_mod(fn, d = d2) == INT2FIX(0)) break;

+ }

+ }

+#endif

+ return rb_rational_new(b, d);

+}

+

static VALUE

float_to_r(VALUE self)

{

- VALUE f, n;

+ double a, f;

+ int n;

- float_decode_internal(self, &f, &n);
- return f_mul(f, f_expt(INT2FIX(FLT_RADIX), n));
- a = RFLOAT_VALUE(self);
- f = frexp(a, &n);
- f = ldexp(f, DBL_MANT_DIG);
- n -= DBL_MANT_DIG;
- if (n <= DBL_MANT_DIG && f != 0) {
- return float_r_round(a, f, n);
- }
- return f_mul(rb_dbl2big(f), f_expt(INT2FIX(FLT_RADIX), INT2FIX(n))); }

@@ -1569,4 +1640,7 @@ Init_Rational(void)

id_to_s = rb_intern("to_s");

id_truncate = rb_intern("truncate");

+#ifdef NEEDS_FDIV

+ id_fdiv = rb_intern("fdiv");

+#endif

ml = (long)(log(DBL_MAX) / log(2.0) - 1);

--

Nobu Nakada

=end

#### #6 Updated by Yukihiro Matsumoto over 7 years ago

=begin

Hi,

In message "Re: Re: [Feature #1408] 0.1.to_r not equal to (1/10)"

on Sat, 16 May 2009 06:23:53 +0900, Nobuyoshi Nakada nobu@ruby-lang.org writes:

|What about this?

Could you explain how this patch differs from the original?

matz.

=end

#### #7 Updated by Nobuyoshi Nakada over 7 years ago

=begin

Hi,

At Mon, 18 May 2009 11:15:16 +0900,

Yukihiro Matsumoto wrote in :

Could you explain how this patch differs from the original?

Searches more reduceable numerator which can round trip. Since

it just tries the numerator only in very restricted condtion,

better result may be achieved by trying also the denominator,

in other cases. In fact, the patch works for very simple

cases, e.g. 0.1 and (1.0/3.0), but doesn't for 0.24.

--

Nobu Nakada

=end

#### #9 Updated by Marc-Andre Lafortune almost 7 years ago

**Assignee**set to*Yukihiro Matsumoto***Category**set to*core*

=begin

=end

#### #10 Updated by Marc-Andre Lafortune almost 7 years ago

=begin

Sorry to be late to the party on this one.

It is important to remember that a Float is always an approximation.

1.0 has to be understood as 1.0 +/- EPSILON, where the EPSILON is platform dependent. 1.0 is not more equal to 1 than to 1 + EPSILON/2. Indeed, there is no way to distinguish either when they are stored as floats.

To believe that Float#to_s loses data is wrong. If r.to_s returns "1.2", it implies that 1.2 is one of the values in the range of possible values for that floating number. It could have been 1.2000...0006. Or something else. There is no way to know, so #to_s chooses, wisely, to return the simplest value in the range.

There are many rationals that would be encoded as floats the same way. There is no magic way to know that the "exact" value was exactly 12/10 or 5404319552844595/4503599627370496, or anything in between. All have the same representation as a float. There is no reason to believe that the missing (binary) decimals that couldn't be written in space allowed where all 0. Actually, there is reason to believe that they were *probably* non zero, because fractions that can not be expressed with a finite number of terms in their expansion in a given base all have a recurring expansion. I.e. if the significand does not end with a whole bunch of zeros (rational has finite expansion) then it probably ends with an infinite pattern (say 011011011 in binary, or 333333 in decimal).

For any given float, there is one and only one rational with the smallest denominator that falls in the range of its possible values. It is currently given by Number#rationalize, and I really do not understand why #to_r would return anything else.

I cannot see any purpose to any other fraction. Moreover, the current algorithm, which returns the middle of the range of possibilities, is platform dependent since the range of possibilities is platform dependent. That makes it even less helpful.

Is there an example where one would want 0.1.to_r to be 3602879701896397/36028797018963968 ?

Do we really think that 0.1.to_r to be 3602879701896397/36028797018963968 corresponds to the principle of least surprise?

Note that I'm writing that fraction but with a different native double encoding, the fraction would be different.

=end

#### #11 Updated by Kazuhiro NISHIYAMA over 6 years ago

**Status**changed from*Open*to*Assigned***Target version**changed from*1.9.2*to*2.0.0*

=begin

=end

#### #12 Updated by Marc-Andre Lafortune over 6 years ago

=begin

Why isn't Float#to_r simply calling Float#rationalize ?

=end

#### #13 Updated by Matthias Wächter over 6 years ago

=begin

Am 20.09.2009 06:17, schrieb Marc-Andre Lafortune:

Sorry to be late to the party on this one.

I’m late as well ;)

It is important to remember that a Float is always an approximation.

No. It is an approximation only for:

• conversion from most decimal numbers, especially floats, and

• calculations that drop digits.

You can do exact math in a limited range of operations, and the question

should be whether the approximation approach should overrule this exact

math range of use, especially considering that conversion back to

decimal *could* be done precisely, however, sometimes requiring a bunch

of digits.

1.0 has to be understood as 1.0 +/- EPSILON, where the EPSILON is platform

dependent. 1.0 is not more equal to 1 than to 1 + EPSILON/2. Indeed, there

is no way to distinguish either when they are stored as floats.

If what’s stored in the Float *is* your precise result, you certainly

would not ask for precision reduction just because it *could* have been

the result of an imprecise calculation.

To believe that Float#to_s loses data is wrong.

I think there should be both a Float#to_s and Float#to_nearest_s. The

first would be precise, the second would output the “shortest” decimal

representation within ±EPSILON/2.

If r.to_s returns "1.2", it implies that 1.2 is one of the values in the

range of possible values for that floating number. It could have been

1.2000...0006. Or something else. There is no way to know, so #to_s chooses,

wisely, to return the simplest value in the range.

This is based on the assumption that no-one would ever care about

Float’s precision.

There are many rationals that would be encoded as floats the same way. There

is no magic way to know that the "exact" value was exactly 12/10 or

5404319552844595/4503599627370496, or anything in between. All have the same

representation as a float. There is no reason to believe that the missing

(binary) decimals that couldn't be written in space allowed where all 0.

Actually, there is reason to believe that they wereprobablynon zero,

because fractions that can not be expressed with a finite number of terms in

their expansion in a given base all have a recurring expansion. I.e. if the

significand does not end with a whole bunch of zeros (rational has finite

expansion) then it probably ends with an infinite pattern (say 011011011 in

binary, or 333333 in decimal).For any given float, there is one and only one rational with the smallest

denominator that falls in the range of its possible values. It is currently

given by Number#rationalize, and I really do not understand why #to_r would

return anything else.I cannot see any purpose to any other fraction. Moreover, the current algorithm,

which returns the middle of the range of possibilities, is platform dependent

since the range of possibilities is platform dependent. That makes it even less

helpful.Is there an example where one would want 0.1.to_r to be

3602879701896397/36028797018963968 ?

If the binary/Float’s representation of

3602879701896397/36028797018963968 is the real result of the

calculation? How do you know?

Do we really think that 0.1.to_r to be 3602879701896397/36028797018963968

corresponds to the principle of least surprise?

False assumption here. Using floats for exact decimal math already

violates POLS. Don’t blame the messenger, i.e. the converter back to

decimal, the only part of the game that could *always* be precise.

Note that I'm writing that fraction but with a different native double

encoding, the fraction would be different.

Sure. Great to have different levels of precision/imprecision from the

computers.

And portability is not always the issue, otherwise there would have

never been different native floating point precisions.

– Matthias

=end

#### #14 Updated by Matthias Wächter over 6 years ago

=begin

Hello Marc-Andre,

On 19.04.2010 00:14, Marc-Andre Lafortune wrote:

I hope my dissent will not sound too harsh.

Not at all.

Arguing that 0.1.to_r should be 3602879701896397/36028797018963968 is

the same as arguing that 0.1.to_s should outputs these 55 decimals.

Right, that’s my point. 0.1 as a Float has a precise meaning in binary as in decimal, so Float#to_s should keep those 55 decimals. That’s why I said

that Float#to_nearest_s – choose a better name or an option to Fload#to_s – should be created that does »what everyone expects« to_s to do.

The same applies to Float#to_r. It should be as precise as possible, which it is currently. The function that does »what everyone expects« should be

Float#to_nearest_r in the same way as for the string representation.

For these reasons, the set S is of little interest to anybody.

The problem is that most people think that Floating point arithmetic is precise, which it is only for the the cases I described in my last mail.

What

isinteresting is the set of real numbers. Floating numbers are

used to represent themapproximately. To add to my voice, here are a

couple of excerpts from the first links that come up on google

(highlight mine):"In computing, floating point describes a system for representing

numbers that would be too large or too small to be represented as

integers. Numbers are in general representedapproximatelyto a

fixed number of significant digits and scaled using an exponent."

http://en.wikipedia.org/wiki/Floating_point"Squeezing infinitely many real numbers into a finite number of bits

requires anapproximaterepresentation.... Therefore the result of a

floating-point calculation must often be rounded in order to fit back

into its finite representation. This rounding error is the

characteristic feature of floating-point computation." source:

http://docs.sun.com/source/806-3568/ncg_goldberg.html

That’s where the problem starts. Everyone thinks he can do exact math on a computer, and the only problem was the approximation of the binary

representation of a real number, characterized by ±EPSILON/2. No, the *real* issue is the approximation of calculations which not only accumulates

EPSILON with each calculation, but it can shift EPSILON to any order. Think of something trivial like (1E-40+0.1-0.1) returning 0.0 vs.

(1E-40+0.3-0.2-0.1) returning -2.7E-17. There is no real math in floats.

One can go as far as saying that availability of math-like operators and math-like precedence in a programming language supports the expectations of

real-number-like behavior and precision. But this is slightly off-topic, and in fact method calls for simple math are not doing any good to

readability. Math-like operator precedence is different and something completely unnecessary in a programming language, IMHO.

Note that typing 0.1 in Ruby is a "calculation" which consists in

finding the member of S closest to 1/10.Your final question was: how do I know that the value someone is

talking about is 0.1 and not

0.1000000000000000055511151231257827021181583404541015625 (or

equivalently 3602879701896397/36028797018963968) ?I call it common sense.

It looks so obvious when we are talking about 0.1. If we talk about any other number with 80 digits, my point may become clearer.

What do you do if it’s not 0.1 a.k.a. 0.1000000000000000055511151231257827021181583404541015625 but

0.09999999999999997779553950749686919152736663818359375 (the result of (0.3-0.2)? What’s the difference for your argument? Now we will not get back

the expected nearest 0.1 anyway without applying the actually required/expected rounding constraints.

If it’s just about 0.1.to_r, i.e. converting from a decimal constant number to rational, use String#to_r.

Bottom line: Floats are not exact in terms of math, but they are exact in terms of computer-level implementation, implementing IEEE 754. We should

respect the latter and help people deal with the former.

– Matthias

=end

#### #15 Updated by tadayoshi funaba over 6 years ago

=begin

Why isn't Float#to_r simply calling Float#rationalize ?

a = 0.5337486539516013

b = 0.5337486539516012

a == b #=> false

a.to_r == a #=> true

a.rationalize == a #=> false

a.to_r == b #=> false

a.rationalize == b #=> true

actually, flonum is restricted rational number.

however, rationalize bends the value.

to_r is the simplest and the cheapest way, rationalize is not so.

moreover, various languages support exact conversion (e.g. CL, Scheme, Haskell, Squeak, Python).

=end

#### #16 Updated by Kenta Murata over 6 years ago

=begin

Float#rationalize is added again at r27503.

Please check that revision.

On 2010/05/06, at 7:23, Marc-Andre Lafortune wrote:

Maybe a kind Japanese reader can provide the gist of

to explain why was Float#rationalize removed?I would also appreciate opinions as to why it wouldn't be a net

improvement if to_r used the rationalize algorithm and some other

methods were provided for anyone wanting the value of the

representation (e.g. Float#representation which would return [sign,

mantissa, significand] and/or Float#representation_to_r would give the

rational corresponding to the internal representation of that float)

--

Kenta Murata

OpenPGP FP = FA26 35D7 4F98 3498 0810 E0D5 F213 966F E9EB 0BCC

E-mail: mrkn@mrkn.jp

twitter: http://twitter.com/mrkn/

blog: http://d.hatena.ne.jp/mrkn/

=end

#### #17 [ruby-core:39168] Updated by Kenta Murata almost 5 years ago

**Status**changed from*Assigned*to*Closed*

I close this ticket because the topic was too diverged.

Would you please make new tickets for the new version of ruby if anyone has objections.