I have the following string:
"1.273,08"
And I need to convert to float and the result must be:
1273.08
I tried some code using gsub but I can't solve this.
How can I do this conversion?
You have already received two good answers how to massage your String into your desired format using String#delete and String#tr.
But there is a deeper problem.
The decimal value 1 273.0810 cannot be accurately represented as an IEEE 754-2019 / ISO/IEC 60559:2020 binary64 floating point value.
Just like the value 1/3rd can easily be represented in ternary (0.13) but has an infinite representation in decimal (0.33333333…10, i.e. 0.[3]…10) and thus cannot be accurately represented, the value 8/100th can easily be represented in decimal (0.0810) but has an infinite representation in binary (0.0001010001111010111000010100011110101110000101…2, i.e. 0.[00010100011110101110]…2). In other words, it is impossible to express 1 273.0810 as a Ruby Float.
And that's not specific to Ruby, or even to programming, that is just basic high school maths: you cannot represent this number in binary, period, just like you cannot represent 1/3rd in decimal, or π in any integer base.
And of course, computers don't have infinite memory, so not only does 1 273.0810 have an infinite representation in binary, but as a Float, it will also be cut off after 64 bits. The closest possible value to 1 273.0810 as an IEEE 754-2019 / ISO/IEC 60559:2020 binary64 floating point value is 1 273.079 999 999 999 927 240 423 858 1710, which is less than 1 273.0810.
That is why you should never represent money using binary numbers: everybody will expect it to be decimal, not binary; if I write a cheque, I write it in decimal, not binary. People will expect that it is impossible to represent $ 1/3rd, they will expect that it is impossible to represent $ π, but they will not expect and not accept that if they put $ 1273.08 into their account, they will actually end up with slightly less than that.
The correct way to represent money would be to use a specialized Money datatype, or at least using the bigdecimal library from the standard library:
require 'bigdecimal'
BigDecimal('1.273,08'.delete('.').tr(',', '.'))
#=> 0.127308e4
I would do
"1.273,08".delete('.') # delete '.' from the string
.tr(',', '.') # replace ',' with '.'
.to_f # translate to float
#=> 1273.08
So, we're using . as a thousands separator and , instead of a dot:
str = "1.273,08"
str.gsub('.','').gsub(',', '.').to_f
Related
Is there any way to tell the default ruby JSON library to parse non-integer numeric values as string (or BigDecimal?) instead of floats?
ie JSON.parse('{"foo": 123.45}')['foo'].class outputs Float, which may lead to precision issues.
PD: the oj library supports loading these values as BigDecimals.
PD2: seems there isn't: https://github.com/flori/json/blob/76f41a84e2bace20c3076aba53887537e37dfdb2/lib/json/pure/parser.rb#L196
In theory JSON as a container could hold highly precise numbers, but in practice one end is generally limited to IEEE 754 double precision floating point numbers as that is what JavaScript itself is limited to. Any precision loss will already be incurred if the values are encoded in JavaScript or almost any JSON implementation.
Hence, converting to BigDecimal from the parsed Float will almost always result in no additional loss of precision:
data = JSON.parse("[1.025]")
# Float can't represent decimal values precisely, so `round` fails
data.first.round(2) # => 1.02
# Converting to big decimal improves the precision of future operations
BigDecimal.new(data.first.to_s).round(2).to_s # => "1.03"
You are much better off transporting your highly precise values as strings.
Lastly, if you really need to ruby libraries can always be monkey patched to behave how you want.
I'm using ruby's Rational library to convert the width & height of images to aspect ratios.
I've noticed that string arguments are treated differently than numeric arguments.
>> Rational('1.91','1')
=> (191/100)
>> Rational(1.91,1)
=> (8601875288277647/4503599627370496)
>> RUBY_VERSION
=> "2.1.5"
>> RUBY_ENGINE
=> "ruby"
FYI 1.91:1 is an aspect ratio recommended by Facebook for images on their platform.
Values like 191 and 100 are much more convenient to store in my database than 8601875288277647 and 4503599627370496. But I'd like to understand where this different originates before deciding which approach to use.
The Rational test suite doesn't seem to cover this exact case.
Disclaimer: This is only an educated guess, based on some knowledge on how to implement such a feat.
As Kent Dahl already said, Floats are not precise, they have a fixed precision, which means 1.91 is really 1.910000000000000000001 or something like that, which ruby "knows" should be displayed as 1.91.
"1.91" on the other hand is a string, basically an array of characters: '1', '.', '9', '1'.
This said, here is what you need to do, to build the rational out of floats:
Get rid of the . (mathematically by multiplying the numerator and denominator with 10^x, or multiplying with ten as many times, as there are numbers behind the .)
Find the greatest common denominator (gcd)
Divide num and denom with the gcd
Step 1 however, is a little different for Float and String:
The Float, we will have to multiply with 10^x, where x is (because of the precision) not 2 (as one would think with 1.91), but more something like 16 (remember: 1.9100...1).
For the String, we COULD cast it into a float and do the same trick, but hey, there is an easier way: We just count the number of characters behind the dot (which is 2), remove the dot and multiply the denom with 10^2... This is not only the easier, but also the more precise way.
The big numbers might disappear again, when applying step 3, that's why you will not always get those strange results when dealing with rationals from floats.
TLDR: The numbers will be built differently based on the arguments being String, or FLoat. FLoats can produce long-ass numbers, because precision.
The Float 1.91 is stored as a double which has a given amount of precision, limited by binary presentation. The equivalent Rational object retains this precision a such as possible, so it is huge. There is no way of storing 1.91 exactly in a double, but the value you get is close enough for most uses.
As for the String, it represents a different value - the exact value of 1.91 - and as you create a Rational it retains it better. It is more correct than the Float, UT takes longer to use for calculations.
This is similar to the problem with 1.0/3 as it "goes on forever" 0.333333...etc, but Rational can represent it exactly.
I am using IEEE fixed point package in VHDL.
It works well, but I now facing a problem concerning their string representation in a test bench : I would like to dump them in a text file.
I have found that it is indeed possible to directly write ufixed or sfixed using :
write(buf, to_string(x)); --where x is either sfixed or ufixed (and buf : line)
But then I get values like 11110001.10101 (for sfixed q8.5 representation).
So my question : how to convert back these fixed point numbers to reals (and then to string) ?
The variable needs to be split into two std-logic-vector parts, the integer part can be converted to a string using standard conversion, but for the fraction part the string conversion is a bit different. For the integer part you need to use a loop and divide by 10 and convert the modulo remainder into ascii character, building up from the lower digit to the higher digit. For the fractional part it also need a loop but one needs to multiply by 10 take the floor and isolate this digit to get the corresponding character, then that integer is used to be substracted to the fraction number, etc. This is the concept, worked in MATLAB to test and making a vhdl version I will share soon. I was surprised not to find such useful function anywhere. Of course fixed-point format can vary Q(N,M) N and M can have all sorts of values, while for floating point, it is standardized.
Why is the following operation leading me to this value:
14.99 + 1.5 = 16.490000000000002
I would expect it to be 16.49. How can I avoid those extra decimals?
That's how floating point arithmetic works. If you want a rounded number that's still a Float object, you can do
result.round(2) #=> 16.49
or if you just need a string:
"%0.2f" % result
This is not due to Ruby, but because of the way floating point numbers are represented in a computer (according to the IEEE 754 standard).
In short, some floating point numbers just can't be represented exactly in a computer. If you need better precision, you can try the BigDecimal class.
I am writing code that will deal with currencies, charges, etc. I am going to use the BigDecimal class for math and storage, but we ran into something weird with it.
This statement:
1876.8 == BigDecimal('1876.8')
returns false.
If I run those values through a formatting string "%.13f" I get:
"%.20f" % 1876.8 => 1876.8000000000000
"%.20f" % BigDecimal('1876.8') => 1876.8000000000002
Note the extra 2 from the BigDecimal at the last decimal place.
I thought BigDecimal was supposed to counter the inaccuracies of storing real numbers directly in the native floating point of the computer. Where is this 2 coming from?
It won't give you as much control over the number of decimal places, but the conventional format mechanism for BigDecimal appears to be:
a.to_s('F')
If you need more control, consider using the Money gem, assuming your domain problem is mostly about currency.
gem install money
You are right, BigDecimal should be storing it correctly, my best guess is:
BigDecimal is storing the value correctly
When passed to a string formatting function, BigDecimal is being cast as a lower precision floating point value, creating the ...02.
When compared directly with a float, the float has an extra decimal place far beyond the 20 you see (classic floats can't be compared behavoir).
Either way, you are unlikely to get accurate results comparing a float to a BigDecimal.
Don't compare FPU decimal string fractions for equality
The problem is that the equality comparison of a floating or double value with a decimal constant that contains a fraction is rarely successful.
Very few decimal string fractions have exact values in the binary FP representation, so equality comparisons are usually doomed.*
To answer your exact question, the 2 is coming from a slightly different conversion of the decimal string fraction into the Float format. Because the fraction cannot be represented exactly, it's possible that two computations will consider different amounts of precision in intermediate calculations and ultimately end up rounding the result to a 52-bit IEEE 754 double precision mantissa differently. It hardly matters because there is no exact representation anyway, but one is probably more wrong than the other.
In particular, your 1876.8 cannot be represented exactly by an FP object, in fact, between 0.01 and 0.99, only 0.25, 0.50, and 0.75 have exact binary representations. All the others, include 1876.8, repeat forever and are rounded to 52 bits. This is about half of the reason that BigDecimal even exists. (The other half of the reason is the fixed precision of FP data: sometimes you need more.)
So, the result that you get when comparing an actual machine value with a decimal string constant depends on every single bit in the binary fraction ... down to 1/252 ... and even then requires rounding.
If there is anything even the slightest bit (hehe, bit, sorry) imperfect about the process that produced the number, or the input conversion code, or anything else involved, they won't look exactly equal.
An argument could even be made that the comparison should always fail because no IEEE-format FPU can even represent that number exactly. They really are not equal, even though they look like it. On the left, your decimal string has been converted to a binary string, and most of the numbers just don't convert exactly. On the right, it's still a decimal string.
So don't mix floats with BigDecimal, just compare one BigDecimal with another BigDecimal. (Even when both operands are floats, testing for equality requires great care or a fuzzy test. Also, don't trust every formatted digit: output formatting will carry remainders way off the right side of the fraction, so you don't generally start seeing zeroes, you will just see garbage values.)
*The problem: machine numbers are x/2n, but decimal constants are x/(2n * 5m). Your value as sign, exponent, and mantissa is the infinitely repeating 0 10000001001 1101010100110011001100110011001100110011001100110011... Ironically, FP arithmetic is perfectly precise and equality comparisons work perfectly well when the value has no fraction.
as David said, BigDecimal is storing it right
p (BigDecimal('1876.8') * 100000000000000).to_i
returns 187680000000000000
so, yes, the string formatting is ruining it
If you don't need fractional cents, consider storing and manipulating the currency as an integer, then dividing by 100 when it's time to display. I find that easier than dealing with the inevitable precision issues of storing and manipulating in floating point.
On Mac OS X, I'm running ruby 1.8.7 (2008-08-11 patchlevel 72) [i686-darwin9]
irb(main):004:0> 1876.8 == BigDecimal('1876.8') => true
However, being Ruby, I think you should think in terms of messages sent to objects. What does this return to you:
BigDecimal('1876.8') == 1876.8
The two aren't equivalent, and if you're trying to use BigDecimal's ability to determine precise decimal equality, it should be the receiver of the message asking about the equality.
For the same reason I don't think formatting the BigDecimal by sending a format message to the format string is the right approach either.