Why do the hash values differ for NaN and Inf - Inf?












10














I use this hash function a lot, i.e. to record the value of a dataframe. Wanted to see if I could break it. Why aren't these hash values identical?



This requires the digest package.



Plain text output:



> digest(Inf-Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"
> digest(1)
[1] "6717f2823d3202449301145073ab8719"
> digest(1 + 0)
[1] "6717f2823d3202449301145073ab8719"
> digest(5)
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(sum(1, 1, 1, 1, 1))
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(1^0)
[1] "6717f2823d3202449301145073ab8719"
> 1^0
[1] 1
> digest(1)
[1] "6717f2823d3202449301145073ab8719"


Additional weirdness. Calculations equal NaN have identical hash values, but NaN's hash values are not equivalent:



> Inf - Inf
[1] NaN
> 0/0
[1] NaN
> digest(Inf - Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(0/0)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"









share|improve this question
























  • @BenBolker, done.
    – King_Cordelia
    3 hours ago










  • Looks like a bug. You could ask Dirk directly to fix it and and add a unit test for this particular case.
    – Oleg Sklyar
    3 hours ago










  • What is the type of NaN if input directly? It is probably numeric/double when it results from a subtraction of a division operation.
    – Oleg Sklyar
    3 hours ago










  • yes, it's numeric; str(Inf-Inf) and str(NaN) are both "num NaN" (and identical(Inf-Inf,NaN) is TRUE ...
    – Ben Bolker
    3 hours ago










  • @OlegSklyar, class of NaN and class of operations that equal NaN (0/0, Inf - Inf) is "numeric".
    – King_Cordelia
    3 hours ago
















10














I use this hash function a lot, i.e. to record the value of a dataframe. Wanted to see if I could break it. Why aren't these hash values identical?



This requires the digest package.



Plain text output:



> digest(Inf-Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"
> digest(1)
[1] "6717f2823d3202449301145073ab8719"
> digest(1 + 0)
[1] "6717f2823d3202449301145073ab8719"
> digest(5)
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(sum(1, 1, 1, 1, 1))
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(1^0)
[1] "6717f2823d3202449301145073ab8719"
> 1^0
[1] 1
> digest(1)
[1] "6717f2823d3202449301145073ab8719"


Additional weirdness. Calculations equal NaN have identical hash values, but NaN's hash values are not equivalent:



> Inf - Inf
[1] NaN
> 0/0
[1] NaN
> digest(Inf - Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(0/0)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"









share|improve this question
























  • @BenBolker, done.
    – King_Cordelia
    3 hours ago










  • Looks like a bug. You could ask Dirk directly to fix it and and add a unit test for this particular case.
    – Oleg Sklyar
    3 hours ago










  • What is the type of NaN if input directly? It is probably numeric/double when it results from a subtraction of a division operation.
    – Oleg Sklyar
    3 hours ago










  • yes, it's numeric; str(Inf-Inf) and str(NaN) are both "num NaN" (and identical(Inf-Inf,NaN) is TRUE ...
    – Ben Bolker
    3 hours ago










  • @OlegSklyar, class of NaN and class of operations that equal NaN (0/0, Inf - Inf) is "numeric".
    – King_Cordelia
    3 hours ago














10












10








10







I use this hash function a lot, i.e. to record the value of a dataframe. Wanted to see if I could break it. Why aren't these hash values identical?



This requires the digest package.



Plain text output:



> digest(Inf-Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"
> digest(1)
[1] "6717f2823d3202449301145073ab8719"
> digest(1 + 0)
[1] "6717f2823d3202449301145073ab8719"
> digest(5)
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(sum(1, 1, 1, 1, 1))
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(1^0)
[1] "6717f2823d3202449301145073ab8719"
> 1^0
[1] 1
> digest(1)
[1] "6717f2823d3202449301145073ab8719"


Additional weirdness. Calculations equal NaN have identical hash values, but NaN's hash values are not equivalent:



> Inf - Inf
[1] NaN
> 0/0
[1] NaN
> digest(Inf - Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(0/0)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"









share|improve this question















I use this hash function a lot, i.e. to record the value of a dataframe. Wanted to see if I could break it. Why aren't these hash values identical?



This requires the digest package.



Plain text output:



> digest(Inf-Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"
> digest(1)
[1] "6717f2823d3202449301145073ab8719"
> digest(1 + 0)
[1] "6717f2823d3202449301145073ab8719"
> digest(5)
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(sum(1, 1, 1, 1, 1))
[1] "5e338704a8e069ebd8b38ca71991cf94"
> digest(1^0)
[1] "6717f2823d3202449301145073ab8719"
> 1^0
[1] 1
> digest(1)
[1] "6717f2823d3202449301145073ab8719"


Additional weirdness. Calculations equal NaN have identical hash values, but NaN's hash values are not equivalent:



> Inf - Inf
[1] NaN
> 0/0
[1] NaN
> digest(Inf - Inf)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(0/0)
[1] "0d59b2dae9351c1ce6c76133295322d7"
> digest(NaN)
[1] "4e9653ddf814f0d16b72624aeb85bc20"






r math hash digest






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 3 hours ago







King_Cordelia

















asked 3 hours ago









King_CordeliaKing_Cordelia

536




536












  • @BenBolker, done.
    – King_Cordelia
    3 hours ago










  • Looks like a bug. You could ask Dirk directly to fix it and and add a unit test for this particular case.
    – Oleg Sklyar
    3 hours ago










  • What is the type of NaN if input directly? It is probably numeric/double when it results from a subtraction of a division operation.
    – Oleg Sklyar
    3 hours ago










  • yes, it's numeric; str(Inf-Inf) and str(NaN) are both "num NaN" (and identical(Inf-Inf,NaN) is TRUE ...
    – Ben Bolker
    3 hours ago










  • @OlegSklyar, class of NaN and class of operations that equal NaN (0/0, Inf - Inf) is "numeric".
    – King_Cordelia
    3 hours ago


















  • @BenBolker, done.
    – King_Cordelia
    3 hours ago










  • Looks like a bug. You could ask Dirk directly to fix it and and add a unit test for this particular case.
    – Oleg Sklyar
    3 hours ago










  • What is the type of NaN if input directly? It is probably numeric/double when it results from a subtraction of a division operation.
    – Oleg Sklyar
    3 hours ago










  • yes, it's numeric; str(Inf-Inf) and str(NaN) are both "num NaN" (and identical(Inf-Inf,NaN) is TRUE ...
    – Ben Bolker
    3 hours ago










  • @OlegSklyar, class of NaN and class of operations that equal NaN (0/0, Inf - Inf) is "numeric".
    – King_Cordelia
    3 hours ago
















@BenBolker, done.
– King_Cordelia
3 hours ago




@BenBolker, done.
– King_Cordelia
3 hours ago












Looks like a bug. You could ask Dirk directly to fix it and and add a unit test for this particular case.
– Oleg Sklyar
3 hours ago




Looks like a bug. You could ask Dirk directly to fix it and and add a unit test for this particular case.
– Oleg Sklyar
3 hours ago












What is the type of NaN if input directly? It is probably numeric/double when it results from a subtraction of a division operation.
– Oleg Sklyar
3 hours ago




What is the type of NaN if input directly? It is probably numeric/double when it results from a subtraction of a division operation.
– Oleg Sklyar
3 hours ago












yes, it's numeric; str(Inf-Inf) and str(NaN) are both "num NaN" (and identical(Inf-Inf,NaN) is TRUE ...
– Ben Bolker
3 hours ago




yes, it's numeric; str(Inf-Inf) and str(NaN) are both "num NaN" (and identical(Inf-Inf,NaN) is TRUE ...
– Ben Bolker
3 hours ago












@OlegSklyar, class of NaN and class of operations that equal NaN (0/0, Inf - Inf) is "numeric".
– King_Cordelia
3 hours ago




@OlegSklyar, class of NaN and class of operations that equal NaN (0/0, Inf - Inf) is "numeric".
– King_Cordelia
3 hours ago












2 Answers
2






active

oldest

votes


















16














tl;dr this has to do with very deep details of how NaNs are represented in binary. You could work around it by using digest(.,ascii=TRUE) ...



Following up on @Jozef's answer: note boldfaced digits ...




> base::serialize(Inf-Inf,connection=NULL)
[1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
[26] 00 0e 00 00 00 01 ff f8 00 00 00 00 00 00
> base::serialize(NaN,connection=NULL)
[1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
[26] 00 0e 00 00 00 01 7f f8 00 00 00 00 00 00


Alternatively, using pryr::bytes() ...



> bytes(NaN)
[1] "7F F8 00 00 00 00 00 00"
> bytes(Inf-Inf)
[1] "FF F8 00 00 00 00 00 00"


The Wikipedia article on floating point format/NaNs says:




Some operations of floating-point arithmetic are invalid, such as taking the square root of a negative number. The act of reaching an invalid result is called a floating-point exception. An exceptional result is represented by a special code called a NaN, for "Not a Number". All NaNs in IEEE 754-1985 have this format:




  • sign = either 0 or 1.

  • biased exponent = all 1 bits.

  • fraction = anything except all 0 bits (since all 0 bits represents infinity).




The sign is the first bit; the exponent is the next 11 bits; the fraction is the last 52 bits. Translating the first four hex digits given above to binary, Inf-Inf is 1111 1111 1111 0100 (sign=1; exponent is all ones, as required; fraction starts with 0100) whereas NaN is 0111 1111 1111 0100 (the same, but with sign=0).



To understand why Inf-Inf ends up with sign bit 1 and NaN has sign bit 0 you'd probably have to dig more deeply into the way floating point arithmetic is implemented on this platform ...



It might be worth raising an issue on the digest GitHub repo about this; I can't think of an elegant way to do it, but it seems reasonable that objects where identical(x,y) is TRUE in R should have identical hashes ... Note that identical() specifically ignores these differences in bit patterns via the single.NA (default TRUE) argument:




single.NA: logical indicating if there is conceptually just one numeric
‘NA’ and one ‘NaN’; ‘single.NA = FALSE’ differentiates bit
patterns.




Within the C code, it looks like R simply uses C's != operator to compare NaN values unless bitwise comparison is enabled, in which case it does an explicit check of equality of the memory locations: see here. That is, C's comparison operator appears to treat different kinds of NaN values as equivalent ...






share|improve this answer























  • Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
    – King_Cordelia
    3 hours ago










  • Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
    – corsiKa
    35 mins ago



















8














I believe this has do to with digest using base::serialize, which gives non-identical results for the 2 objects:



identical(
base::serialize(Inf-Inf, connection = NULL, ascii = FALSE),
base::serialize(NaN, connection = NULL, ascii = FALSE)
)
# [1] FALSE


Even though



identical(Inf-Inf, NaN)
# [1] TRUE





share|improve this answer





















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    2 Answers
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    active

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    active

    oldest

    votes






    active

    oldest

    votes









    16














    tl;dr this has to do with very deep details of how NaNs are represented in binary. You could work around it by using digest(.,ascii=TRUE) ...



    Following up on @Jozef's answer: note boldfaced digits ...




    > base::serialize(Inf-Inf,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 ff f8 00 00 00 00 00 00
    > base::serialize(NaN,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 7f f8 00 00 00 00 00 00


    Alternatively, using pryr::bytes() ...



    > bytes(NaN)
    [1] "7F F8 00 00 00 00 00 00"
    > bytes(Inf-Inf)
    [1] "FF F8 00 00 00 00 00 00"


    The Wikipedia article on floating point format/NaNs says:




    Some operations of floating-point arithmetic are invalid, such as taking the square root of a negative number. The act of reaching an invalid result is called a floating-point exception. An exceptional result is represented by a special code called a NaN, for "Not a Number". All NaNs in IEEE 754-1985 have this format:




    • sign = either 0 or 1.

    • biased exponent = all 1 bits.

    • fraction = anything except all 0 bits (since all 0 bits represents infinity).




    The sign is the first bit; the exponent is the next 11 bits; the fraction is the last 52 bits. Translating the first four hex digits given above to binary, Inf-Inf is 1111 1111 1111 0100 (sign=1; exponent is all ones, as required; fraction starts with 0100) whereas NaN is 0111 1111 1111 0100 (the same, but with sign=0).



    To understand why Inf-Inf ends up with sign bit 1 and NaN has sign bit 0 you'd probably have to dig more deeply into the way floating point arithmetic is implemented on this platform ...



    It might be worth raising an issue on the digest GitHub repo about this; I can't think of an elegant way to do it, but it seems reasonable that objects where identical(x,y) is TRUE in R should have identical hashes ... Note that identical() specifically ignores these differences in bit patterns via the single.NA (default TRUE) argument:




    single.NA: logical indicating if there is conceptually just one numeric
    ‘NA’ and one ‘NaN’; ‘single.NA = FALSE’ differentiates bit
    patterns.




    Within the C code, it looks like R simply uses C's != operator to compare NaN values unless bitwise comparison is enabled, in which case it does an explicit check of equality of the memory locations: see here. That is, C's comparison operator appears to treat different kinds of NaN values as equivalent ...






    share|improve this answer























    • Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
      – King_Cordelia
      3 hours ago










    • Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
      – corsiKa
      35 mins ago
















    16














    tl;dr this has to do with very deep details of how NaNs are represented in binary. You could work around it by using digest(.,ascii=TRUE) ...



    Following up on @Jozef's answer: note boldfaced digits ...




    > base::serialize(Inf-Inf,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 ff f8 00 00 00 00 00 00
    > base::serialize(NaN,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 7f f8 00 00 00 00 00 00


    Alternatively, using pryr::bytes() ...



    > bytes(NaN)
    [1] "7F F8 00 00 00 00 00 00"
    > bytes(Inf-Inf)
    [1] "FF F8 00 00 00 00 00 00"


    The Wikipedia article on floating point format/NaNs says:




    Some operations of floating-point arithmetic are invalid, such as taking the square root of a negative number. The act of reaching an invalid result is called a floating-point exception. An exceptional result is represented by a special code called a NaN, for "Not a Number". All NaNs in IEEE 754-1985 have this format:




    • sign = either 0 or 1.

    • biased exponent = all 1 bits.

    • fraction = anything except all 0 bits (since all 0 bits represents infinity).




    The sign is the first bit; the exponent is the next 11 bits; the fraction is the last 52 bits. Translating the first four hex digits given above to binary, Inf-Inf is 1111 1111 1111 0100 (sign=1; exponent is all ones, as required; fraction starts with 0100) whereas NaN is 0111 1111 1111 0100 (the same, but with sign=0).



    To understand why Inf-Inf ends up with sign bit 1 and NaN has sign bit 0 you'd probably have to dig more deeply into the way floating point arithmetic is implemented on this platform ...



    It might be worth raising an issue on the digest GitHub repo about this; I can't think of an elegant way to do it, but it seems reasonable that objects where identical(x,y) is TRUE in R should have identical hashes ... Note that identical() specifically ignores these differences in bit patterns via the single.NA (default TRUE) argument:




    single.NA: logical indicating if there is conceptually just one numeric
    ‘NA’ and one ‘NaN’; ‘single.NA = FALSE’ differentiates bit
    patterns.




    Within the C code, it looks like R simply uses C's != operator to compare NaN values unless bitwise comparison is enabled, in which case it does an explicit check of equality of the memory locations: see here. That is, C's comparison operator appears to treat different kinds of NaN values as equivalent ...






    share|improve this answer























    • Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
      – King_Cordelia
      3 hours ago










    • Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
      – corsiKa
      35 mins ago














    16












    16








    16






    tl;dr this has to do with very deep details of how NaNs are represented in binary. You could work around it by using digest(.,ascii=TRUE) ...



    Following up on @Jozef's answer: note boldfaced digits ...




    > base::serialize(Inf-Inf,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 ff f8 00 00 00 00 00 00
    > base::serialize(NaN,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 7f f8 00 00 00 00 00 00


    Alternatively, using pryr::bytes() ...



    > bytes(NaN)
    [1] "7F F8 00 00 00 00 00 00"
    > bytes(Inf-Inf)
    [1] "FF F8 00 00 00 00 00 00"


    The Wikipedia article on floating point format/NaNs says:




    Some operations of floating-point arithmetic are invalid, such as taking the square root of a negative number. The act of reaching an invalid result is called a floating-point exception. An exceptional result is represented by a special code called a NaN, for "Not a Number". All NaNs in IEEE 754-1985 have this format:




    • sign = either 0 or 1.

    • biased exponent = all 1 bits.

    • fraction = anything except all 0 bits (since all 0 bits represents infinity).




    The sign is the first bit; the exponent is the next 11 bits; the fraction is the last 52 bits. Translating the first four hex digits given above to binary, Inf-Inf is 1111 1111 1111 0100 (sign=1; exponent is all ones, as required; fraction starts with 0100) whereas NaN is 0111 1111 1111 0100 (the same, but with sign=0).



    To understand why Inf-Inf ends up with sign bit 1 and NaN has sign bit 0 you'd probably have to dig more deeply into the way floating point arithmetic is implemented on this platform ...



    It might be worth raising an issue on the digest GitHub repo about this; I can't think of an elegant way to do it, but it seems reasonable that objects where identical(x,y) is TRUE in R should have identical hashes ... Note that identical() specifically ignores these differences in bit patterns via the single.NA (default TRUE) argument:




    single.NA: logical indicating if there is conceptually just one numeric
    ‘NA’ and one ‘NaN’; ‘single.NA = FALSE’ differentiates bit
    patterns.




    Within the C code, it looks like R simply uses C's != operator to compare NaN values unless bitwise comparison is enabled, in which case it does an explicit check of equality of the memory locations: see here. That is, C's comparison operator appears to treat different kinds of NaN values as equivalent ...






    share|improve this answer














    tl;dr this has to do with very deep details of how NaNs are represented in binary. You could work around it by using digest(.,ascii=TRUE) ...



    Following up on @Jozef's answer: note boldfaced digits ...




    > base::serialize(Inf-Inf,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 ff f8 00 00 00 00 00 00
    > base::serialize(NaN,connection=NULL)
    [1] 58 0a 00 00 00 03 00 03 06 00 00 03 05 00 00 00 00 05 55 54 46 2d 38 00 00
    [26] 00 0e 00 00 00 01 7f f8 00 00 00 00 00 00


    Alternatively, using pryr::bytes() ...



    > bytes(NaN)
    [1] "7F F8 00 00 00 00 00 00"
    > bytes(Inf-Inf)
    [1] "FF F8 00 00 00 00 00 00"


    The Wikipedia article on floating point format/NaNs says:




    Some operations of floating-point arithmetic are invalid, such as taking the square root of a negative number. The act of reaching an invalid result is called a floating-point exception. An exceptional result is represented by a special code called a NaN, for "Not a Number". All NaNs in IEEE 754-1985 have this format:




    • sign = either 0 or 1.

    • biased exponent = all 1 bits.

    • fraction = anything except all 0 bits (since all 0 bits represents infinity).




    The sign is the first bit; the exponent is the next 11 bits; the fraction is the last 52 bits. Translating the first four hex digits given above to binary, Inf-Inf is 1111 1111 1111 0100 (sign=1; exponent is all ones, as required; fraction starts with 0100) whereas NaN is 0111 1111 1111 0100 (the same, but with sign=0).



    To understand why Inf-Inf ends up with sign bit 1 and NaN has sign bit 0 you'd probably have to dig more deeply into the way floating point arithmetic is implemented on this platform ...



    It might be worth raising an issue on the digest GitHub repo about this; I can't think of an elegant way to do it, but it seems reasonable that objects where identical(x,y) is TRUE in R should have identical hashes ... Note that identical() specifically ignores these differences in bit patterns via the single.NA (default TRUE) argument:




    single.NA: logical indicating if there is conceptually just one numeric
    ‘NA’ and one ‘NaN’; ‘single.NA = FALSE’ differentiates bit
    patterns.




    Within the C code, it looks like R simply uses C's != operator to compare NaN values unless bitwise comparison is enabled, in which case it does an explicit check of equality of the memory locations: see here. That is, C's comparison operator appears to treat different kinds of NaN values as equivalent ...







    share|improve this answer














    share|improve this answer



    share|improve this answer








    edited 3 hours ago

























    answered 3 hours ago









    Ben BolkerBen Bolker

    133k11222310




    133k11222310












    • Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
      – King_Cordelia
      3 hours ago










    • Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
      – corsiKa
      35 mins ago


















    • Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
      – King_Cordelia
      3 hours ago










    • Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
      – corsiKa
      35 mins ago
















    Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
    – King_Cordelia
    3 hours ago




    Awesome, thanks.The point about some operations of floating-point arithmetic being invalid is a great one.
    – King_Cordelia
    3 hours ago












    Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
    – corsiKa
    35 mins ago




    Negative infinity is obviously bigger than positive infinity making the result negative. After all, any 2's compliment stored number has one more negative value than positive value. It's science.
    – corsiKa
    35 mins ago













    8














    I believe this has do to with digest using base::serialize, which gives non-identical results for the 2 objects:



    identical(
    base::serialize(Inf-Inf, connection = NULL, ascii = FALSE),
    base::serialize(NaN, connection = NULL, ascii = FALSE)
    )
    # [1] FALSE


    Even though



    identical(Inf-Inf, NaN)
    # [1] TRUE





    share|improve this answer


























      8














      I believe this has do to with digest using base::serialize, which gives non-identical results for the 2 objects:



      identical(
      base::serialize(Inf-Inf, connection = NULL, ascii = FALSE),
      base::serialize(NaN, connection = NULL, ascii = FALSE)
      )
      # [1] FALSE


      Even though



      identical(Inf-Inf, NaN)
      # [1] TRUE





      share|improve this answer
























        8












        8








        8






        I believe this has do to with digest using base::serialize, which gives non-identical results for the 2 objects:



        identical(
        base::serialize(Inf-Inf, connection = NULL, ascii = FALSE),
        base::serialize(NaN, connection = NULL, ascii = FALSE)
        )
        # [1] FALSE


        Even though



        identical(Inf-Inf, NaN)
        # [1] TRUE





        share|improve this answer












        I believe this has do to with digest using base::serialize, which gives non-identical results for the 2 objects:



        identical(
        base::serialize(Inf-Inf, connection = NULL, ascii = FALSE),
        base::serialize(NaN, connection = NULL, ascii = FALSE)
        )
        # [1] FALSE


        Even though



        identical(Inf-Inf, NaN)
        # [1] TRUE






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 3 hours ago









        JozefJozef

        821210




        821210






























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