Efficient Round edition with different rounding direction












1












$begingroup$


As pointed out in this post, Mathematica has a special version of Round that




Round rounds numbers of the form x.5 toward the nearest even integer.




A comment by David G suggest that why not have differnt options Direction → {"HalfDown","HalfUp","HalfEven","HalfOdd","Stochastic"}



These days I need a version of Round to HalfUp. I write a quite ugly and slow function as below



myRound[x_, d_] := Module[{},
c1 = (1./d)*10;
c2 = 1./d;
theDigit = Last@IntegerDigits@IntegerPart[x*c1];
If[theDigit >= 5,
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2] + 1)/c2],
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2])/c2]]]


speed test



In[267]:= 
myRound[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[267]= {30.7072, Null}

In[268]:=
Round[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[268]= {0.285921, Null}


So I am wondering if someone on this site already have developed an efficient toolkit for round matters?










share|improve this question











$endgroup$












  • $begingroup$
    Floor[x+0.5]?
    $endgroup$
    – Szabolcs
    Mar 17 at 11:58










  • $begingroup$
    @Szabolcs But Floor gives integer. Round can round at any digit
    $endgroup$
    – matheorem
    Mar 17 at 12:02






  • 2




    $begingroup$
    Are your aware of RoundingRule?
    $endgroup$
    – Silvia
    Mar 17 at 12:25






  • 1




    $begingroup$
    Could extend the method proposed by @Szabolcs: myRound[x_, d_] := d*Floor[x/d + 1/2]
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 13:58






  • 1




    $begingroup$
    That's a result of using decimal values e.g. .01 that do not have exact binary equivalents. Could instead do myRound[8.121,1/100] and numericize afterward.
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 18:36
















1












$begingroup$


As pointed out in this post, Mathematica has a special version of Round that




Round rounds numbers of the form x.5 toward the nearest even integer.




A comment by David G suggest that why not have differnt options Direction → {"HalfDown","HalfUp","HalfEven","HalfOdd","Stochastic"}



These days I need a version of Round to HalfUp. I write a quite ugly and slow function as below



myRound[x_, d_] := Module[{},
c1 = (1./d)*10;
c2 = 1./d;
theDigit = Last@IntegerDigits@IntegerPart[x*c1];
If[theDigit >= 5,
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2] + 1)/c2],
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2])/c2]]]


speed test



In[267]:= 
myRound[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[267]= {30.7072, Null}

In[268]:=
Round[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[268]= {0.285921, Null}


So I am wondering if someone on this site already have developed an efficient toolkit for round matters?










share|improve this question











$endgroup$












  • $begingroup$
    Floor[x+0.5]?
    $endgroup$
    – Szabolcs
    Mar 17 at 11:58










  • $begingroup$
    @Szabolcs But Floor gives integer. Round can round at any digit
    $endgroup$
    – matheorem
    Mar 17 at 12:02






  • 2




    $begingroup$
    Are your aware of RoundingRule?
    $endgroup$
    – Silvia
    Mar 17 at 12:25






  • 1




    $begingroup$
    Could extend the method proposed by @Szabolcs: myRound[x_, d_] := d*Floor[x/d + 1/2]
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 13:58






  • 1




    $begingroup$
    That's a result of using decimal values e.g. .01 that do not have exact binary equivalents. Could instead do myRound[8.121,1/100] and numericize afterward.
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 18:36














1












1








1





$begingroup$


As pointed out in this post, Mathematica has a special version of Round that




Round rounds numbers of the form x.5 toward the nearest even integer.




A comment by David G suggest that why not have differnt options Direction → {"HalfDown","HalfUp","HalfEven","HalfOdd","Stochastic"}



These days I need a version of Round to HalfUp. I write a quite ugly and slow function as below



myRound[x_, d_] := Module[{},
c1 = (1./d)*10;
c2 = 1./d;
theDigit = Last@IntegerDigits@IntegerPart[x*c1];
If[theDigit >= 5,
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2] + 1)/c2],
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2])/c2]]]


speed test



In[267]:= 
myRound[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[267]= {30.7072, Null}

In[268]:=
Round[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[268]= {0.285921, Null}


So I am wondering if someone on this site already have developed an efficient toolkit for round matters?










share|improve this question











$endgroup$




As pointed out in this post, Mathematica has a special version of Round that




Round rounds numbers of the form x.5 toward the nearest even integer.




A comment by David G suggest that why not have differnt options Direction → {"HalfDown","HalfUp","HalfEven","HalfOdd","Stochastic"}



These days I need a version of Round to HalfUp. I write a quite ugly and slow function as below



myRound[x_, d_] := Module[{},
c1 = (1./d)*10;
c2 = 1./d;
theDigit = Last@IntegerDigits@IntegerPart[x*c1];
If[theDigit >= 5,
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2] + 1)/c2],
Internal`StringToDouble@ToString@N[(IntegerPart[x*c2])/c2]]]


speed test



In[267]:= 
myRound[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[267]= {30.7072, Null}

In[268]:=
Round[#, 0.01] & /@ RandomReal[1., 1000000]; // AbsoluteTiming

Out[268]= {0.285921, Null}


So I am wondering if someone on this site already have developed an efficient toolkit for round matters?







machine-precision precision-and-accuracy round






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Mar 17 at 11:59







matheorem

















asked Mar 17 at 11:54









matheoremmatheorem

6,65743178




6,65743178












  • $begingroup$
    Floor[x+0.5]?
    $endgroup$
    – Szabolcs
    Mar 17 at 11:58










  • $begingroup$
    @Szabolcs But Floor gives integer. Round can round at any digit
    $endgroup$
    – matheorem
    Mar 17 at 12:02






  • 2




    $begingroup$
    Are your aware of RoundingRule?
    $endgroup$
    – Silvia
    Mar 17 at 12:25






  • 1




    $begingroup$
    Could extend the method proposed by @Szabolcs: myRound[x_, d_] := d*Floor[x/d + 1/2]
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 13:58






  • 1




    $begingroup$
    That's a result of using decimal values e.g. .01 that do not have exact binary equivalents. Could instead do myRound[8.121,1/100] and numericize afterward.
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 18:36


















  • $begingroup$
    Floor[x+0.5]?
    $endgroup$
    – Szabolcs
    Mar 17 at 11:58










  • $begingroup$
    @Szabolcs But Floor gives integer. Round can round at any digit
    $endgroup$
    – matheorem
    Mar 17 at 12:02






  • 2




    $begingroup$
    Are your aware of RoundingRule?
    $endgroup$
    – Silvia
    Mar 17 at 12:25






  • 1




    $begingroup$
    Could extend the method proposed by @Szabolcs: myRound[x_, d_] := d*Floor[x/d + 1/2]
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 13:58






  • 1




    $begingroup$
    That's a result of using decimal values e.g. .01 that do not have exact binary equivalents. Could instead do myRound[8.121,1/100] and numericize afterward.
    $endgroup$
    – Daniel Lichtblau
    Mar 17 at 18:36
















$begingroup$
Floor[x+0.5]?
$endgroup$
– Szabolcs
Mar 17 at 11:58




$begingroup$
Floor[x+0.5]?
$endgroup$
– Szabolcs
Mar 17 at 11:58












$begingroup$
@Szabolcs But Floor gives integer. Round can round at any digit
$endgroup$
– matheorem
Mar 17 at 12:02




$begingroup$
@Szabolcs But Floor gives integer. Round can round at any digit
$endgroup$
– matheorem
Mar 17 at 12:02




2




2




$begingroup$
Are your aware of RoundingRule?
$endgroup$
– Silvia
Mar 17 at 12:25




$begingroup$
Are your aware of RoundingRule?
$endgroup$
– Silvia
Mar 17 at 12:25




1




1




$begingroup$
Could extend the method proposed by @Szabolcs: myRound[x_, d_] := d*Floor[x/d + 1/2]
$endgroup$
– Daniel Lichtblau
Mar 17 at 13:58




$begingroup$
Could extend the method proposed by @Szabolcs: myRound[x_, d_] := d*Floor[x/d + 1/2]
$endgroup$
– Daniel Lichtblau
Mar 17 at 13:58




1




1




$begingroup$
That's a result of using decimal values e.g. .01 that do not have exact binary equivalents. Could instead do myRound[8.121,1/100] and numericize afterward.
$endgroup$
– Daniel Lichtblau
Mar 17 at 18:36




$begingroup$
That's a result of using decimal values e.g. .01 that do not have exact binary equivalents. Could instead do myRound[8.121,1/100] and numericize afterward.
$endgroup$
– Daniel Lichtblau
Mar 17 at 18:36










1 Answer
1






active

oldest

votes


















3












$begingroup$

I offer the following solution



r2[x_, a_] := x - Mod[x, a, -(a/2)]


We can verify that it has the desired result, using PiecewiseExpand



PiecewiseExpand[r2[x, a], -2 a < x < 2 a && a > 0]


Performance is only a little slower than the built-in Round



list = RandomReal[{0, 1}, 1000000];

AbsoluteTiming[Round[list, 0.1];]
(* {0.0079, Null} *)

AbsoluteTiming[r2[list, 0.1];]
(* {0.009414, Null} *)





share|improve this answer









$endgroup$













  • $begingroup$
    Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
    $endgroup$
    – matheorem
    Mar 17 at 13:22






  • 4




    $begingroup$
    @matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
    $endgroup$
    – Michael E2
    Mar 17 at 15:14






  • 2




    $begingroup$
    @matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
    $endgroup$
    – Michael E2
    Mar 17 at 15:40






  • 2




    $begingroup$
    @matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
    $endgroup$
    – mikado
    Mar 17 at 18:38








  • 1




    $begingroup$
    @matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
    $endgroup$
    – Michael E2
    Mar 18 at 2:52











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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









3












$begingroup$

I offer the following solution



r2[x_, a_] := x - Mod[x, a, -(a/2)]


We can verify that it has the desired result, using PiecewiseExpand



PiecewiseExpand[r2[x, a], -2 a < x < 2 a && a > 0]


Performance is only a little slower than the built-in Round



list = RandomReal[{0, 1}, 1000000];

AbsoluteTiming[Round[list, 0.1];]
(* {0.0079, Null} *)

AbsoluteTiming[r2[list, 0.1];]
(* {0.009414, Null} *)





share|improve this answer









$endgroup$













  • $begingroup$
    Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
    $endgroup$
    – matheorem
    Mar 17 at 13:22






  • 4




    $begingroup$
    @matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
    $endgroup$
    – Michael E2
    Mar 17 at 15:14






  • 2




    $begingroup$
    @matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
    $endgroup$
    – Michael E2
    Mar 17 at 15:40






  • 2




    $begingroup$
    @matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
    $endgroup$
    – mikado
    Mar 17 at 18:38








  • 1




    $begingroup$
    @matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
    $endgroup$
    – Michael E2
    Mar 18 at 2:52
















3












$begingroup$

I offer the following solution



r2[x_, a_] := x - Mod[x, a, -(a/2)]


We can verify that it has the desired result, using PiecewiseExpand



PiecewiseExpand[r2[x, a], -2 a < x < 2 a && a > 0]


Performance is only a little slower than the built-in Round



list = RandomReal[{0, 1}, 1000000];

AbsoluteTiming[Round[list, 0.1];]
(* {0.0079, Null} *)

AbsoluteTiming[r2[list, 0.1];]
(* {0.009414, Null} *)





share|improve this answer









$endgroup$













  • $begingroup$
    Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
    $endgroup$
    – matheorem
    Mar 17 at 13:22






  • 4




    $begingroup$
    @matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
    $endgroup$
    – Michael E2
    Mar 17 at 15:14






  • 2




    $begingroup$
    @matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
    $endgroup$
    – Michael E2
    Mar 17 at 15:40






  • 2




    $begingroup$
    @matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
    $endgroup$
    – mikado
    Mar 17 at 18:38








  • 1




    $begingroup$
    @matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
    $endgroup$
    – Michael E2
    Mar 18 at 2:52














3












3








3





$begingroup$

I offer the following solution



r2[x_, a_] := x - Mod[x, a, -(a/2)]


We can verify that it has the desired result, using PiecewiseExpand



PiecewiseExpand[r2[x, a], -2 a < x < 2 a && a > 0]


Performance is only a little slower than the built-in Round



list = RandomReal[{0, 1}, 1000000];

AbsoluteTiming[Round[list, 0.1];]
(* {0.0079, Null} *)

AbsoluteTiming[r2[list, 0.1];]
(* {0.009414, Null} *)





share|improve this answer









$endgroup$



I offer the following solution



r2[x_, a_] := x - Mod[x, a, -(a/2)]


We can verify that it has the desired result, using PiecewiseExpand



PiecewiseExpand[r2[x, a], -2 a < x < 2 a && a > 0]


Performance is only a little slower than the built-in Round



list = RandomReal[{0, 1}, 1000000];

AbsoluteTiming[Round[list, 0.1];]
(* {0.0079, Null} *)

AbsoluteTiming[r2[list, 0.1];]
(* {0.009414, Null} *)






share|improve this answer












share|improve this answer



share|improve this answer










answered Mar 17 at 12:33









mikadomikado

6,7471929




6,7471929












  • $begingroup$
    Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
    $endgroup$
    – matheorem
    Mar 17 at 13:22






  • 4




    $begingroup$
    @matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
    $endgroup$
    – Michael E2
    Mar 17 at 15:14






  • 2




    $begingroup$
    @matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
    $endgroup$
    – Michael E2
    Mar 17 at 15:40






  • 2




    $begingroup$
    @matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
    $endgroup$
    – mikado
    Mar 17 at 18:38








  • 1




    $begingroup$
    @matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
    $endgroup$
    – Michael E2
    Mar 18 at 2:52


















  • $begingroup$
    Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
    $endgroup$
    – matheorem
    Mar 17 at 13:22






  • 4




    $begingroup$
    @matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
    $endgroup$
    – Michael E2
    Mar 17 at 15:14






  • 2




    $begingroup$
    @matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
    $endgroup$
    – Michael E2
    Mar 17 at 15:40






  • 2




    $begingroup$
    @matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
    $endgroup$
    – mikado
    Mar 17 at 18:38








  • 1




    $begingroup$
    @matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
    $endgroup$
    – Michael E2
    Mar 18 at 2:52
















$begingroup$
Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
$endgroup$
– matheorem
Mar 17 at 13:22




$begingroup$
Wow, great solution. But there is a issue with hidden fractions as pointed out by mathematica.stackexchange.com/q/65298/4742 . But if we use InternalStringToDouble@ToString`, the performance will drop down.
$endgroup$
– matheorem
Mar 17 at 13:22




4




4




$begingroup$
@matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
$endgroup$
– Michael E2
Mar 17 at 15:14




$begingroup$
@matheorem Technically, 1.265 in binary floating point corresponds to the fraction 5697053528623677/4503599627370496 which less than 1265/1000. The issue is due to rounding decimal to binary. Thus it is a problem of inputting the number you meant, not a problem with r2.
$endgroup$
– Michael E2
Mar 17 at 15:14




2




2




$begingroup$
@matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
$endgroup$
– Michael E2
Mar 17 at 15:40




$begingroup$
@matheorem The problem with imprecise calculation is that unwanted errors creep in, such as what you're complaining about. :) How about this?: r2[x_, a_] := x (1 + Sign[x] $MachineEpsilon) - Mod[x (1 + Sign[x] $MachineEpsilon), a, -(a/2)]
$endgroup$
– Michael E2
Mar 17 at 15:40




2




2




$begingroup$
@matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
$endgroup$
– mikado
Mar 17 at 18:38






$begingroup$
@matheorem, I think you need to consider why you know the input is exactly 1.265 rather than a little more or a little less, and why the rounding is important to you. If you know the 3rd decimal place is exact, I suggest you multiply all your numbers by 1000, and work with integers.
$endgroup$
– mikado
Mar 17 at 18:38






1




1




$begingroup$
@matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
$endgroup$
– Michael E2
Mar 18 at 2:52




$begingroup$
@matheorem That's what happens (automatically) when you use real (floating-point) numbers on all common CPUs.
$endgroup$
– Michael E2
Mar 18 at 2:52


















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